diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 000000000..b1fd9845b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,27 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: Bug +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 000000000..e74cb57fa --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: Feature +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/core_tests.yml b/.github/workflows/core_tests.yml index 6215c66da..0cd77dff4 100644 --- a/.github/workflows/core_tests.yml +++ b/.github/workflows/core_tests.yml @@ -180,7 +180,7 @@ jobs: - prototype_mtc_extended - placeholder_sandag - prototype_sandag_xborder - - prototype_semcog + - production_semcog - prototype_mwcog - placeholder_multiple_zone fail-fast: false diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md index fdfc60719..237d7e414 100644 --- a/HOW_TO_RELEASE.md +++ b/HOW_TO_RELEASE.md @@ -42,15 +42,16 @@ 00. Run black to ensure that the codebase passes all style checks. This check should only take a few seconds. These checks are also done on - Travis and are platform independent, so they should not be necessary to + GitHub Actions and are platform independent, so they should not be necessary to replicate locally, but are listed here for completeness. ```sh black --check --diff . ``` -00. Run the regular test suite on Windows. Travis tests are done on Linux, - but most users are on Windows, and the test suite should also be run - on Windows to ensure that it works on that platform as well. If you +00. Run the regular test suite on Windows. Most GitHub Actions tests are done on Linux, + Linux (it's faster to start up and run a new clean VM for testing) but most + users are on Windows, and the test suite should also be run on Windows to + ensure that it works on that platform as well. If you are not preparing this release on Windows, you should be sure to run at least through this step on a Windows machine before finalizing a release. @@ -135,6 +136,10 @@ ```sh gh release create v1.2.3 ``` + The process of creating and tagging a release will automatically + trigger various GitHub Actions scripts to build, test, and publish the + new release to PyPI and conda forge, assuming there are no errors. + For a development pre-release, include the `--prerelease` argument. As the project's policy is that only formally released code is merged to the main branch, any pre-release should also be built against a diff --git a/activitysim/abm/models/__init__.py b/activitysim/abm/models/__init__.py index 91b802d76..1c288f8c4 100644 --- a/activitysim/abm/models/__init__.py +++ b/activitysim/abm/models/__init__.py @@ -20,6 +20,7 @@ joint_tour_frequency, joint_tour_participation, joint_tour_scheduling, + joint_tour_frequency_composition, location_choice, mandatory_scheduling, mandatory_tour_frequency, diff --git a/activitysim/abm/models/cdap.py b/activitysim/abm/models/cdap.py index b37cf8a9a..f7da93687 100644 --- a/activitysim/abm/models/cdap.py +++ b/activitysim/abm/models/cdap.py @@ -78,6 +78,17 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id): file_name=model_settings["FIXED_RELATIVE_PROPORTIONS_SPEC"] ) + add_joint_tour_utility = model_settings.get("ADD_JOINT_TOUR_UTILITY", False) + + if add_joint_tour_utility: + # Rules and coefficients for generating cdap joint tour specs for different household sizes + joint_tour_coefficients_file_name = model_settings.get( + "JOINT_TOUR_COEFFICIENTS", "cdap_joint_tour_coefficients.csv" + ) + cdap_joint_tour_coefficients = pd.read_csv( + config.config_file_path(joint_tour_coefficients_file_name), comment="#" + ) + persons_merged = persons_merged.to_frame() # add tour-based chunk_id so we can chunk all trips in tour together @@ -101,11 +112,27 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id): # (also when multiprocessing locutor might not see all household sizes) logger.info("Pre-building cdap specs") for hhsize in range(2, cdap.MAX_HHSIZE + 1): - spec = cdap.build_cdap_spec(cdap_interaction_coefficients, hhsize, cache=True) + spec = cdap.build_cdap_spec( + cdap_interaction_coefficients, + hhsize, + cache=True, + joint_tour_alt=add_joint_tour_utility, + ) if inject.get_injectable("locutor", False): spec.to_csv( config.output_file_path("cdap_spec_%s.csv" % hhsize), index=True ) + if add_joint_tour_utility: + # build cdap joint tour spec + # joint_spec_dependency = spec.loc[[c for c in spec.index if c.startswith(('M_p', 'N_p', 'H_p'))]] + joint_spec = cdap.build_cdap_joint_spec( + cdap_joint_tour_coefficients, hhsize, cache=True + ) + if inject.get_injectable("locutor", False): + joint_spec.to_csv( + config.output_file_path("cdap_joint_spec_%s.csv" % hhsize), + index=True, + ) if estimator: estimator.write_model_settings(model_settings, "cdap.yaml") @@ -127,17 +154,32 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id): logger.info("Running cdap_simulate with %d persons", len(persons_merged.index)) - choices = cdap.run_cdap( - persons=persons_merged, - person_type_map=person_type_map, - cdap_indiv_spec=cdap_indiv_spec, - cdap_interaction_coefficients=cdap_interaction_coefficients, - cdap_fixed_relative_proportions=cdap_fixed_relative_proportions, - locals_d=constants, - chunk_size=chunk_size, - trace_hh_id=trace_hh_id, - trace_label=trace_label, - ) + if add_joint_tour_utility: + choices, hh_joint = cdap.run_cdap( + persons=persons_merged, + person_type_map=person_type_map, + cdap_indiv_spec=cdap_indiv_spec, + cdap_interaction_coefficients=cdap_interaction_coefficients, + cdap_fixed_relative_proportions=cdap_fixed_relative_proportions, + locals_d=constants, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_label=trace_label, + add_joint_tour_utility=add_joint_tour_utility, + ) + else: + choices = cdap.run_cdap( + persons=persons_merged, + person_type_map=person_type_map, + cdap_indiv_spec=cdap_indiv_spec, + cdap_interaction_coefficients=cdap_interaction_coefficients, + cdap_fixed_relative_proportions=cdap_fixed_relative_proportions, + locals_d=constants, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_label=trace_label, + add_joint_tour_utility=add_joint_tour_utility, + ) if estimator: estimator.write_choices(choices) @@ -161,6 +203,11 @@ def cdap_simulate(persons_merged, persons, households, chunk_size, trace_hh_id): # - annotate households table households = households.to_frame() + + if add_joint_tour_utility: + hh_joint = hh_joint.reindex(households.index) + households["has_joint_tour"] = hh_joint + expressions.assign_columns( df=households, model_settings=model_settings.get("annotate_households"), diff --git a/activitysim/abm/models/joint_tour_frequency_composition.py b/activitysim/abm/models/joint_tour_frequency_composition.py new file mode 100644 index 000000000..3e52d27fa --- /dev/null +++ b/activitysim/abm/models/joint_tour_frequency_composition.py @@ -0,0 +1,204 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np +import pandas as pd +import os +from activitysim.core.interaction_simulate import interaction_simulate + +from activitysim.core import simulate +from activitysim.core import tracing +from activitysim.core import pipeline +from activitysim.core import config +from activitysim.core import inject +from activitysim.core import expressions + +from .util import estimation + +from .util.overlap import hh_time_window_overlap +from .util.tour_frequency import process_joint_tours_frequency_composition + +logger = logging.getLogger(__name__) + + +@inject.step() +def joint_tour_frequency_composition( + households_merged, persons, chunk_size, trace_hh_id +): + """ + This model predicts the frequency and composition of fully joint tours. + """ + + trace_label = "joint_tour_frequency_composition" + model_settings_file_name = "joint_tour_frequency_composition.yaml" + + model_settings = config.read_model_settings(model_settings_file_name) + + alt_tdd = simulate.read_model_alts( + "joint_tour_frequency_composition_alternatives.csv", set_index="alt" + ) + + # - only interested in households with more than one cdap travel_active person and + # - at least one non-preschooler + households_merged = households_merged.to_frame() + choosers = households_merged[households_merged.participates_in_jtf_model].copy() + + # - only interested in persons in choosers households + persons = persons.to_frame() + persons = persons[persons.household_id.isin(choosers.index)] + + logger.info("Running %s with %d households", trace_label, len(choosers)) + + # alt preprocessor + alt_preprocessor_settings = model_settings.get("ALTS_PREPROCESSOR", None) + if alt_preprocessor_settings: + + locals_dict = {} + + alt_tdd = alt_tdd.copy() + + expressions.assign_columns( + df=alt_tdd, + model_settings=alt_preprocessor_settings, + locals_dict=locals_dict, + trace_label=trace_label, + ) + + # - preprocessor + preprocessor_settings = model_settings.get("preprocessor", None) + if preprocessor_settings: + + locals_dict = { + "persons": persons, + "hh_time_window_overlap": hh_time_window_overlap, + } + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=trace_label, + ) + + estimator = estimation.manager.begin_estimation("joint_tour_frequency_composition") + + model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) + coefficients_df = simulate.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + constants = config.get_model_constants(model_settings) + + if estimator: + estimator.write_spec(model_settings) + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + estimator.write_alternatives(alts) + + assert choosers.index.name == "household_id" + assert "household_id" not in choosers.columns + choosers["household_id"] = choosers.index + + estimator.set_chooser_id(choosers.index.name) + + # The choice value 'joint_tour_frequency_composition' assigned by interaction_simulate + # is the index value of the chosen alternative in the alternatives table. + choices = interaction_simulate( + choosers=choosers, + alternatives=alt_tdd, + spec=model_spec, + locals_d=constants, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name=trace_label, + estimator=estimator, + ) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values( + choices, "households", "joint_tour_frequency_composition" + ) + estimator.write_override_choices(choices) + estimator.end_estimation() + + # add joint tour frequency composition column to household table + households_merged["joint_tour_frequency_composition"] = choices.reindex( + households_merged.index + ).fillna(0) + + # - create joint_tours based on choices + + # - we need a person_id in order to generate the tour index (and for register_traceable_table) + # - but we don't know the tour participants yet + # - so we arbitrarily choose the first person in the household + # - to be point person for the purpose of generating an index and setting origin + temp_point_persons = persons.loc[persons.PNUM == 1] + temp_point_persons["person_id"] = temp_point_persons.index + temp_point_persons = temp_point_persons.set_index("household_id") + temp_point_persons = temp_point_persons[["person_id", "home_zone_id"]] + + # create a tours table of tour_category "joint" and different tour_types (e.g. shopping, eat) + # and add the composition column (adults or children or mixed) to the tour + + # Choices + # hhid choice + # 11111 1 + # 22222 2 + # 33333 3 + + # Alts + # alt purpose1 purpose2 party1 party2 eat shop + # 1 5 0 3 0 1 0 + # 2 5 6 1 3 1 1 + # 3 6 0 1 0 0 1 + + # Joint Tours + # hhid type category composition + # 11111 eat joint mixed + # 22222 eat joint adults + # 22222 shop joint mixed + # 33333 shop joint adults + + joint_tours = process_joint_tours_frequency_composition( + choices, alt_tdd, temp_point_persons + ) + + tours = pipeline.extend_table("tours", joint_tours) + + tracing.register_traceable_table("tours", joint_tours) + pipeline.get_rn_generator().add_channel("tours", joint_tours) + + # we expect there to be an alt with no tours - which we can use to backfill non-travelers + no_tours_alt = 0 + households_merged["joint_tour_frequency_composition"] = ( + choices.reindex(households_merged.index).fillna(no_tours_alt).astype(str) + ) + + households_merged["num_hh_joint_tours"] = ( + joint_tours.groupby("household_id") + .size() + .reindex(households_merged.index) + .fillna(0) + .astype(np.int8) + ) + + pipeline.replace_table("households", households_merged) + + tracing.print_summary( + "joint_tour_frequency_composition", + households_merged.joint_tour_frequency_composition, + value_counts=True, + ) + + if trace_hh_id: + tracing.trace_df( + households_merged, label="joint_tour_frequency_composition.households" + ) + + tracing.trace_df( + joint_tours, + label="joint_tour_frequency_composition.joint_tours", + slicer="household_id", + ) diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py index 939d9fedd..ee8658ae5 100644 --- a/activitysim/abm/models/joint_tour_participation.py +++ b/activitysim/abm/models/joint_tour_participation.py @@ -3,6 +3,7 @@ import logging import pandas as pd +import numpy as np from activitysim.abm.models.util.canonical_ids import MAX_PARTICIPANT_PNUM from activitysim.core import ( @@ -57,7 +58,7 @@ def joint_tour_participation_candidates(joint_tours, persons_merged): # - stable (predictable) index # if this happens, participant_id may not be unique - # channel random seeds will overlap at MAX_PARTICIPANT_PNUM (not probably a big deal) + # channel random seeds will overlap at MAX_PARTICIPANT_PNUM (probably not a big deal) # and estimation infer will fail assert ( candidates.PNUM.max() < MAX_PARTICIPANT_PNUM @@ -65,6 +66,7 @@ def joint_tour_participation_candidates(joint_tours, persons_merged): candidates["participant_id"] = ( candidates[joint_tours.index.name] * MAX_PARTICIPANT_PNUM ) + candidates.PNUM + candidates["participant_id"] = candidates["participant_id"].astype(np.uint64) candidates.set_index( "participant_id", drop=True, inplace=True, verify_integrity=True ) @@ -198,7 +200,19 @@ def participants_chooser(probs, choosers, spec, trace_label): transpose=False, ) print(unsatisfied_candidates.head(20)) - assert False + + if model_settings.get("FORCE_PARTICIPATION", False): + logger.warning( + f"Forcing joint tour participation for {num_tours_remaining} tours." + ) + # anybody with probability > 0 is forced to join the joint tour + probs[choice_col] = np.where(probs[choice_col] > 0, 1, 0) + non_choice_col = [col for col in probs.columns if col != choice_col][0] + probs[non_choice_col] = 1 - probs[choice_col] + else: + raise RuntimeError( + f"{num_tours_remaining} tours could not be satisfied after {iter} iterations" + ) choices, rands = logit.make_choices( probs, trace_label=trace_label, trace_choosers=choosers @@ -209,6 +223,13 @@ def participants_chooser(probs, choosers, spec, trace_label): tour_satisfaction = get_tour_satisfaction(candidates, participate) num_tours_satisfied_this_iter = tour_satisfaction.sum() + if (iter > MAX_ITERATIONS) & ( + num_tours_remaining != num_tours_satisfied_this_iter + ): + logger.error( + f"Still do not satisfy participation for {num_tours_remaining - num_tours_satisfied_this_iter} tours." + ) + if num_tours_satisfied_this_iter > 0: num_tours_remaining -= num_tours_satisfied_this_iter diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py index 1a7564942..dd5e279b3 100644 --- a/activitysim/abm/models/location_choice.py +++ b/activitysim/abm/models/location_choice.py @@ -426,6 +426,8 @@ def run_location_sample( ) dest_size_terms = dest_size_terms[dest_size_terms.size_term > 0] + assert len(dest_size_terms) > 0, "No destination with available size terms" + # by default, enable presampling for multizone systems, unless they disable it in settings file pre_sample_taz = not (network_los.zone_system == los.ONE_ZONE) if pre_sample_taz and not config.setting("want_dest_choice_presampling", True): diff --git a/activitysim/abm/models/non_mandatory_tour_frequency.py b/activitysim/abm/models/non_mandatory_tour_frequency.py index 93b36b2f4..521f49c47 100644 --- a/activitysim/abm/models/non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/non_mandatory_tour_frequency.py @@ -271,7 +271,7 @@ def non_mandatory_tour_frequency(persons, persons_merged, chunk_size, trace_hh_i no_tours_alt = (alternatives.sum(axis=1) == 0).index[0] # need to reindex as we only handled persons with cdap_activity in ['M', 'N'] persons["non_mandatory_tour_frequency"] = ( - choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int8) + choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int16) ) """ diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py index 3b10f32b4..a87703b8b 100644 --- a/activitysim/abm/models/parking_location_choice.py +++ b/activitysim/abm/models/parking_location_choice.py @@ -232,7 +232,6 @@ def run_parking_destination( alt_column_filter_name = model_settings.get("ALTERNATIVE_FILTER_COLUMN_NAME") alternatives = land_use[land_use[alt_column_filter_name]] - alternatives.index.name = parking_location_column_name choices_list = [] diff --git a/activitysim/abm/models/trip_mode_choice.py b/activitysim/abm/models/trip_mode_choice.py index 54586a448..e7bb200e4 100644 --- a/activitysim/abm/models/trip_mode_choice.py +++ b/activitysim/abm/models/trip_mode_choice.py @@ -164,8 +164,10 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): model_spec = simulate.read_model_spec(file_name=model_settings["SPEC"]) nest_spec = config.get_logit_model_settings(model_settings) + cols_to_keep = model_settings.get("CHOOSER_COLS_TO_KEEP", None) choices_list = [] + cols_to_keep_list = [] for primary_purpose, trips_segment in trips_merged.groupby("primary_purpose"): segment_trace_label = tracing.extend_trace_label(trace_label, primary_purpose) @@ -250,6 +252,14 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): ) choices_list.append(choices) + if cols_to_keep: + cols_not_in_choosers = [ + col for col in cols_to_keep if col not in trips_segment.columns + ] + assert ( + len(cols_not_in_choosers) == 0 + ), "{cols_not_in_choosers} from CHOOSER_COLS_TO_KEEP is not in the choosers dataframe" + cols_to_keep_list.append(trips_segment[cols_to_keep]) choices_df = pd.concat(choices_list) @@ -279,6 +289,12 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): estimator.write_override_choices(choices_df.trip_mode) estimator.end_estimation() trips_df = trips.to_frame() + + # adding columns from the chooser table to include in final output + if len(cols_to_keep_list) > 0: + cols_to_keep_df = pd.concat(cols_to_keep_list) + choices_df = pd.concat([choices_df, cols_to_keep_df], axis=1) + assign_in_place(trips_df, choices_df) if pipeline.is_table("school_escort_tours") & model_settings.get( @@ -301,7 +317,7 @@ def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id): pipeline.replace_table("trips", trips_df) if model_settings.get("annotate_trips"): - annotate.annotate_trips(model_settings, trace_label) + annotate.annotate_trips(model_settings, trace_label, locals_dict) if trace_hh_id: tracing.trace_df( diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py index f8345650d..54c3eb201 100644 --- a/activitysim/abm/models/trip_scheduling.py +++ b/activitysim/abm/models/trip_scheduling.py @@ -1,6 +1,7 @@ # ActivitySim # See full license in LICENSE.txt. import logging +import warnings from builtins import range import numpy as np @@ -8,11 +9,11 @@ from activitysim.abm.models.util import estimation from activitysim.abm.models.util.trip import cleanup_failed_trips, failed_trip_cohorts -from activitysim.core import chunk, config, inject, logit, pipeline, tracing +from activitysim.core import chunk, config, expressions, inject, pipeline, tracing from activitysim.core.util import reindex -from .util.school_escort_tours_trips import split_out_school_escorting_trips from .util import probabilistic_scheduling as ps +from .util.school_escort_tours_trips import split_out_school_escorting_trips logger = logging.getLogger(__name__) @@ -36,6 +37,7 @@ DEPARTURE_MODE = "departure" DURATION_MODE = "stop_duration" +RELATIVE_MODE = "relative" PROBS_JOIN_COLUMNS_DEPARTURE_BASED = [ "primary_purpose", "outbound", @@ -43,6 +45,23 @@ "trip_num", ] PROBS_JOIN_COLUMNS_DURATION_BASED = ["outbound", "stop_num"] +PROBS_JOIN_COLUMNS_RELATIVE_BASED = ["outbound", "periods_left"] + + +def _logic_version(model_settings): + logic_version = model_settings.get("logic_version", None) + if logic_version is None: + warnings.warn( + "The trip_scheduling component now has a logic_version setting " + "to control how the scheduling rules are applied. The default " + "logic_version is currently set at `1` but may be moved up in " + "the future. Explicitly set `logic_version` to 2 in the model " + "settings to upgrade your model logic now, or set it to 1 to " + "suppress this message.", + FutureWarning, + ) + logic_version = 1 + return logic_version def set_tour_hour(trips, tours): @@ -106,7 +125,7 @@ def set_stop_num(trips): trips["stop_num"] = trips.stop_num.where(trips["outbound"], trips["trip_num"]) -def update_tour_earliest(trips, outbound_choices): +def update_tour_earliest(trips, outbound_choices, logic_version: int): """ Updates "earliest" column for inbound trips based on the maximum outbound trip departure time of the tour. @@ -119,6 +138,14 @@ def update_tour_earliest(trips, outbound_choices): outbound_choices: pd.Series time periods depart choices, one per trip (except for trips with zero probs) + logic_version : int + Logic version 1 is the original ActivitySim implementation, which + sets the "earliest" value to the max outbound departure for all + inbound trips, regardless of what that max outbound departure value + is (even if it is NA). Logic version 2 introduces a change whereby + that assignment is only made if the max outbound departure value is + not NA. + Returns ------- modifies trips in place @@ -142,9 +169,19 @@ def update_tour_earliest(trips, outbound_choices): # set the trips "earliest" column equal to the max outbound departure # time for all inbound trips. preserve values that were used for outbound trips - tmp_trips["earliest"] = tmp_trips["earliest"].where( - tmp_trips["outbound"], tmp_trips["max_outbound_departure"] - ) + # FIXME - extra logic added because max_outbound_departure can be NA if previous failed trip was removed + if logic_version == 1: + tmp_trips["earliest"] = tmp_trips["earliest"].where( + tmp_trips["outbound"], tmp_trips["max_outbound_departure"] + ) + elif logic_version > 1: + tmp_trips["earliest"] = np.where( + ~tmp_trips["outbound"] & ~tmp_trips["max_outbound_departure"].isna(), + tmp_trips["max_outbound_departure"], + tmp_trips["earliest"], + ) + else: + raise ValueError(f"bad logic_version: {logic_version}") trips["earliest"] = tmp_trips["earliest"].reindex(trips.index) @@ -181,6 +218,7 @@ def schedule_trips_in_leg( failfix = model_settings.get(FAILFIX, FAILFIX_DEFAULT) depart_alt_base = model_settings.get("DEPART_ALT_BASE", 0) scheduling_mode = model_settings.get("scheduling_mode", "departure") + preprocessor_settings = model_settings.get("preprocessor", None) if scheduling_mode == "departure": probs_join_cols = model_settings.get( @@ -190,10 +228,14 @@ def schedule_trips_in_leg( probs_join_cols = model_settings.get( "probs_join_cols", PROBS_JOIN_COLUMNS_DURATION_BASED ) + elif scheduling_mode == "relative": + probs_join_cols = model_settings.get( + "probs_join_cols", PROBS_JOIN_COLUMNS_RELATIVE_BASED + ) else: logger.error( "Invalid scheduling mode specified: {0}.".format(scheduling_mode), - "Please select one of ['departure', 'stop_duration'] and try again.", + "Please select one of ['departure', 'stop_duration', 'relative'] and try again.", ) # logger.debug("%s scheduling %s trips" % (trace_label, trips.shape[0])) @@ -232,18 +274,34 @@ def schedule_trips_in_leg( ADJUST_NEXT_DEPART_COL = "latest" trips.next_trip_id = trips.next_trip_id.where(~is_final, NO_TRIP_ID) + network_los = inject.get_injectable("network_los") + locals_dict = {"network_los": network_los} + locals_dict.update(config.get_model_constants(model_settings)) + first_trip_in_leg = True for i in range(trips.trip_num.min(), trips.trip_num.max() + 1): + nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i) - if outbound or scheduling_mode == DURATION_MODE: + # - annotate trips + if preprocessor_settings: + expressions.assign_columns( + df=trips, + model_settings=preprocessor_settings, + locals_dict=locals_dict, + trace_label=nth_trace_label, + ) + + if ( + outbound + or (scheduling_mode == DURATION_MODE) + or (scheduling_mode == RELATIVE_MODE) + ): # iterate in ascending trip_num order nth_trips = trips[trips.trip_num == i] else: # iterate over inbound trips in descending trip_num order, skipping the final trip nth_trips = trips[trips.trip_num == trips.trip_count - i] - nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i) - choices = ps.make_scheduling_choices( nth_trips, scheduling_mode, @@ -265,6 +323,17 @@ def schedule_trips_in_leg( ) choices = choices.fillna(trips[ADJUST_NEXT_DEPART_COL]) + if scheduling_mode == RELATIVE_MODE: + # choices are relative to the previous departure time + choices = nth_trips.earliest + choices + # need to update the departure time based on the choice + logic_version = _logic_version(model_settings) + if logic_version == 1: + raise ValueError( + "cannot use logic version 1 with 'relative' scheduling mode" + ) + update_tour_earliest(trips, choices, logic_version) + # adjust allowed depart range of next trip has_next_trip = nth_trips.next_trip_id != NO_TRIP_ID if has_next_trip.any(): @@ -299,7 +368,6 @@ def run_trip_scheduling( trace_hh_id, trace_label, ): - set_tour_hour(trips_chunk, tours) set_stop_num(trips_chunk) @@ -328,7 +396,7 @@ def run_trip_scheduling( # departure time of last outbound trips must constrain # departure times for initial inbound trips - update_tour_earliest(trips_chunk, choices) + update_tour_earliest(trips_chunk, choices, _logic_version(model_settings)) if (~trips_chunk.outbound).any(): leg_chunk = trips_chunk[~trips_chunk.outbound] @@ -353,7 +421,6 @@ def run_trip_scheduling( @inject.step() def trip_scheduling(trips, tours, chunk_size, trace_hh_id): - """ Trip scheduling assigns depart times for trips within the start, end limits of the tour. @@ -437,9 +504,9 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): ] estimator.write_choosers(trips_df[chooser_cols_for_estimation]) - probs_spec = pd.read_csv( - config.config_file_path("trip_scheduling_probs.csv"), comment="#" - ) + probs_spec_file = model_settings.get("PROBS_SPEC", "trip_scheduling_probs.csv") + logger.debug(f"probs_spec_file: {config.config_file_path(probs_spec_file)}") + probs_spec = pd.read_csv(config.config_file_path(probs_spec_file), comment="#") # FIXME for now, not really doing estimation for probabilistic model - just overwriting choices # besides, it isn't clear that named coefficients would be helpful if we had some form of estimation # coefficients_df = simulate.read_model_coefficients(model_settings) @@ -465,13 +532,10 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): ) in chunk.adaptive_chunked_choosers_by_chunk_id( trips_df, chunk_size, trace_label, trace_label ): - i = 0 while (i < max_iterations) and not trips_chunk.empty: - # only chunk log first iteration since memory use declines with each iteration with chunk.chunk_log(trace_label) if i == 0 else chunk.chunk_log_skip(): - i += 1 is_last_iteration = i == max_iterations @@ -499,6 +563,11 @@ def trip_scheduling(trips, tours, chunk_size, trace_hh_id): failed = choices.reindex(trips_chunk.index).isnull() logger.info("%s %s failed", trace_label_i, failed.sum()) + if (failed.sum() > 0) & ( + model_settings.get("scheduling_mode") == "relative" + ): + raise RuntimeError("failed trips with relative scheduling mode") + if not is_last_iteration: # boolean series of trips whose leg scheduling failed failed_cohorts = failed_trip_cohorts(trips_chunk, failed) diff --git a/activitysim/abm/models/util/annotate.py b/activitysim/abm/models/util/annotate.py index a7ef4f54c..e50519b38 100644 --- a/activitysim/abm/models/util/annotate.py +++ b/activitysim/abm/models/util/annotate.py @@ -15,7 +15,7 @@ logger = logging.getLogger(__name__) -def annotate_tours(model_settings, trace_label): +def annotate_tours(model_settings, trace_label, locals_dict={}): """ Add columns to the tours table in the pipeline according to spec. @@ -28,12 +28,13 @@ def annotate_tours(model_settings, trace_label): expressions.assign_columns( df=tours, model_settings=model_settings.get("annotate_tours"), + locals_dict=locals_dict, trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), ) pipeline.replace_table("tours", tours) -def annotate_trips(model_settings, trace_label): +def annotate_trips(model_settings, trace_label, locals_dict={}): """ Add columns to the trips table in the pipeline according to spec. @@ -46,6 +47,7 @@ def annotate_trips(model_settings, trace_label): expressions.assign_columns( df=trips, model_settings=model_settings.get("annotate_trips"), + locals_dict=locals_dict, trace_label=tracing.extend_trace_label(trace_label, "annotate_trips"), ) pipeline.replace_table("trips", trips) diff --git a/activitysim/abm/models/util/cdap.py b/activitysim/abm/models/util/cdap.py index 7e0f55186..b71d5128a 100644 --- a/activitysim/abm/models/util/cdap.py +++ b/activitysim/abm/models/util/cdap.py @@ -195,6 +195,12 @@ def individual_utilities( useful_columns = [_hh_id_, _ptype_, "cdap_rank", _hh_size_] indiv_utils[useful_columns] = persons[useful_columns] + # add attributes for joint tour utility + model_settings = config.read_model_settings("cdap.yaml") + additional_useful_columns = model_settings.get("JOINT_TOUR_USEFUL_COLUMNS", None) + if additional_useful_columns is not None: + indiv_utils[additional_useful_columns] = persons[additional_useful_columns] + if trace_hh_id: tracing.trace_df( indiv_utils, @@ -259,6 +265,10 @@ def cached_spec_name(hhsize): return "cdap_spec_%s" % hhsize +def cached_joint_spec_name(hhsize): + return "cdap_joint_spec_%s" % hhsize + + def get_cached_spec(hhsize): spec_name = cached_spec_name(hhsize) @@ -281,14 +291,39 @@ def get_cached_spec(hhsize): return None +def get_cached_joint_spec(hhsize): + + spec_name = cached_joint_spec_name(hhsize) + + spec = inject.get_injectable(spec_name, None) + if spec is not None: + logger.debug( + "build_cdap_joint_spec returning cached injectable spec %s", spec_name + ) + return spec + + return None + + def cache_spec(hhsize, spec): spec_name = cached_spec_name(hhsize) # cache as injectable inject.add_injectable(spec_name, spec) +def cache_joint_spec(hhsize, spec): + spec_name = cached_joint_spec_name(hhsize) + # cache as injectable + inject.add_injectable(spec_name, spec) + + def build_cdap_spec( - interaction_coefficients, hhsize, trace_spec=False, trace_label=None, cache=True + interaction_coefficients, + hhsize, + trace_spec=False, + trace_label=None, + cache=True, + joint_tour_alt=False, ): """ Build a spec file for computing utilities of alternative household member interaction patterns @@ -355,6 +390,14 @@ def build_cdap_spec( # e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN'] for hhsize=2 alternatives = ["".join(tup) for tup in itertools.product("HMN", repeat=hhsize)] + if joint_tour_alt: + joint_alternatives = [ + "".join(tup) + "J" + for tup in itertools.product("HMN", repeat=hhsize) + if tup.count("M") + tup.count("N") >= 2 + ] + alternatives = alternatives + joint_alternatives + # spec df has expression column plus a column for each alternative spec = pd.DataFrame(columns=[expression_name] + alternatives) @@ -482,6 +525,207 @@ def build_cdap_spec( return spec +def build_cdap_joint_spec( + joint_tour_coefficients, hhsize, trace_spec=False, trace_label=None, cache=True +): + """ + Build a spec file for computing joint tour utilities of alternative household member for households of specified size. + We generate this spec automatically from a table of rules and coefficients because the + interaction rules are fairly simple and can be expressed compactly whereas + there is a lot of redundancy between the spec files for different household sizes, as well as + in the vectorized expression of the interaction alternatives within the spec file itself + joint_tour_coefficients has five columns: + label + label of the expression + description + description of the expression + dependency + if the expression is dependent on alternative, and which alternative is it dependent on + (e.g. M_px, N_px, H_px) + expression + expression of the utility term + coefficient + The coefficient to apply for the alternative + The generated spec will have the eval expression in the index, and a utility column for each + alternative (e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN', 'MMJ', 'MNJ', 'NMJ', 'NNJ'] for hhsize 2 with joint alts) + Parameters + ---------- + joint_tour_coefficients : pandas.DataFrame + Rules and coefficients for generating joint tour specs for different household sizes + hhsize : int + household size for which the spec should be built. + Returns + ------- + spec: pandas.DataFrame + """ + + t0 = tracing.print_elapsed_time() + + # cdap joint spec is same for all households of MAX_HHSIZE and greater + hhsize = min(hhsize, MAX_HHSIZE) + + if cache: + spec = get_cached_joint_spec(hhsize) + if spec is not None: + return spec + + expression_name = "Expression" + + # generate a list of activity pattern alternatives for this hhsize + # e.g. ['HH', 'HM', 'HN', 'MH', 'MM', 'MN', 'NH', 'NM', 'NN'] for hhsize=2 + alternatives = ["".join(tup) for tup in itertools.product("HMN", repeat=hhsize)] + + joint_alternatives = [ + "".join(tup) + "J" + for tup in itertools.product("HMN", repeat=hhsize) + if tup.count("M") + tup.count("N") >= 2 + ] + alternatives = alternatives + joint_alternatives + + # spec df has expression column plus a column for each alternative + spec = pd.DataFrame(columns=[expression_name] + alternatives) + + # Before processing the interaction_coefficients, we add add rows to the spec to carry + # the alternative utilities previously computed for each individual into all hh alternative + # columns in which the individual assigned that alternative. The Expression column contains + # the name of the choosers column with that individuals utility for the individual alternative + # and the hh alternative columns that should receive that utility are given a value of 1 + # e.g. M_p1 is a column in choosers with the individual utility to person p1 of alternative M + # Expression MM MN MH NM NN NH HM HN HH + # M_p1 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 + # N_p1 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 + for pnum in range(1, hhsize + 1): + for activity in ["M", "N", "H"]: + + new_row_index = len(spec) + spec.loc[new_row_index, expression_name] = add_pn(activity, pnum) + + # list of alternative columns where person pnum has expression activity + # e.g. for M_p1 we want the columns where activity M is in position p1 + alternative_columns = [ + alt for alt in alternatives if alt[pnum - 1] == activity + ] + spec.loc[new_row_index, alternative_columns] = 1 + + # for each row in the joint util table + for row in joint_tour_coefficients.itertuples(): + + # if there is no dependencies + if row.dependency is np.nan: + expression = row.Expression + # add a new row to spec + new_row_index = len(spec) + spec.loc[new_row_index, expression_name] = expression + spec.loc[new_row_index, alternatives] = row.coefficient + # if there is dependencies + else: + dependency_name = row.dependency + expression = row.Expression + coefficient = row.coefficient + if dependency_name in ["M_px", "N_px", "H_px"]: + if "_pxprod" in expression: + prod_conds = row.Expression.split("|") + expanded_expressions = [ + tup + for tup in itertools.product( + range(len(prod_conds)), repeat=hhsize + ) + ] + for expression_tup in expanded_expressions: + expression_list = [] + dependency_list = [] + for counter in range(len(expression_tup)): + expression_list.append( + prod_conds[expression_tup[counter]].replace( + "xprod", str(counter + 1) + ) + ) + if expression_tup[counter] == 0: + dependency_list.append( + dependency_name.replace("x", str(counter + 1)) + ) + + expression_value = "&".join(expression_list) + dependency_value = pd.Series( + np.ones(len(alternatives)), index=alternatives + ) + if len(dependency_list) > 0: + for dependency in dependency_list: + # temp = spec.loc[spec[expression_name]==dependency, alternatives].squeeze().fillna(0) + dependency_value *= ( + spec.loc[ + spec[expression_name] == dependency, + alternatives, + ] + .squeeze() + .fillna(0) + ) + + # add a new row to spec + new_row_index = len(spec) + spec.loc[new_row_index] = dependency_value + spec.loc[new_row_index, expression_name] = expression_value + spec.loc[new_row_index, alternatives] = ( + spec.loc[new_row_index, alternatives] * coefficient + ) + + elif "_px" in expression: + for pnum in range(1, hhsize + 1): + dependency_name = row.dependency.replace("x", str(pnum)) + expression = row.Expression.replace("x", str(pnum)) + + # add a new row to spec + new_row_index = len(spec) + spec.loc[new_row_index] = spec.loc[ + spec[expression_name] == dependency_name + ].squeeze() + spec.loc[new_row_index, expression_name] = expression + spec.loc[new_row_index, alternatives] = ( + spec.loc[new_row_index, alternatives] * coefficient + ) + + # drop dependency rows + spec = spec[~spec[expression_name].str.startswith(("M_p", "N_p", "H_p"))] + + # eval expression goes in the index + spec.set_index(expression_name, inplace=True) + + for c in spec.columns: + spec[c] = spec[c].fillna(0) + + simulate.uniquify_spec_index(spec) + + # make non-joint alts 0 + for c in alternatives: + if c.endswith("J"): + continue + else: + spec[c] = 0 + + if trace_spec: + tracing.trace_df( + spec, + "%s.hhsize%d_joint_spec" % (trace_label, hhsize), + transpose=False, + slicer="NONE", + ) + + if trace_spec: + tracing.trace_df( + spec, + "%s.hhsize%d_joint_spec_patched" % (trace_label, hhsize), + transpose=False, + slicer="NONE", + ) + + if cache: + cache_joint_spec(hhsize, spec) + + t0 = tracing.print_elapsed_time("build_cdap_joint_spec hh_size %s" % hhsize, t0) + + return spec + + def add_interaction_column(choosers, p_tup): """ Add an interaction column in place to choosers, listing the ptypes of the persons in p_tup @@ -578,6 +822,12 @@ def hh_choosers(indiv_utils, hhsize): # we want to merge the ptype and M, N, and H utilities for each individual in the household merge_cols = [_hh_id_, _ptype_, "M", "N", "H"] + # add attributes for joint tour utility + model_settings = config.read_model_settings("cdap.yaml") + additional_merge_cols = model_settings.get("JOINT_TOUR_USEFUL_COLUMNS", None) + if additional_merge_cols is not None: + merge_cols.extend(additional_merge_cols) + if hhsize > MAX_HHSIZE: raise RuntimeError("hh_choosers hhsize > MAX_HHSIZE") @@ -620,11 +870,19 @@ def hh_choosers(indiv_utils, hhsize): for tup in itertools.combinations(list(range(1, hhsize + 1)), i): add_interaction_column(choosers, tup) + # add hhsize + choosers["hhsize"] = hhsize + return choosers def household_activity_choices( - indiv_utils, interaction_coefficients, hhsize, trace_hh_id=None, trace_label=None + indiv_utils, + interaction_coefficients, + hhsize, + trace_hh_id=None, + trace_label=None, + add_joint_tour_utility=False, ): """ Calculate household utilities for each activity pattern alternative for households of hhsize @@ -669,6 +927,7 @@ def household_activity_choices( hhsize, trace_spec=(trace_hh_id in choosers.index), trace_label=trace_label, + joint_tour_alt=add_joint_tour_utility, ) utils = simulate.eval_utilities(spec, choosers, trace_label=trace_label) @@ -676,6 +935,23 @@ def household_activity_choices( if len(utils.index) == 0: return pd.Series(dtype="float64") + # calculate joint tour utility + if add_joint_tour_utility & (hhsize > 1): + # calculate joint utils + joint_tour_spec = build_cdap_joint_spec( + interaction_coefficients, + hhsize, + trace_spec=(trace_hh_id in choosers.index), + trace_label=trace_label, + ) + + joint_tour_utils = simulate.eval_utilities( + joint_tour_spec, choosers, trace_label=trace_label + ) + + # add joint util to util + utils = utils.add(joint_tour_utils) + probs = logit.utils_to_probs(utils, trace_label=trace_label) # select an activity pattern alternative for each household based on probability @@ -867,6 +1143,7 @@ def _run_cdap( locals_d, trace_hh_id, trace_label, + add_joint_tour_utility, ): """ Implements core run_cdap functionality on persons df (or chunked subset thereof) @@ -909,6 +1186,7 @@ def _run_cdap( hhsize=hhsize, trace_hh_id=trace_hh_id, trace_label=trace_label, + add_joint_tour_utility=add_joint_tour_utility, ) hh_choices_list.append(choices) @@ -940,6 +1218,13 @@ def _run_cdap( persons["cdap_activity"] = person_choices chunk.log_df(trace_label, "persons", persons) + # return household joint tour flag + if add_joint_tour_utility: + hh_activity_choices = hh_activity_choices.to_frame(name="hh_choices") + hh_activity_choices["has_joint_tour"] = hh_activity_choices["hh_choices"].apply( + lambda x: 1 if "J" in x else 0 + ) + # if DUMP: # tracing.trace_df(hh_activity_choices, '%s.DUMP.hh_activity_choices' % trace_label, # transpose=False, slicer='NONE') @@ -951,7 +1236,10 @@ def _run_cdap( del persons chunk.log_df(trace_label, "persons", None) - return result + if add_joint_tour_utility: + return result, hh_activity_choices["has_joint_tour"] + else: + return result def run_cdap( @@ -964,6 +1252,7 @@ def run_cdap( chunk_size=0, trace_hh_id=None, trace_label=None, + add_joint_tour_utility=False, ): """ Choose individual activity patterns for persons. @@ -990,6 +1279,8 @@ def run_cdap( hh_id to trace or None if no hh tracing trace_label : str label for tracing or None if no tracing + add_joint_tour_utility : Bool + cdap model include joint tour utility or not Returns ------- @@ -1011,16 +1302,30 @@ def run_cdap( chunk_trace_label, ) in chunk.adaptive_chunked_choosers_by_chunk_id(persons, chunk_size, trace_label): - cdap_results = _run_cdap( - persons_chunk, - person_type_map, - cdap_indiv_spec, - cdap_interaction_coefficients, - cdap_fixed_relative_proportions, - locals_d, - trace_hh_id, - chunk_trace_label, - ) + if add_joint_tour_utility: + cdap_results, hh_choice_results = _run_cdap( + persons_chunk, + person_type_map, + cdap_indiv_spec, + cdap_interaction_coefficients, + cdap_fixed_relative_proportions, + locals_d, + trace_hh_id, + chunk_trace_label, + add_joint_tour_utility, + ) + else: + cdap_results = _run_cdap( + persons_chunk, + person_type_map, + cdap_indiv_spec, + cdap_interaction_coefficients, + cdap_fixed_relative_proportions, + locals_d, + trace_hh_id, + chunk_trace_label, + add_joint_tour_utility, + ) result_list.append(cdap_results) @@ -1042,4 +1347,8 @@ def run_cdap( ) # return choices column as series - return cdap_results["cdap_activity"] + if add_joint_tour_utility: + return cdap_results["cdap_activity"], hh_choice_results + else: + # return choices column as series + return cdap_results["cdap_activity"] diff --git a/activitysim/abm/models/util/probabilistic_scheduling.py b/activitysim/abm/models/util/probabilistic_scheduling.py index aab2e8d70..6fc7689a6 100644 --- a/activitysim/abm/models/util/probabilistic_scheduling.py +++ b/activitysim/abm/models/util/probabilistic_scheduling.py @@ -172,10 +172,22 @@ def _preprocess_scheduling_probs( ) elif scheduling_mode == "stop_duration": chooser_probs = _preprocess_stop_duration_probs(choosers) + elif scheduling_mode == "relative": + # creating a dataframe with just the trip_id as index and alternatives as columns + probs_cols = [ + c + for c in probs_spec.columns + if ((c not in probs_join_cols) & (c.isnumeric())) + ] + chooser_probs = choosers.loc[:, probs_cols] + chooser_probs = chooser_probs.div(chooser_probs.sum(axis=1), axis=0) + assert ( + ~chooser_probs.isna().values.any() + ), f"Missing probabilities for trips \n {chooser_probs[chooser_probs.isna().any(axis=1)].index}" else: logger.error( "Invalid scheduling mode specified: {0}.".format(scheduling_mode), - "Please select one of ['departure', 'stop_duration'] and try again.", + "Please select one of ['departure', 'stop_duration', 'relative'] and try again.", ) # probs should sum to 1 with residual probs resulting in choice of 'fail' @@ -196,6 +208,12 @@ def _postprocess_scheduling_choices( # convert alt choice index to depart time (setting failed choices to -1) failed = choices == choice_cols.get_loc("fail") + if scheduling_mode == "relative": + if failed.any(): + RuntimeError( + f"Failed trips in realtive mode for {failed.sum()} trips: {choosers[failed]}" + ) + # For the stop duration-based probabilities, the alternatives are offsets that # get applied to trip-specific departure and arrival times, so depart_alt_base # is a column/series rather than a scalar. @@ -334,7 +352,11 @@ def make_scheduling_choices( if failed.any(): choices = choices[~failed] - if all([check_col in choosers_df.columns for check_col in ["earliest", "latest"]]): + if all( + [check_col in choosers_df.columns for check_col in ["earliest", "latest"]] + ) & (scheduling_mode != "relative"): + # check to make sure choice does not come before previously scheduled trip or after end of tour + # does not apply if choices are relative to previous trip depart assert (choices >= choosers_df.earliest[~failed]).all() assert (choices <= choosers_df.latest[~failed]).all() diff --git a/activitysim/abm/models/util/tour_frequency.py b/activitysim/abm/models/util/tour_frequency.py index 37108b564..a73482fe5 100644 --- a/activitysim/abm/models/util/tour_frequency.py +++ b/activitysim/abm/models/util/tour_frequency.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd +from activitysim.core import config from activitysim.abm.models.util.canonical_ids import set_tour_index from activitysim.core.util import reindex @@ -459,3 +460,283 @@ def process_joint_tours(joint_tour_frequency, joint_tour_frequency_alts, point_p 17978574 joint 4 5143198 """ return tours + + +def process_joint_tours_frequency_composition( + joint_tour_frequency_composition, + joint_tour_frequency_composition_alts, + point_persons, +): + """ + This method processes the joint_tour_frequency_composition column that comes out of + the model of the same name and turns into a DataFrame that represents the + joint tours that were generated + + Parameters + ---------- + joint_tour_frequency_composition : pandas.Series + household joint_tour_frequency_composition (which came out of the joint tour frequency composition model) + indexed by household_id + joint_tour_frequency_composition_alts: DataFrame + A DataFrame which has as a unique index with joint_tour_frequency_composition values + and frequency counts for the tours to be generated for that choice + point_persons : pandas DataFrame + table with columns for (at least) person_ids and home_zone_id indexed by household_id + + Returns + ------- + tours : DataFrame + An example of a tours DataFrame is supplied as a comment in the + source code - it has an index which is a tour identifier, a household_id + column, a tour_type column, composition column and tour_type_num and tour_num columns + which is set to 1 or 2 depending whether it is the first or second joint tour + made by the household. + """ + + assert not joint_tour_frequency_composition.isnull().any() + + tours = process_tours_frequency_composition( + joint_tour_frequency_composition.dropna(), + joint_tour_frequency_composition_alts, + tour_category="joint", + parent_col="household_id", + ) + + assert not tours.index.duplicated().any() + assert point_persons.index.name == "household_id" + + # - assign a temp point person to tour so we can create stable index + tours["person_id"] = reindex(point_persons.person_id, tours.household_id) + tours["origin"] = reindex(point_persons.home_zone_id, tours.household_id) + + # assign stable (predictable) tour_id + set_tour_index(tours, is_joint=True) + + """ + household_id tour_type tour_type_count tour_type_num tour_num tour_count + tour_id + 3209530 320953 disc 1 1 1 2 + 3209531 320953 disc 2 2 2 2 + 23267026 2326702 shop 1 1 1 1 + 17978574 1797857 main 1 1 1 1 + + tour_category tour_category_id person_id + 3209530 joint 4 577234 + 3209531 joint 4 577234 + 23267026 joint 4 1742708 + 17978574 joint 4 5143198 + """ + return tours + + +def process_tours_frequency_composition( + joint_tour_frequency_composition, + joint_tour_frequency_composition_alts, + tour_category, + parent_col="person_id", +): + """ + This method processes the joint_tour_frequency_composition column that comes + out of the model of the same name and turns into a DataFrame that + represents the tours that were generated + + Parameters + ---------- + joint_tour_frequency_composition: Series + A series which has as the index and the chosen alternative + index as the value + joint_tour_frequency_composition_alts: DataFrame + A DataFrame which has as a unique index which relates to the values + in the series above typically includes columns which are named for trip + purposes with values which are counts for that trip purpose, and tour composition. Example + trip purposes include escort, shopping, othmaint, othdiscr, eatout, + social, etc. Tour composition includes adults, children, and mixed. + A row would be an alternative which might be to take + one shopping trip in a adult-only tour and zero trips of other purposes, etc. + tour_category : str + one of 'mandatory', 'non_mandatory', 'atwork', or 'joint' + parent_col: str + the name of the index (parent_tour_id for atwork subtours, otherwise person_id) + + Returns + ------- + tours : pandas.DataFrame + An example of a tours DataFrame is supplied as a comment in the + source code - it has an index which is a unique tour identifier, + a person_id column, and a tour type column which comes from the + column names of the alternatives DataFrame supplied above. + + tours.tour_type - tour type (e.g. school, work, shopping, eat) + tours.composition - tour composition (e.g. adults, children, mixed) + tours.tour_type_num - if there are two 'school' type tours, they will be numbered 1 and 2 + tours.tour_type_count - number of tours of tour_type parent has (parent's max tour_type_num) + tours.tour_num - index of tour (of any type) for parent + tours.tour_count - number of tours of any type) for parent (parent's max tour_num) + tours.tour_category - one of 'mandatory', 'non_mandatory', 'atwork', or 'joint' + """ + + # get the actual alternatives for each person - have to go back to the + # non_mandatory_tour_frequency_alts dataframe to get this - the choice + # above just stored the index values for the chosen alts + tour_counts = joint_tour_frequency_composition_alts.loc[ + joint_tour_frequency_composition + ] + + # assign person ids to the index + tour_counts.index = joint_tour_frequency_composition.index + + """ + alt1 alt2 alt3 + + 2588676 2 0 0 + 2588677 1 1 0 + """ + + tours = create_joint_tours(tour_counts, tour_category, parent_col) + + return tours + + +def create_joint_tours(tour_counts, tour_category, parent_col="person_id"): + """ + This method processes the tour_frequency column that comes + out of the model of the same name and turns into a DataFrame that + represents the tours that were generated + + Parameters + ---------- + tour_counts: DataFrame + table specifying how many tours of each type to create + one row per person (or parent_tour for atwork subtours) + one (int) column per tour_type, with number of tours to create + tour_category : str + one of 'mandatory', 'non_mandatory', 'atwork', or 'joint' + + Returns + ------- + tours : pandas.DataFrame + An example of a tours DataFrame is supplied as a comment in the + source code - it has an index which is a unique tour identifier, + a person_id column, and a tour type column which comes from the + column names of the alternatives DataFrame supplied above. + + tours.tour_type - tour type (e.g. school, work, shopping, eat) + tours.composition - tour composition (e.g. adults, children, mixed) + tours.tour_type_num - if there are two 'school' type tours, they will be numbered 1 and 2 + tours.tour_type_count - number of tours of tour_type parent has (parent's max tour_type_num) + tours.tour_num - index of tour (of any type) for parent + tours.tour_count - number of tours of any type) for parent (parent's max tour_num) + tours.tour_category - one of 'mandatory', 'non_mandatory', 'atwork', or 'joint' + """ + + # FIXME - document requirement to ensure adjacent tour_type_nums in tour_num order + + """ + alt1 alt2 alt3 + + 2588676 2 0 0 + 2588677 1 1 0 + """ + model_settings_file_name = "joint_tour_frequency_composition.yaml" + + model_settings = config.read_model_settings(model_settings_file_name) + + alts_table_structure = model_settings.get("ALTS_TABLE_STRUCTURE", None) + assert ( + alts_table_structure is not None + ), f"Expected to find ALTS_TABLE_STRUCTURE setting in joint_tour_frequency_composition.yaml" + + tour_type_dict = alts_table_structure.get("PURPOSE", None).get("VALUE_MAP", None) + assert ( + tour_type_dict is not None + ), f"Expected to find PURPOSE.VALUE_MAP setting in ALTS_TABLE_STRUCTURE" + + tour_type_cols = alts_table_structure.get("PURPOSE", None).get("COLUMNS", None) + assert ( + tour_type_cols is not None + ), f"Expected to find PURPOSE.COLUMNS setting in ALTS_TABLE_STRUCTURE" + + tour_comp_dict = alts_table_structure.get("COMPOSITION", None).get( + "VALUE_MAP", None + ) + assert ( + tour_comp_dict is not None + ), f"Expected to find COMPOSITION.VALUE_MAP setting in ALTS_TABLE_STRUCTURE" + + tour_comp_cols = alts_table_structure.get("COMPOSITION", None).get("COLUMNS", None) + assert ( + tour_comp_cols is not None + ), f"Expected to find COMPOSITION.COLUMNS setting in ALTS_TABLE_STRUCTURE" + + # reformat with the columns given below + tours_purp = tour_counts[tour_type_cols].stack().reset_index() + tours_purp.columns = [parent_col, "tour_id_temp", "tour_type"] + tours_purp["tour_id_temp"] = range(1, 1 + len(tours_purp)) + tours_purp["tour_type"] = tours_purp["tour_type"].map(tour_type_dict) + + """ + tour_id_temp tour_type + 0 2588676 purpose1 5 + 1 2588676 purpose2 0 + 2 2588677 purpose1 5 + 3 2588677 purpose2 5 + 4 2588678 purpose1 6 + 5 2588678 purpose2 7 + + parent_col is the index from non_mandatory_tour_frequency + tour_type is the column name from non_mandatory_tour_frequency_alts + tour_type_count is the count value of the tour's chosen alt's tour_type from alts table + """ + tours_comp = tour_counts[tour_comp_cols].stack().reset_index() + tours_comp.columns = [parent_col, "tour_id_temp", "composition"] + tours_comp["tour_id_temp"] = range(1, 1 + len(tours_comp)) + tours_comp["composition"] = tours_comp["composition"].map(tour_comp_dict) + + """ + tour_id_temp tour_composition + 0 2588676 party1 1 + 1 2588676 party2 0 + 2 2588677 party1 1 + 3 2588677 party2 1 + 4 2588678 party1 1 + 5 2588678 party2 2 + + parent_col is the index from non_mandatory_tour_frequency + tour_type is the column name from non_mandatory_tour_frequency_alts + tour_type_count is the count value of the tour's chosen alt's tour_type from alts table + """ + tours = pd.merge( + tours_purp, tours_comp, how="left", on=[parent_col, "tour_id_temp"] + ) + + tours = tours[(tours.tour_type.notnull()) & (tours.tour_type.notnull())] + + grouped = tours.groupby([parent_col, "tour_type"]) + tours["tour_type_num"] = grouped.cumcount() + 1 + tours["tour_type_count"] = tours["tour_type_num"] + grouped.cumcount( + ascending=False + ) + + grouped = tours.groupby(parent_col) + tours["tour_num"] = grouped.cumcount() + 1 + tours["tour_count"] = tours["tour_num"] + grouped.cumcount(ascending=False) + + """ + tour_type tour_type_num tour_type_count tour_num tour_count + 0 2588676 alt1 1 2 1 4 + 0 2588676 alt1 2 2 2 4 + 0 2588676 alt2 1 1 3 4 + 0 2588676 alt3 1 1 4 4 + """ + + # set these here to ensure consistency across different tour categories + assert tour_category in ["mandatory", "non_mandatory", "atwork", "joint"] + tours["tour_category"] = tour_category + + # for joint tours, the correct number will be filled in after participation step + tours["number_of_participants"] = 1 + + # index is arbitrary but don't want any duplicates in index + tours.reset_index(drop=True, inplace=True) + + return tours diff --git a/activitysim/abm/models/vehicle_type_choice.py b/activitysim/abm/models/vehicle_type_choice.py index 95723eb9d..324e505dd 100644 --- a/activitysim/abm/models/vehicle_type_choice.py +++ b/activitysim/abm/models/vehicle_type_choice.py @@ -124,7 +124,7 @@ def annotate_vehicle_type_choice_persons(model_settings, trace_label): model_settings=model_settings.get("annotate_persons"), trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), ) - pipeline.replace_table("persons", households) + pipeline.replace_table("persons", persons) def annotate_vehicle_type_choice_vehicles(model_settings, trace_label): @@ -214,9 +214,14 @@ def construct_model_alternatives(model_settings, alts_cats_dict, vehicle_type_da alts_wide._merge == "left_only", ["body_type", "fuel_type", "age"] ] - assert ( - len(missing_alts) == 0 - ), f"missing vehicle data for alternatives:\n {missing_alts}" + if model_settings.get("REQUIRE_DATA_FOR_ALL_ALTS", False): + # fail if alternative does not have an associated record in the data + assert ( + len(missing_alts) == 0 + ), f"missing vehicle data for alternatives:\n {missing_alts}" + else: + # eliminate alternatives if no vehicle type data + alts_wide = alts_wide[alts_wide._merge != "left_only"] alts_wide.drop(columns="_merge", inplace=True) # converting age to integer to allow interactions in utilities diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py index 3a3ba7f56..396cf7c56 100644 --- a/activitysim/abm/tables/shadow_pricing.py +++ b/activitysim/abm/tables/shadow_pricing.py @@ -61,6 +61,12 @@ "workplace": "income_segment", } +default_segment_to_name_dict = { + # model_selector : persons_segment_name + "school": "school_segment", + "workplace": "income_segment", +} + def size_table_name(model_selector): """ diff --git a/activitysim/cli/create.py b/activitysim/cli/create.py index ec545d378..7cd8d218c 100644 --- a/activitysim/cli/create.py +++ b/activitysim/cli/create.py @@ -123,7 +123,7 @@ def get_example( Files downloaded via http pointers will be cached in this location. If a path is not given but just a truthy value, then a cache directory is created using in a location - selected by the appdirs library (or, if not installed, + selected by the platformdirs library (or, if not installed, linking is skipped.) """ if example_name not in EXAMPLES: @@ -200,11 +200,11 @@ def download_asset(url, target_path, sha256=None, link=True): if link: if not isinstance(link, (str, Path)): try: - import appdirs + import platformdirs except ImportError: link = False else: - link = appdirs.user_data_dir("ActivitySim") + link = platformdirs.user_data_dir("ActivitySim") original_target_path = target_path target_path = os.path.join(link, target_path) os.makedirs(os.path.dirname(target_path), exist_ok=True) diff --git a/activitysim/core/config.py b/activitysim/core/config.py index 7024d0512..aaa7ed2fb 100644 --- a/activitysim/core/config.py +++ b/activitysim/core/config.py @@ -750,6 +750,14 @@ def filter_warnings(): else: warnings.filterwarnings("default", category=CacheMissWarning) + # beginning from PR #660 (after 1.2.0), a FutureWarning is emitted when the trip + # scheduling component lacks a logic_version setting + warnings.filterwarnings( + "ignore", + category=FutureWarning, + message="The trip_scheduling component now has a logic_version setting.*", + ) + def handle_standard_args(parser=None): diff --git a/activitysim/core/configuration.py b/activitysim/core/configuration.py new file mode 100644 index 000000000..3174653b2 --- /dev/null +++ b/activitysim/core/configuration.py @@ -0,0 +1,291 @@ +from typing import Union + +try: + from pydantic import BaseModel as PydanticBase +except ModuleNotFoundError: + + class PydanticBase: + pass + + +class InputTable(PydanticBase): + """ + The features that define an input table to be read by ActivitySim. + """ + + tablename: str + """Name of the injected table""" + + filename: str = None + """ + Name of the CSV or HDF5 file to read. + + If not provided, defaults to `input_store` + """ + + index_col: str = None + """table column to use for the index""" + + rename_columns: dict[str, str] = None + """dictionary of column name mappings""" + + keep_columns: list[str] = None + """ + columns to keep once read in to memory. + + Save only the columns needed for modeling or analysis to save on memory + and file I/O + """ + + h5_tablename: str = None + """table name if reading from HDF5 and different from `tablename`""" + + +class Settings(PydanticBase): + """ + The overall settings for the ActivitySim model system. + + The input for these settings is typically stored in one main YAML file, + usually called ``settings.yaml``. + + Note that this implementation is presently used only for generating + documentation, but future work may migrate the settings implementation to + actually use this pydantic code to validate the settings before running + the model. + """ + + models: list[str] + """ + list of model steps to run - auto ownership, tour frequency, etc. + + See :ref:`model_steps` for more details about each step. + """ + + resume_after: str = None + """to resume running the data pipeline after the last successful checkpoint""" + + input_table_list: list[InputTable] + """list of table names, indices, and column re-maps for each table in `input_store`""" + + input_store: str = None + """HDF5 inputs file""" + + create_input_store: bool = False + """ + Write the inputs as read in back to an HDF5 store. + + If enabled, this writes the store to the outputs folder to use for subsequent + model runs, as reading HDF5 can be faster than reading CSV files.""" + + households_sample_size: int = None + """ + Number of households to sample and simulate + + If omitted or set to 0, ActivitySim will simulate all households. + """ + trace_hh_id: Union[int, list] = None + """ + Trace household id(s) + + If omitted, no tracing is written out + """ + + trace_od: list[int] = None + """ + Trace origin, destination pair in accessibility calculation + + If omitted, no tracing is written out. + """ + + chunk_training_mode: str = None + """ + The method to use for chunk training. + + Valid values include {disabled, training, production, adaptive}. + See :ref:`chunk_size` for more details. + """ + + chunk_size: int = None + """ + Approximate amount of RAM to allocate to ActivitySim for batch processing. + + See :ref:`chunk_size` for more details. + """ + + chunk_method: str = None + """ + Memory use measure to use for chunking. + + See :ref:`chunk_size`. + """ + + checkpoints: Union[bool, list] = True + """ + When to write checkpoint (intermediate table states) to disk. + + If True, checkpoints are written at each step. If False, no intermediate + checkpoints will be written before the end of run. Or, provide an explicit + list of models to checkpoint. + """ + + check_for_variability: bool = False + """ + Debugging feature to find broken model specifications. + + Enabling this check does not alter valid results but slows down model runs. + """ + + log_alt_losers: bool = False + """ + Write out expressions when all alternatives are unavailable. + + This can be useful for model development to catch errors in specifications. + Enabling this check does not alter valid results but slows down model runs. + """ + + use_shadow_pricing: bool = False + """turn shadow_pricing on and off for work and school location""" + + output_tables: list[str] = None + """list of output tables to write to CSV or HDF5""" + + want_dest_choice_sample_tables: bool = False + """turn writing of sample_tables on and off for all models""" + + cleanup_pipeline_after_run: bool = False + """ + Cleans up pipeline after successful run. + + This will clean up pipeline only after successful runs, by creating a + single-checkpoint pipeline file, and deleting any subprocess pipelines. + """ + + sharrow: Union[bool, str] = False + """ + Set the sharrow operating mode. + + .. versionadded:: 1.2 + + * `false` - Do not use sharrow. This is the default if no value is given. + * `true` - Use sharrow optimizations when possible, but fall back to + legacy `pandas.eval` systems when any error is encountered. This is the + preferred mode for running with sharrow if reliability is more important + than performance. + * `require` - Use sharrow optimizations, and raise an error if they fail + unexpectedly. This is the preferred mode for running with sharrow + if performance is a concern. + * `test` - Run every relevant calculation using both sharrow and legacy + systems, and compare them to ensure the results match. This is the slowest + mode of operation, but useful for development and debugging. + """ + + +class ZarrDigitalEncoding(PydanticBase): + """Digital encoding instructions for skim tables. + + .. versionadded:: 1.2 + """ + + regex: str + """A regular expression for matching skim matrix names. + + All skims with names that match under typical regular expression rules + for Python will be processed together. + """ + + joint_dict: str + """The name of the joint dictionary for this group. + + This must be a unique name for this set of skims, and a new array + will be added to the Dataset with this name. It will be an integer- + type array indicating the position of each element in the jointly + encoded dictionary.""" + + +class TAZ_Settings(PydanticBase): + """ + Complex settings for TAZ skims that are not just OMX file(s). + + .. versionadded:: 1.2 + """ + + omx: str = None + """The filename of the data stored in OMX format. + + This is treated as a fallback for the raw input data, if ZARR format data + is not available. + """ + + zarr: str = None + """The filename of the data stored in ZARR format. + + Reading ZARR data can be much faster than reading OMX format data, so if + this filename is given, the ZARR file format is preferred if it exists. If + it does not exist, then OMX data is read in and then ZARR data is written + out for future usage. + + .. versionadded:: 1.2 + """ + + zarr_digital_encoding: list[ZarrDigitalEncoding] = None + """ + A list of encodings to apply before saving skims in ZARR format. + + .. versionadded:: 1.2 + """ + + +class NetworkSettings(PydanticBase): + """ + Network level of service and skims settings + + The input for these settings is typically stored in one YAML file, + usually called ``network_los.yaml``. + """ + + zone_system: int + """Which zone system type is used. + + * 1 - TAZ only. + * 2 - MAZ and TAZ. + * 3 - MAZ, TAZ, and TAP + """ + + taz_skims: Union[str, TAZ_Settings] = None + """Instructions for how to load and pre-process skim matrices. + + If given as a string, it is interpreted as the location for OMX file(s), + either as a single file or as a glob-matching pattern for multiple files. + The time period for the matrix must be represented at the end of the matrix + name and be seperated by a double_underscore (e.g. `BUS_IVT__AM` indicates base + skim BUS_IVT with a time period of AM. + + Alternatively, this can be given as a nested dictionary defined via the + TAZ_Settings class, which allows for ZARR transformation and pre-processing. + """ + + skim_time_periods: dict + """time period upper bound values and labels + + * ``time_window`` - total duration (in minutes) of the modeled time span (Default: 1440 minutes (24 hours)) + * ``period_minutes`` - length of time (in minutes) each model time period represents. Must be whole factor of ``time_window``. (Default: 60 minutes) + * ``periods`` - Breakpoints that define the aggregate periods for skims and assignment + * ``labels`` - Labels to define names for aggregate periods for skims and assignment + """ + + read_skim_cache: bool = False + """Read cached skims (using numpy memmap) from output directory. + + Reading from memmap is much faster than omx, but the memmap is a huge + uncompressed file. + """ + + write_skim_cache: bool = False + """Write memmapped cached skims to output directory. + + This is needed if you want to use the cached skims to speed up subsequent + runs. + """ + + cache_dir: str = None + """alternate dir to read/write cache files (defaults to output_dir)""" diff --git a/activitysim/core/los.py b/activitysim/core/los.py index e69ed587f..c1c3a8451 100644 --- a/activitysim/core/los.py +++ b/activitysim/core/los.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. import logging +import os import warnings import numpy as np diff --git a/activitysim/core/pathbuilder_cache.py b/activitysim/core/pathbuilder_cache.py index 1bd219693..5153cf160 100644 --- a/activitysim/core/pathbuilder_cache.py +++ b/activitysim/core/pathbuilder_cache.py @@ -9,6 +9,7 @@ import numpy as np import pandas as pd +import psutil from activitysim.core import config, inject, simulate, util diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml index 6af4302c1..d8fc46215 100644 --- a/activitysim/examples/example_manifest.yaml +++ b/activitysim/examples/example_manifest.yaml @@ -488,77 +488,55 @@ data/final_accessibility.csv 949994345c9965054be260589fd64079e4032d2e4079e46b9eb9c758a40527c3 -- name: prototype_semcog +- name: production_semcog description: 67 zone test example for the SEMCOG region - # activitysim create -e prototype_semcog -d test_prototype_semcog - # cd test_prototype_semcog + # activitysim create -e production_semcog -d test_production_semcog + # cd test_production_semcog # python simulation.py -c configs -o output -d data # cd .. include: - - prototype_semcog/extensions - - prototype_semcog/data - - prototype_semcog/configs - - prototype_semcog/configs_mp - - prototype_semcog/output - - prototype_semcog/README.MD - - prototype_semcog/simulation.py + - production_semcog/extensions + - production_semcog/data + - production_semcog/configs + - production_semcog/configs_mp + - production_semcog/output + - production_semcog/README.MD + - production_semcog/simulation.py -- name: prototype_semcog_z500 - description: 500 zone intermediate size example for the SEMCOG region - # activitysim create -e prototype_semcog_z500 -d test_prototype_semcog_z500 - # cd test_prototype_semcog_z500 - # python simulation.py -c configs -o output -d data - # cd .. - include: - - prototype_semcog/extensions - - prototype_semcog/data - - prototype_semcog/configs - - prototype_semcog/configs_mp - - prototype_semcog/output - - prototype_semcog/README.MD - - prototype_semcog/simulation.py - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_z500/skims.omx - data/skims.omx - 7c824032940d1006e731d715bcee8771572e03558eef40ca1ca5f820bfc071ca - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_z500/households.csv - data/households.csv - 93b74d3ad86c4c49222a2427cca6014a12a34c0b46c8a934fc8f849f943b820f - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_z500/persons.csv - data/persons.csv - 3496761cc3b5f9718349ccd3ab97de7db96b8b5eed535a9bf97248fa087161a1 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_z500/land_use.csv - data/land_use.csv - d4ddb6f5fc7cd844e3e9149d03d32c841c5cb641a77aea9174045ddb85f27a3a - -- name: prototype_semcog_full - description: 2899 zone full size example for the SEMCOG region - # activitysim create -e prototype_semcog_full -d test_prototype_semcog_full - # cd test_prototype_semcog_full +- name: production_semcog_full + description: 2899 zone full size production model for the SEMCOG region + # activitysim create -e production_semcog_full -d test_production_semcog_full + # cd test_production_semcog_full # python simulation.py -c configs_mp -c configs -o output -d data # cd .. include: - - prototype_semcog/extensions - # prototype_semcog/data # data loaded from activitysim_resources - - prototype_semcog/configs - - prototype_semcog/configs_mp - - prototype_semcog/output - - prototype_semcog/README.MD - - prototype_semcog/simulation.py - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/skims.omx - data/skims.omx - efa1f87395df354ccfad7cfe4e67ace96c7ac04fa0290e5e6f8e2593fc0ba6a3 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/skims2.omx - data/skims2.omx - 3cec3453f12c3f07a19ef896b98da2ab79cbcca8bb541f845f6541bf878feeff + - production_semcog/extensions + - production_semcog/configs + - production_semcog/configs_mp + - production_semcog/output + - production_semcog/README.MD + - production_semcog/simulation.py + - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/skims_offpeak.omx + data/skims_offpeak.omx + 685e93a725e2c04e4b1fc11b92609472c347ce830475448e39b52947fa729d80 + - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/skims_peak.omx + data/skims_peak.omx + f92d924728c3984bc0d960b0258f6a395be5169167ccc88747503a446ff153f9 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/households.csv data/households.csv - 25f5fcabd2953951e0e1cfe3ca2cc8bb4bc63ee11bbdee299a8fc026a775986d + 34d5db243f7d176c536012f4d475fcc7dc51deb46c070e0e74f5e667578bcd15 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/persons.csv data/persons.csv - 934209ad17b789de07ad601c3f77a905a1c35c4bd7c15c62f088c045b3c64f43 + d8c7eac449efd57758504ca80cf86f150d232b85d659423baab9d203a47dbb6d - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/land_use.csv data/land_use.csv - 8f9146993bff315afc6ada52182cb3ea719a6a1ed07095b51535bc7c026285f6 + 2e3c5a8b50d638f6956b3c78c9402ce15f3ceaa4cdc5a51d9d802652b6843772 + - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/maz_to_maz_walk.csv + data/maz_to_maz_walk.csv + 3dd2511cfdac1380653f8cf93a11ee00c625800aad378b46cd1ce191371cec39 + - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/maz_to_maz_bike.csv + data/maz_to_maz_bike.csv + b158a181a48723117c86286a2e3041a35a980143d6b767e3f541c28bfbd38dcf - name: prototype_arc description: 36 zone test example for the ARC region diff --git a/activitysim/examples/prototype_semcog/.gitignore b/activitysim/examples/production_semcog/.gitignore similarity index 100% rename from activitysim/examples/prototype_semcog/.gitignore rename to activitysim/examples/production_semcog/.gitignore diff --git a/activitysim/examples/prototype_semcog/README.MD b/activitysim/examples/production_semcog/README.MD similarity index 100% rename from activitysim/examples/prototype_semcog/README.MD rename to activitysim/examples/production_semcog/README.MD diff --git a/activitysim/examples/prototype_semcog/configs/_dummy_coefficients.csv b/activitysim/examples/production_semcog/configs/_dummy_coefficients.csv similarity index 100% rename from activitysim/examples/prototype_semcog/configs/_dummy_coefficients.csv rename to activitysim/examples/production_semcog/configs/_dummy_coefficients.csv diff --git a/activitysim/examples/prototype_semcog/configs/accessibility.csv b/activitysim/examples/production_semcog/configs/accessibility.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/prototype_semcog/configs/accessibility.csv rename to activitysim/examples/production_semcog/configs/accessibility.csv index 37558cf5d..043dabb5d --- a/activitysim/examples/prototype_semcog/configs/accessibility.csv +++ b/activitysim/examples/production_semcog/configs/accessibility.csv @@ -31,6 +31,7 @@ round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) transit peak retail,trPkRetail,df.e05_retail * _decay transit peak total,trPkTotal,df.tot_emp * _decay +,trPKHH,df.tot_hhs * _decay #,, #,, transit off-peak #,, diff --git a/activitysim/examples/prototype_semcog/configs/accessibility.yaml b/activitysim/examples/production_semcog/configs/accessibility.yaml old mode 100755 new mode 100644 similarity index 87% rename from activitysim/examples/prototype_semcog/configs/accessibility.yaml rename to activitysim/examples/production_semcog/configs/accessibility.yaml index 86ab86629..e13e79fc7 --- a/activitysim/examples/prototype_semcog/configs/accessibility.yaml +++ b/activitysim/examples/production_semcog/configs/accessibility.yaml @@ -1,6 +1,6 @@ # columns from land_use table to add to df -land_use_columns: ['e05_retail', 'tot_emp'] +land_use_columns: ['e05_retail', 'tot_emp', 'tot_hhs'] CONSTANTS: # dispersion parameters diff --git a/activitysim/examples/prototype_semcog/configs/annotate_households.csv b/activitysim/examples/production_semcog/configs/annotate_households.csv old mode 100755 new mode 100644 similarity index 87% rename from activitysim/examples/prototype_semcog/configs/annotate_households.csv rename to activitysim/examples/production_semcog/configs/annotate_households.csv index 3ec08e3a7..f8df22993 --- a/activitysim/examples/prototype_semcog/configs/annotate_households.csv +++ b/activitysim/examples/production_semcog/configs/annotate_households.csv @@ -2,7 +2,7 @@ Description,Target,Expression #,, annotate households table after import ,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" #,,FIXME households.income can be negative - so we clip? -income,income,households.HINCP.fillna(0) +income,income,households.hincp.fillna(0) income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" #,, @@ -14,7 +14,7 @@ median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)" #,, #num_workers was renamed in import,, -,num_workers,"_PERSON_COUNT('(ESR==1)|(ESR==2)|(ESR==4)|(ESR==5)', persons, households)" +,num_workers,"_PERSON_COUNT('(esr==1)|(esr==2)|(esr==4)|(esr==5)', persons, households)" number of non_workers,num_non_workers,households.hhsize - num_workers #,, #,,we assume that everyone 16 and older is a potential driver @@ -27,13 +27,15 @@ num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', perso num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" -non_family,non_family,households.HHT.isin(HHT_NONFAMILY) -family,family,households.HHT.isin(HHT_FAMILY) +non_family,non_family,households.hht.isin(HHT_NONFAMILY) +family,family,households.hht.isin(HHT_FAMILY) home_is_urban,home_is_urban,"reindex(land_use.AreaType, households.home_zone_id) < setting('urban_threshold')" home_is_rural,home_is_rural,"reindex(land_use.AreaType, households.home_zone_id) > setting('rural_threshold')" +home_county,home_county,"reindex(land_use.COUNTY, households.home_zone_id)" +home_in_detroit,home_in_detroit,"reindex(land_use.COUNTY, households.home_zone_id) == 1" #,, default for work and school location logsums before auto_ownership model is run ,auto_ownership,households.auto_ownership -TAZ column to match settings file,TAZ,households.home_zone_id +TAZ column to match settings file,TAZ,households.TAZ number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)" diff --git a/activitysim/examples/prototype_semcog/configs/annotate_households_cdap.csv b/activitysim/examples/production_semcog/configs/annotate_households_cdap.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_households_cdap.csv rename to activitysim/examples/production_semcog/configs/annotate_households_cdap.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_households_workplace.csv b/activitysim/examples/production_semcog/configs/annotate_households_workplace.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_households_workplace.csv rename to activitysim/examples/production_semcog/configs/annotate_households_workplace.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_landuse.csv b/activitysim/examples/production_semcog/configs/annotate_landuse.csv old mode 100755 new mode 100644 similarity index 80% rename from activitysim/examples/prototype_semcog/configs/annotate_landuse.csv rename to activitysim/examples/production_semcog/configs/annotate_landuse.csv index 4ef3e4310..3321d09c4 --- a/activitysim/examples/prototype_semcog/configs/annotate_landuse.csv +++ b/activitysim/examples/production_semcog/configs/annotate_landuse.csv @@ -3,3 +3,4 @@ Description,Target,Expression household_density,household_density,land_use.tot_hhs / (land_use.tot_acres) employment_density,employment_density,land_use.tot_emp / (land_use.tot_acres) density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +,University_Name,"np.where(land_use.Univ_Tier==1, ""University of Michigan"", "" "")" diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons.csv b/activitysim/examples/production_semcog/configs/annotate_persons.csv old mode 100755 new mode 100644 similarity index 92% rename from activitysim/examples/prototype_semcog/configs/annotate_persons.csv rename to activitysim/examples/production_semcog/configs/annotate_persons.csv index b89e292f9..4c270bded --- a/activitysim/examples/prototype_semcog/configs/annotate_persons.csv +++ b/activitysim/examples/production_semcog/configs/annotate_persons.csv @@ -7,11 +7,11 @@ age_16_p,age_16_p,persons.age >= 16 adult,adult,persons.age >= 18 male,male,persons.sex == 1 female,female,persons.sex == 2 -,esr,persons.ESR.fillna(0) -,wkhp,persons.WKHP.fillna(0) -,wkw,persons.WKW.fillna(0) -,schg,persons.SCHG.fillna(0) -,mil,persons.MIL.fillna(0) +,esr,persons.esr.fillna(0) +,wkhp,persons.wkhp.fillna(0) +,wkw,persons.wkw.fillna(0) +,schg,persons.schg.fillna(0) +,mil,persons.mil.fillna(0) employment status type,pemploy,np.zeros(len(persons)) ,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)" ,pemploy,"np.where((persons['age'] >= 16) & ((esr == 3) | (esr == 6)), PEMPLOY_NOT, pemploy)" @@ -60,8 +60,11 @@ is_worker,is_worker,"np.where((pemploy == PEMPLOY_FULL) |( pemploy == PEMPLOY_PA home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" hh_child,hh_child,"reindex(households.children, persons.household_id)" person number,PNUM,persons.member_id -income,income,"reindex(households.HINCP, persons.household_id)" +income,income,"reindex(households.hincp, persons.household_id)" income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" is_fulltime_worker,is_fulltime_worker,"((age_16_p) & (wkhp >=35) & (wkw>=1) & (wkw<=4) & (~esr.isin([3,6])))" is_parttime_worker,is_parttime_worker,"((age_16_p) & (~esr.isin([3,6])) & (is_fulltime_worker == False))" +recoding existing as legacy,naicsp_recode,"persons['naicsp'].replace('-9', '0000')" +coding industry according to pums,industry_naics,naicsp_recode.str[0].astype(int)*10 +coding naicsp for telecommuting freq model, naics_category,naicsp_recode.str[0:2] diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv b/activitysim/examples/production_semcog/configs/annotate_persons_after_hh.csv old mode 100755 new mode 100644 similarity index 89% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_after_hh.csv index 7e5743c8e..8e8990906 --- a/activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv +++ b/activitysim/examples/production_semcog/configs/annotate_persons_after_hh.csv @@ -3,7 +3,7 @@ Description,Target,Expression #,, adults get full hh_value_of_time and children get 60% ,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" ,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" -,_hh_income,"reindex(households.HINCP, persons.household_id)" +,_hh_income,"reindex(households.hincp, persons.household_id)" ,_num_adults,"reindex(households.num_adults, persons.household_id)" ,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" ,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" @@ -20,3 +20,5 @@ Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 ,_has_children_6_to_12,"reindex(households.num_children_6_to_12, persons.household_id)" has_young_children,has_young_children,_has_young_children>0 has_children_6_to_12,has_children_6_to_12,_has_children_6_to_12>0 +home_county_,home_county_,"reindex(households.home_county, persons.household_id)" +home_county, home_county, home_county_ diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv b/activitysim/examples/production_semcog/configs/annotate_persons_cdap.csv old mode 100755 new mode 100644 similarity index 81% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_cdap.csv index 6e426628c..2ad5e56a6 --- a/activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv +++ b/activitysim/examples/production_semcog/configs/annotate_persons_cdap.csv @@ -4,4 +4,3 @@ travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" -,cdap_activity,"np.where((persons.work_from_home) & (persons.cdap_activity=='M'), np.random.choice(['N','H']), persons.cdap_activity)" diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_jtp.csv b/activitysim/examples/production_semcog/configs/annotate_persons_jtp.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_jtp.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_jtp.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_mtf.csv b/activitysim/examples/production_semcog/configs/annotate_persons_mtf.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_mtf.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_mtf.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_nmtf.csv b/activitysim/examples/production_semcog/configs/annotate_persons_nmtf.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_nmtf.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_nmtf.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_school.csv b/activitysim/examples/production_semcog/configs/annotate_persons_school.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_school.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_school.csv diff --git a/activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv b/activitysim/examples/production_semcog/configs/annotate_persons_workplace.csv old mode 100755 new mode 100644 similarity index 91% rename from activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv rename to activitysim/examples/production_semcog/configs/annotate_persons_workplace.csv index 36d6a01f8..e4f4762b4 --- a/activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv +++ b/activitysim/examples/production_semcog/configs/annotate_persons_workplace.csv @@ -30,3 +30,7 @@ work_zone_area_type,work_zone_area_type,"reindex(land_use.AreaType, persons.work ,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" #,,auto savings over walk or transit capped at 120 and normalized to unity ,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" +,umich_student,df.school_zone_id.isin(land_use[land_use.University_Name=='University of Michigan'].index.values) +,umich_worker,df.workplace_zone_id.isin(land_use[land_use.University_Name=='University of Michigan'].index.values) +#,, +,dest_hourly_peak_parking_cost,"reindex(land_use.parking_hourly, df.workplace_zone_id)" diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_destination.csv old mode 100755 new mode 100644 similarity index 78% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_destination.csv index c0e44b679..1bfb2a55b --- a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv +++ b/activitysim/examples/production_semcog/configs/atwork_subtour_destination.csv @@ -4,8 +4,6 @@ util_dist,util_dist,@_DIST,coef_dist_atwork util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork -# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork -# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum_atwork diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml b/activitysim/examples/production_semcog/configs/atwork_subtour_destination.yaml old mode 100755 new mode 100644 similarity index 88% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml rename to activitysim/examples/production_semcog/configs/atwork_subtour_destination.yaml index ea7a48e37..af05b1ece --- a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml +++ b/activitysim/examples/production_semcog/configs/atwork_subtour_destination.yaml @@ -3,6 +3,10 @@ SPEC: atwork_subtour_destination.csv SAMPLE_SPEC: atwork_subtour_destination_sample.csv COEFFICIENTS: atwork_subtour_destination_coeffs.csv +SIZE_TERM_SELECTOR: atwork +SEGMENTS: + - atwork +ORIG_ZONE_ID: workplace_zone_id SAMPLE_SIZE: 30 diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_coeffs.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_destination_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_coeffs.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_destination_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_destination_sample.csv old mode 100755 new mode 100644 similarity index 71% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_destination_sample.csv index bacf3e5c2..16035e5be --- a/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv +++ b/activitysim/examples/production_semcog/configs/atwork_subtour_destination_sample.csv @@ -4,7 +4,5 @@ util_dist,util_dist,@_DIST,coef_dist_atwork util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork -# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork -# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_frequency.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_frequency.csv diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.yaml b/activitysim/examples/production_semcog/configs/atwork_subtour_frequency.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.yaml rename to activitysim/examples/production_semcog/configs/atwork_subtour_frequency.yaml diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_alternatives.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_frequency_alternatives.csv diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_coeffs.csv b/activitysim/examples/production_semcog/configs/atwork_subtour_frequency_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_coeffs.csv rename to activitysim/examples/production_semcog/configs/atwork_subtour_frequency_coeffs.csv diff --git a/activitysim/examples/production_semcog/configs/auto_ownership.csv b/activitysim/examples/production_semcog/configs/auto_ownership.csv new file mode 100644 index 000000000..5714716c5 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/auto_ownership.csv @@ -0,0 +1,23 @@ +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_1,1 Adult (age 16+),num_drivers==1,,coef_cars1_drivers_1,coef_cars2_drivers_1,coef_cars3_drivers_1,coef_cars4_drivers_1 +util_drivers_2,2 Adult (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 +util_drivers_3_up,3+ Adults (age 16+),num_drivers>=3,,coef_cars1_drivers_3_up,coef_cars2_drivers_3_up,coef_cars3_drivers_3_up,coef_cars4_drivers_3_up +util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 +util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 +util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 +util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars2_presence_children_0_4,coef_cars34_presence_children_0_4,coef_cars34_presence_children_0_4 +util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 +util_num_workers_clip_3,Number of workers capped at 3,@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 +util_hh_income_0_15k,Piecewise Linear household income $0-15k,income_in_thousands < 15,,coef_cars1_hh_income_0_15k,coef_cars2_hh_income_0_15k,coef_cars3_hh_income_0_15k,coef_cars4_hh_income_0_15k +util_hh_income_15_35k,Piecewise Linear household income $15-35k,(income_in_thousands >= 15) & (income_in_thousands<35),,coef_cars1_hh_income_15_35k,coef_cars2_hh_income_15_35k,coef_cars3_hh_income_15_35k,coef_cars4_hh_income_15_35k +util_hh_income_35_50k,Piecewise Linear household income $35-50k,(income_in_thousands >= 35) & (income_in_thousands<50),,coef_cars1_hh_income_35_50k,coef_cars2_hh_income_35_50k,coef_cars3_hh_income_35_50k,coef_cars4_hh_income_35_50k +util_hh_income_50_75k,Piecewise Linear household income $50-75k,(income_in_thousands >= 50) & (income_in_thousands<75),,coef_cars1_hh_income_50_75k,coef_cars2_hh_income_50_75k,coef_cars3_hh_income_50_75k,coef_cars4_hh_income_50_75k +util_density,Density index,@df.density_index,,coef_cars1_density,coef_cars2_density,coef_cars3_density,coef_cars4_density +util_retail_transit,Retail accessibility (0.66*PK + 0.34*OP) by transit,(0.66*trPkRetail+0.34*trOpRetail),,coef_cars1_retail_transit,coef_cars2_retail_transit,coef_cars3_retail_transit,coef_cars4_retail_transit +util_retail_non_motor,Retail accessibility by non-motorized,nmRetail,,coef_cars1_retail_non_motor,coef_cars2_retail_non_motor,coef_cars3_retail_non_motor,coef_cars4_retail_non_motor +util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker +util_univ_students_GQ,University Students living in GQ households,(num_college_age > 0) & (type == 3),,coef_cars1_univ_GQ,coef_unavailable,coef_unavailable,coef_unavailable +util_constants,Alternative-specific constants,1,,coef_cars1,coef_cars2,coef_cars3,coef_cars4 +util_detroit,district specific constant,home_in_detroit,coef_detroit,0,0,,0 +#,,,,,,, +calib_constant,calibration constant,1,coef_cars0_asc,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc diff --git a/activitysim/examples/prototype_semcog/configs/auto_ownership.yaml b/activitysim/examples/production_semcog/configs/auto_ownership.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/auto_ownership.yaml rename to activitysim/examples/production_semcog/configs/auto_ownership.yaml diff --git a/activitysim/examples/production_semcog/configs/auto_ownership_coeffs.csv b/activitysim/examples/production_semcog/configs/auto_ownership_coeffs.csv new file mode 100644 index 000000000..525754c11 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/auto_ownership_coeffs.csv @@ -0,0 +1,76 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_cars1_drivers_1,-0.226,F +coef_cars1_drivers_2,0,F +coef_cars1_drivers_3_up,-0.398,F +coef_cars1_persons_16_17,-0.594,F +coef_cars1_persons_18_24,0.281,F +coef_cars1_persons_25_34,0,F +coef_cars1_presence_children_0_4,0,F +coef_cars1_presence_children_5_17,0.365,F +coef_cars1_num_workers_clip_3,-0.389,F +coef_cars1_hh_income_0_15k,-2.54,F +coef_cars1_hh_income_15_35k,-1.2,F +coef_cars1_hh_income_35_50k,0,F +coef_cars1_hh_income_50_75k,0,F +coef_cars1_density,0,F +coef_cars1_retail_transit,-0.444,F +coef_cars1_retail_non_motor,0.115,F +coef_cars1_auto_time_saving_per_worker,1.31,F +coef_cars1_univ_GQ,-3.56,F +coef_cars2_drivers_1,-3.05,F +coef_cars2_drivers_2,0,F +coef_cars2_drivers_3_up,-0.53,F +coef_cars2_persons_16_17,-1.41,F +coef_cars2_persons_18_24,-0.237,F +coef_cars2_persons_25_34,-0.26,F +coef_cars2_presence_children_0_4,0,F +coef_cars2_presence_children_5_17,0.526,F +coef_cars2_num_workers_clip_3,0.433,F +coef_cars2_hh_income_0_15k,-4.08,F +coef_cars2_hh_income_15_35k,-2.41,F +coef_cars2_hh_income_35_50k,-0.772,F +coef_cars2_hh_income_50_75k,-0.532,F +coef_cars2_density,-0.0487,F +coef_cars2_retail_transit,-0.715,F +coef_cars2_retail_non_motor,0,F +coef_cars2_auto_time_saving_per_worker,0.906,F +coef_cars3_drivers_1,-2.97,F +coef_cars3_drivers_2,0,F +coef_cars3_drivers_3_up,1.19,F +coef_cars34_persons_16_17,-1.46,F +coef_cars34_persons_18_24,0,F +coef_cars34_persons_25_34,-0.368,F +coef_cars34_presence_children_0_4,-0.468,F +coef_cars34_presence_children_5_17,0.387,F +coef_cars3_num_workers_clip_3,0.662,F +coef_cars3_hh_income_0_15k,-4.3,F +coef_cars3_hh_income_15_35k,-2.77,F +coef_cars3_hh_income_35_50k,-1.07,F +coef_cars3_hh_income_50_75k,-0.686,F +coef_cars3_density,-0.541,F +coef_cars3_retail_transit,-0.8,F +coef_cars3_retail_non_motor,0,F +coef_cars3_auto_time_saving_per_worker,0.9,F +coef_cars4_drivers_1,-2.58,F +coef_cars4_drivers_2,0,F +coef_cars4_drivers_3_up,1.57,F +coef_cars4_num_workers_clip_3,0.929,F +coef_cars4_hh_income_0_15k,-4.33,F +coef_cars4_hh_income_15_35k,-3.05,F +coef_cars4_hh_income_35_50k,-1.2,F +coef_cars4_hh_income_50_75k,-0.719,F +coef_cars4_density,-0.8,F +coef_cars4_retail_transit,-0.778,F +coef_cars4_retail_non_motor,0,F +coef_cars4_auto_time_saving_per_worker,0.538,F +coef_cars1,3.06,F +coef_cars2,5.44,F +coef_cars3,4.39,F +coef_cars4,3.23,F +coef_detroit,0.25,F +coef_cars0_asc,0.30,F +coef_cars1_asc,0.10,F +coef_cars2_asc,-0.40,F +coef_cars3_asc,-0.80,F +coef_cars4_asc,-0.80,F diff --git a/activitysim/examples/prototype_semcog/configs/cdap.yaml b/activitysim/examples/production_semcog/configs/cdap.yaml old mode 100755 new mode 100644 similarity index 89% rename from activitysim/examples/prototype_semcog/configs/cdap.yaml rename to activitysim/examples/production_semcog/configs/cdap.yaml index 546db913a..9ce3a1d2b --- a/activitysim/examples/prototype_semcog/configs/cdap.yaml +++ b/activitysim/examples/production_semcog/configs/cdap.yaml @@ -1,10 +1,8 @@ COEFFICIENTS: _dummy_coefficients.csv -INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv - CONSTANTS: FULL: 1 PART: 2 diff --git a/activitysim/examples/prototype_semcog/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/production_semcog/configs/cdap_fixed_relative_proportions.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/cdap_fixed_relative_proportions.csv rename to activitysim/examples/production_semcog/configs/cdap_fixed_relative_proportions.csv diff --git a/activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/production_semcog/configs/cdap_indiv_and_hhsize1.csv old mode 100755 new mode 100644 similarity index 85% rename from activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv rename to activitysim/examples/production_semcog/configs/cdap_indiv_and_hhsize1.csv index a3ce4a01c..18ffbe506 --- a/activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv +++ b/activitysim/examples/production_semcog/configs/cdap_indiv_and_hhsize1.csv @@ -49,6 +49,13 @@ University student interaction with off-peak accessibility to retail,(ptype == 3 Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,0.08233, Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,0.08233, Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,0.08233, +# University Student CDAP calibration,,,, +working university student in non-family housing,(ptype == 3) & (is_worker) & (hht >= 4),0.095989229,-0.565315767, +working university student in family housing,(ptype == 3) & (is_worker) & (hht < 4) & (type != 3),0.160883504,-0.939483752, +working university student in GQ housing,(ptype == 3) & (is_worker) & (hht < 4) & (type == 3),0.273268829,-1.255669628, +non-working university student in non-family housing,(ptype == 3) & (~is_worker) & (hht >= 4),0.165733328,-1.2223963, +non-working university student in family housing,(ptype == 3) & (~is_worker) & (hht < 4) & (type != 3),0.05864678,-0.224588408, +non-working university student in GQ housing,(ptype == 3) & (~is_worker) & (hht < 4) & (type == 3),0.22312397,-1.183939606, # commented out because not used in mtctm1,,,, # Full-time worker interaction with usual work location is home,(ptype == 1) * usualWorkLocationIsHome,-1.758,,0.1813 # Part-time worker interaction with usual work location is home,(ptype == 2) * usualWorkLocationIsHome,-1.758,,0.1813 @@ -64,3 +71,12 @@ Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,1.848,1.711 #Turning Mandatory pattern off for FT and PT who work from home (sandag),,,, "Full time worker, works from home",(ptype == 1) & (work_from_home),-999,, "Part time worker, works from home",(ptype == 2) & (work_from_home),-999,, +#,,,, +full-time worker calibration send more ftworker to M,ptype==1,-0.1,0.32,0.12 +part-time worker calibration send more ptworker to N,ptype==2,-0.3,0.45,0.22 +col students calibration send more univ student to M,ptype==3,-0.41,0.2,0.3 +,ptype==4,-0.15,-0.17,0.68 +,ptype==5,0,-0.06,0.25 +,ptype==6,0.47,,-0.6 +,ptype==7,0.27,,-0.2 +,ptype==8,-0.73,1,0.1 diff --git a/activitysim/examples/prototype_semcog/configs/cdap_interaction_coefficients.csv b/activitysim/examples/production_semcog/configs/cdap_interaction_coefficients.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/cdap_interaction_coefficients.csv rename to activitysim/examples/production_semcog/configs/cdap_interaction_coefficients.csv diff --git a/activitysim/examples/production_semcog/configs/constants.yaml b/activitysim/examples/production_semcog/configs/constants.yaml new file mode 100644 index 000000000..fb2a0cdf5 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/constants.yaml @@ -0,0 +1,123 @@ +## ActivitySim +## See full license in LICENSE.txt. +### MODE CHOICE RELATED CONSTANTS + +# cash discount factor for seniors (persons age 65+) or driving-age students, non-driving age students, and pre-school children +cash_discount_factor: 0.5 + +#pass discount factor by operator and person type (1-8) +transit_pass_discount_factor_ddot: + 1: 0.74 + 2: 0.73 + 3: 0.68 + 4: 0.67 + 5: 0.36 + 6: 0.44 + 7: 0.42 + 8: 1.0 +transit_pass_discount_factor_smart: + 1: 0.65 + 2: 0.54 + 3: 0.61 + 4: 0.58 + 5: 0.15 + 6: 0.21 + 7: 0.12 + 8: 1.0 +transit_pass_discount_factor_aata: + 1: 0.08 + 2: 0.16 + 3: 0.02 + 4: 0.25 + 5: 0.05 + 6: 0.12 + 7: 0.17 + 8: 1.0 +transit_pass_discount_factor_um: + 1: 0.0 + 2: 0.0 + 3: 0.0 + 4: 0.0 + 5: 0.0 + 6: 0.0 + 7: 0.0 + 8: 0.0 +transit_pass_discount_factor_other: + 1: 0.34 + 2: 0.60 + 3: 0.35 + 4: 0.68 + 5: 0.25 + 6: 0.75 + 7: 0.84 + 8: 1.0 + +costPerMile: 18.29 +costShareSr2: 1.75 +costShareSr3: 2.50 + +waitThresh: 10.00 +walkThresh: 1.00 +shortWalk: 0.333 +longWalk: 0.667 +walkSpeed: 3.00 +bikeThresh: 6.00 +bikeSpeed: 12.00 +valueOfTime: 8.00 + +max_local_walk_dist: 0.85 +max_prm_walk_dist: 1.2 +max_mix_walk_dist: 1.2 +walk_speed: 2.8 #mph + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + diff --git a/activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv b/activitysim/examples/production_semcog/configs/destination_choice_size_terms.csv old mode 100755 new mode 100644 similarity index 79% rename from activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv rename to activitysim/examples/production_semcog/configs/destination_choice_size_terms.csv index ef359c904..12d4a9818 --- a/activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv +++ b/activitysim/examples/production_semcog/configs/destination_choice_size_terms.csv @@ -14,12 +14,12 @@ non_mandatory,social,0.487,0,0,0,0,0,0,0,0,0,0,0,0,0.244,0,0.177,1,0,0,0,0,0 non_mandatory,othdiscr,0.184,0,0,0,0,0.185,0,0,0,0,0,0,0,0.899,0.511,0.004,1.081,1,0,0,0,0 atwork,atwork,0.03,0,0,0,0.049,1,0,0,0,0.141,0,0,0.482,0.048,0.092,0.013,0,0,0.053,0,0,0 trip,work,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0 -trip,escort,0.001,0,0,0,0,0.225,0,0,0,0,0,0,0,0.144,0.144,0.144,0.144,0,0,0.465,0.166,0 -trip,shopping,0.001,0,0,0,0,0.999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -trip,eatout,0,0,0,0,0,0.742,0,0,0,0,0,0,0,0.258,0.258,0.258,0.258,0,0,0,0,0 -trip,othmaint,0.001,0,0,0,0,0.481,0,0,0,0,0,0,0,0.518,0.518,0.518,0.518,0,0,0,0,0 -trip,social,0.001,0,0,0,0,0.521,0,0,0,0,0,0,0,0.478,0.478,0.478,0.478,0,0,0,0,0 -trip,othdiscr,0.252,0,0,0,0,0.212,0,0,0,0,0,0,0,0.273,0.273,0.273,0.273,0.165,0.165,0,0.098,0 +trip,escort,0.192,0,0,0,0,0.188,0,0,0,0,0,0,0,0,0.164,0.078,0.791,0,0,1,0.768,0 +trip,shopping,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.005,0.003,0,0,0,0 +trip,eatout,0.004,0,0,0,0,0.283,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +trip,othmaint,0.029,0,0,0,0.026,0.587,0,0,0,0.268,0,0,0.445,0.161,1,0.556,0.336,0.501,0.478,0,0,0 +trip,social,0.487,0,0,0,0,0,0,0,0,0,0,0,0,0.244,0,0.177,1,0,0,0,0,0 +trip,othdiscr,0.184,0,0,0,0,0.185,0,0,0,0,0,0,0,0.899,0.511,0.004,1.081,1,0,0,0,0 trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 # not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,0 #trip,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 diff --git a/activitysim/examples/prototype_semcog/configs/free_parking.csv b/activitysim/examples/production_semcog/configs/free_parking.csv old mode 100755 new mode 100644 similarity index 80% rename from activitysim/examples/prototype_semcog/configs/free_parking.csv rename to activitysim/examples/production_semcog/configs/free_parking.csv index 2505a9bf7..8bf15e159 --- a/activitysim/examples/prototype_semcog/configs/free_parking.csv +++ b/activitysim/examples/production_semcog/configs/free_parking.csv @@ -1,6 +1,9 @@ Label,Description,Expression,free,pay +util_nopay_parking,free parking where no costs,parking_daily==0,0,-999 util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0 util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0 util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0 util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0 util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership0),-1.5, diff --git a/activitysim/examples/prototype_semcog/configs/free_parking.yaml b/activitysim/examples/production_semcog/configs/free_parking.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/free_parking.yaml rename to activitysim/examples/production_semcog/configs/free_parking.yaml diff --git a/activitysim/examples/production_semcog/configs/free_parking_annotate_persons_preprocessor.csv b/activitysim/examples/production_semcog/configs/free_parking_annotate_persons_preprocessor.csv new file mode 100644 index 000000000..045e60fbc --- /dev/null +++ b/activitysim/examples/production_semcog/configs/free_parking_annotate_persons_preprocessor.csv @@ -0,0 +1,4 @@ +Description,Target,Expression +,workplace_county_id,"reindex(land_use.COUNTY, persons.workplace_zone_id)" +,parking_hourly," reindex(land_use.parking_hourly, persons.workplace_zone_id)" +,parking_daily,"reindex(land_use.parking_daily, persons.workplace_zone_id)" diff --git a/activitysim/examples/prototype_semcog/configs/free_parking_coeffs.csv b/activitysim/examples/production_semcog/configs/free_parking_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/free_parking_coeffs.csv rename to activitysim/examples/production_semcog/configs/free_parking_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/initialize_households.yaml b/activitysim/examples/production_semcog/configs/initialize_households.yaml old mode 100755 new mode 100644 similarity index 78% rename from activitysim/examples/prototype_semcog/configs/initialize_households.yaml rename to activitysim/examples/production_semcog/configs/initialize_households.yaml index 5d8a5dbd1..fdd962cb2 --- a/activitysim/examples/prototype_semcog/configs/initialize_households.yaml +++ b/activitysim/examples/production_semcog/configs/initialize_households.yaml @@ -19,6 +19,3 @@ annotate_tables: DF: persons TABLES: - households - -# add the work and school location size tables for backwards compatibility if desired -add_size_tables: False \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/initialize_landuse.yaml b/activitysim/examples/production_semcog/configs/initialize_landuse.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/initialize_landuse.yaml rename to activitysim/examples/production_semcog/configs/initialize_landuse.yaml diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_composition.csv b/activitysim/examples/production_semcog/configs/joint_tour_composition.csv old mode 100755 new mode 100644 similarity index 97% rename from activitysim/examples/prototype_semcog/configs/joint_tour_composition.csv rename to activitysim/examples/production_semcog/configs/joint_tour_composition.csv index de03e3dcc..d0ff7cf61 --- a/activitysim/examples/prototype_semcog/configs/joint_tour_composition.csv +++ b/activitysim/examples/production_semcog/configs/joint_tour_composition.csv @@ -19,4 +19,6 @@ util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of h util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, -util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable \ No newline at end of file +util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable +#,,,,, +util_calib,calibration cte,1,4.75,-2,0.27 diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_composition.yaml b/activitysim/examples/production_semcog/configs/joint_tour_composition.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_composition.yaml rename to activitysim/examples/production_semcog/configs/joint_tour_composition.yaml diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/production_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv rename to activitysim/examples/production_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_composition_coeffs.csv b/activitysim/examples/production_semcog/configs/joint_tour_composition_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_composition_coeffs.csv rename to activitysim/examples/production_semcog/configs/joint_tour_composition_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_destination.csv b/activitysim/examples/production_semcog/configs/joint_tour_destination.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_destination.csv rename to activitysim/examples/production_semcog/configs/joint_tour_destination.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml b/activitysim/examples/production_semcog/configs/joint_tour_destination.yaml old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml rename to activitysim/examples/production_semcog/configs/joint_tour_destination.yaml index bac02c2d7..8043564ee --- a/activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml +++ b/activitysim/examples/production_semcog/configs/joint_tour_destination.yaml @@ -18,7 +18,7 @@ CHOOSER_SEGMENT_COLUMN_NAME: tour_type SIMULATE_CHOOSER_COLUMNS: - tour_type - - TAZ + - home_zone_id - person_id - income_segment - num_children @@ -26,7 +26,7 @@ SIMULATE_CHOOSER_COLUMNS: LOGSUM_SETTINGS: tour_mode_choice.yaml # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ +CHOOSER_ORIG_COL_NAME: home_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 14 OUT_PERIOD: 14 diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_destination_coeffs.csv b/activitysim/examples/production_semcog/configs/joint_tour_destination_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_destination_coeffs.csv rename to activitysim/examples/production_semcog/configs/joint_tour_destination_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_destination_sample.csv b/activitysim/examples/production_semcog/configs/joint_tour_destination_sample.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_destination_sample.csv rename to activitysim/examples/production_semcog/configs/joint_tour_destination_sample.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.csv b/activitysim/examples/production_semcog/configs/joint_tour_frequency.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_frequency.csv rename to activitysim/examples/production_semcog/configs/joint_tour_frequency.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.yaml b/activitysim/examples/production_semcog/configs/joint_tour_frequency.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_frequency.yaml rename to activitysim/examples/production_semcog/configs/joint_tour_frequency.yaml diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/production_semcog/configs/joint_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_frequency_alternatives.csv rename to activitysim/examples/production_semcog/configs/joint_tour_frequency_alternatives.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/production_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv rename to activitysim/examples/production_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv index c5580ed71..03db5879b --- a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv +++ b/activitysim/examples/production_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv @@ -29,4 +29,4 @@ Description,Target,Expression logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.TAZ)" +nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.home_zone_id)" diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_coeffs.csv b/activitysim/examples/production_semcog/configs/joint_tour_frequency_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_frequency_coeffs.csv rename to activitysim/examples/production_semcog/configs/joint_tour_frequency_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_participation.csv b/activitysim/examples/production_semcog/configs/joint_tour_participation.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_participation.csv rename to activitysim/examples/production_semcog/configs/joint_tour_participation.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_participation.yaml b/activitysim/examples/production_semcog/configs/joint_tour_participation.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_participation.yaml rename to activitysim/examples/production_semcog/configs/joint_tour_participation.yaml diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/production_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv rename to activitysim/examples/production_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_participation_coeffs.csv b/activitysim/examples/production_semcog/configs/joint_tour_participation_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_participation_coeffs.csv rename to activitysim/examples/production_semcog/configs/joint_tour_participation_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml b/activitysim/examples/production_semcog/configs/joint_tour_scheduling.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml rename to activitysim/examples/production_semcog/configs/joint_tour_scheduling.yaml diff --git a/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/production_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/production_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/logging.yaml b/activitysim/examples/production_semcog/configs/logging.yaml old mode 100755 new mode 100644 similarity index 75% rename from activitysim/examples/prototype_semcog/configs/logging.yaml rename to activitysim/examples/production_semcog/configs/logging.yaml index 657507ee6..7b4f40795 --- a/activitysim/examples/prototype_semcog/configs/logging.yaml +++ b/activitysim/examples/production_semcog/configs/logging.yaml @@ -1,70 +1,53 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - filelock: - level: WARN - - sharrow: - level: INFO - - blib2to3: - level: WARN - - black: - level: WARN - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: elapsedFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - elapsedFormatter: - (): activitysim.core.tracing.ElapsedTimeFormatter - format: '[{elapsedTime}] {levelname:s}: {message:s}' - style: '{' +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency.csv old mode 100755 new mode 100644 similarity index 92% rename from activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv rename to activitysim/examples/production_semcog/configs/mandatory_tour_frequency.csv index 51094ee82..cfc8e1de1 --- a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency.csv @@ -99,3 +99,9 @@ util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable +util_univ_worker_non_family,working university student in non-family housing,(ptype == 3) & (is_worker) & (hht >= 4),coef_univ_worker_non_family_work1,coef_univ_worker_non_family_work2,0,coef_univ_worker_non_family_school2,coef_univ_worker_non_family_work_and_school +util_univ_worker_family,working university student in family housing,(ptype == 3) & (is_worker) & (hht < 4) & (type != 3),coef_univ_worker_family_work1,coef_univ_worker_family_work2,0,coef_univ_worker_family_school2,coef_univ_worker_family_work_and_school +util_univ_worker_GQ,working university student in GQ housing,(ptype == 3) & (is_worker) & (hht < 4) & (type == 3),coef_univ_worker_GQ_work1,coef_univ_worker_GQ_work2,0,coef_univ_worker_GQ_school2,coef_univ_worker_GQ_work_and_school +util_univ_non_worker_non_family,non_working university student in non_family housing,(ptype == 3) & (~is_worker) & (hht >= 4),coef_unavailable,coef_unavailable,0,coef_univ_non_worker_non_family_school2,coef_unavailable +util_univ_non_worker_family,non_working university student in family housing,(ptype == 3) & (~is_worker) & (hht < 4) & (type != 3),coef_unavailable,coef_unavailable,0,coef_univ_non_worker_family_school2,coef_unavailable +util_univ_non_worker_GQ,non_working university student in GQ housing,(ptype == 3) & (~is_worker) & (hht < 4) & (type == 3),coef_unavailable,coef_unavailable,0,coef_univ_non_worker_GQ_school2,coef_unavailable diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.yaml b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.yaml rename to activitysim/examples/production_semcog/configs/mandatory_tour_frequency.yaml diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/production_semcog/configs/mandatory_tour_frequency_alternatives.csv diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency_coeffs.csv old mode 100755 new mode 100644 similarity index 59% rename from activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv rename to activitysim/examples/production_semcog/configs/mandatory_tour_frequency_coeffs.csv index c0909e681..709bb04b6 --- a/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv +++ b/activitysim/examples/production_semcog/configs/mandatory_tour_frequency_coeffs.csv @@ -1,5 +1,20 @@ coefficient_name,value,constrain -coef_unavailable,-999,T +coef_univ_non_worker_GQ_school2,1.1053061791972465,F +coef_univ_non_worker_family_school2,0.4144309804926236,F +coef_univ_non_worker_non_family_school2,0.6905737525503568,F +coef_univ_worker_GQ_school2,0.796660924210737,F +coef_univ_worker_GQ_work1,-1.1776031331447314,F +coef_univ_worker_GQ_work2,-1.3648970409025836,F +coef_univ_worker_GQ_work_and_school,0.14803997146655884,F +coef_univ_worker_family_school2,0.6628398162232699,F +coef_univ_worker_family_work1,-0.46637447728018944,F +coef_univ_worker_family_work2,-1.0892026364474372,F +coef_univ_worker_family_work_and_school,-0.39187199562394276,F +coef_univ_worker_non_family_school2,0.5788696920089542,F +coef_univ_worker_non_family_work1,-0.8581057975250049,F +coef_univ_worker_non_family_work2,-1.9822014482680475,F +coef_univ_worker_non_family_work_and_school,-0.538294137808463,F +coef_unavailable,-999.0,T coef_ft_worker_work2_asc,-3.3781,F coef_pt_worker_work2_asc,-3.0476,F coef_univ_work1_asc,-2.630262534,F @@ -44,11 +59,11 @@ coef_hh_income_gt_50k_worker_work_and_school,0.0347,F coef_hh_income_gt_50k_student_work_and_school,-0.0528,F coef_non_family_hh_category1,-0.25,F coef_non_family_hh_category2,-0.1792,F -coef_num_under_16_not_at_school_work2,0.1804, -coef_num_under_16_not_at_school_school2,0.0866, -coef_num_under_16_not_at_school_work_and_school,-0.1955, -coef_home_urban_work1,-0.2831, -coef_home_urban_work2,0.2308, -coef_home_urban_school1,-0.1361, -coef_home_urban_school2,0.317, -coef_home_urban_work_and_school,-0.3509, +coef_num_under_16_not_at_school_work2,0.1804,F +coef_num_under_16_not_at_school_school2,0.0866,F +coef_num_under_16_not_at_school_work_and_school,-0.1955,F +coef_home_urban_work1,-0.2831,F +coef_home_urban_work2,0.2308,F +coef_home_urban_school1,-0.1361,F +coef_home_urban_school2,0.317,F +coef_home_urban_work_and_school,-0.3509,F diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/production_semcog/configs/mandatory_tour_scheduling.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling.yaml rename to activitysim/examples/production_semcog/configs/mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/production_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/production_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv new file mode 100644 index 000000000..d96114245 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv @@ -0,0 +1,15 @@ +Description,Target,Expression +# make expressions are consistent with reference bins,, +departure_reference_bin,departureRefBin,9 +arrival_reference_bin,arrivalRefBin,29 +duration_reference_bin,durationRefBin,20 +departure_shift,departureLinearShift1,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift,durationShift,"(20-df.duration)*(df.duration<=20) + (df.duration-20)*(df.duration>20)" +# school specific bins +departure_reference_bin_school,departureRefBin_school,9 +arrival_reference_bin_school,arrivalRefBin_school,29 +duration_reference_bin_school,durationRefBin_school,16 +departure_shift_school,departureLinearShift1_school,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)" +arrival_shift_school,arrivalLinearShift1_school,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)" +duration_shift_school,durationShift_school,"(16-df.duration)*(df.duration<=16) + (df.duration-16)*(df.duration>16)" diff --git a/activitysim/examples/production_semcog/configs/network_los.yaml b/activitysim/examples/production_semcog/configs/network_los.yaml new file mode 100644 index 000000000..c6f3c5f89 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/network_los.yaml @@ -0,0 +1,39 @@ + +#inherit_settings: True +#alternate dir to read/write skim cache (defaults to output_dir) +#cache_dir: data/cache + +zone_system: 2 + +#skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +taz_skims: skims*.omx + +maz: land_use.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + # # maz_to_maz blending distance (missing or 0 means no blending) + # max_blend_distance: + # DIST: 5 + # # blend distance of 0 means no blending + # DISTBIKE: 0 + # DISTWALK: 0 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + #blend_distance_skim_name: DIST + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 7, 12, 23, 31, 48] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.csv old mode 100755 new mode 100644 similarity index 73% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.csv index baf6f3b53..a181dfcaa --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv +++ b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.csv @@ -1,5 +1,6 @@ Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +dest_county,_DESTCOUNTY@skims['COUNTY'],1,1,1,1,1,1 util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr @@ -17,3 +18,10 @@ Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +#,,,,,,, +util_Wayne_washtenaw_ASC,"@np.where((df['home_county']==2) & (_DESTCOUNTY==5), 1, 0)",-0.5,-0.5,-0.5,-0.5,-0.5,-0.5 +util_washtenaw_Wayne_ASC,"@np.where((df['home_county']==5) & (_DESTCOUNTY==2), 1, 0)",-0.5,-0.5,-0.5,-0.5,-0.5,-0.5 +util_detorit_macomb_asc,"@np.where((df['home_county']==1) & (_DESTCOUNTY==4), 1, 0)",-0.3,-0.3,-0.3,-0.3,-0.3,-0.3 +util_macome_detroit_asc,"@np.where((df['home_county']==4) & (_DESTCOUNTY==1), 1, 0)",-0.3,-0.3,-0.3,-0.3,-0.3,-0.3 +util_detroit_oakland_asc,"@np.where((df['home_county']==1) & (_DESTCOUNTY==3), 1, 0)",-0.3,-0.3,-0.3,-0.3,-0.3,-0.3 +util_oakland_detorit_asc,"@np.where((df['home_county']==3) & (_DESTCOUNTY==1), 1, 0)",-0.3,-0.3,-0.3,-0.3,-0.3,-0.3 diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.yaml old mode 100755 new mode 100644 similarity index 85% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.yaml index 61ade06f1..7f2ff7633 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml +++ b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination.yaml @@ -26,7 +26,7 @@ SEGMENTS: SIMULATE_CHOOSER_COLUMNS: - tour_type - - TAZ + - home_zone_id - person_id - income_segment - pemploy @@ -34,11 +34,12 @@ SIMULATE_CHOOSER_COLUMNS: - age_0_to_5 - age_6_to_12 - hh_child + - home_county LOGSUM_SETTINGS: tour_mode_choice.yaml # model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ +CHOOSER_ORIG_COL_NAME: home_zone_id ALT_DEST_COL_NAME: alt_dest IN_PERIOD: 14 OUT_PERIOD: 14 @@ -53,11 +54,4 @@ CONSTANTS: WORK_LOW_SEGMENT_ID: 1 WORK_MED_SEGMENT_ID: 2 WORK_HIGH_SEGMENT_ID: 3 - WORK_VERYHIGH_SEGMENT_ID: 4 - - -preprocessor: - SPEC: non_mandatory_tour_destination_annotate_tours_preprocessor - DF: tours - TABLES: - - persons \ No newline at end of file + WORK_VERYHIGH_SEGMENT_ID: 4 \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_coeffs.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_destination_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination_sample.csv old mode 100755 new mode 100644 similarity index 72% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_destination_sample.csv index 1ab7b426d..8cebab344 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv +++ b/activitysim/examples/production_semcog/configs/non_mandatory_tour_destination_sample.csv @@ -1,5 +1,6 @@ Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +dest_county,_DESTCOUNTY@skims['COUNTY'],1,1,1,1,1,1 util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr @@ -15,3 +16,10 @@ util_dist_child_0_5,@(df['age_0_to_5']==True) * _DIST,0,0,0,0,0,0 util_dist_child_6_12,@(df['age_6_to_12']==True) * _DIST,0,0,0,0,0,0 Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +#,,,,,,, +util_Wayne_washtenaw_ASC,"@np.where((df['home_county']==2) & (_DESTCOUNTY==5), 1, 0)",-5,-5,-5,-5,-5,-5 +util_washtenaw_Wayne_ASC,"@np.where((df['home_county']==5) & (_DESTCOUNTY==2), 1, 0)",-5,-5,-5,-5,-5,-5 +util_detorit_macomb_asc,"@np.where((df['home_county']==1) & (_DESTCOUNTY==4), 1, 0)",-4,-4,-4,-4,-4,-4 +util_macome_detroit_asc,"@np.where((df['home_county']==4) & (_DESTCOUNTY==1), 1, 0)",-4,-4,-4,-4,-4,-4 +util_detroit_oakland_asc,"@np.where((df['home_county']==1) & (_DESTCOUNTY==3), 1, 0)",-4,-4,-4,-4,-4,-4 +util_oakland_detorit_asc,"@np.where((df['home_county']==3) & (_DESTCOUNTY==1), 1, 0)",-4,-4,-4,-4,-4,-4 diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency.csv index 76c60822e..e45fd17d6 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency.csv @@ -208,4 +208,11 @@ util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant -util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour \ No newline at end of file +util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour +#,,,,,,,,,, +util_total_number_of_tours_is_1_calib,Total Number of Tours = 1,tot_tours == 1,0.5,0.5,1.2,-0.6,,0.5,0.5,0.5 +util_total_number_of_tours_is_2_calib,Total Number of Tours = 2,tot_tours == 2,,0.3,1,0.9,0.4,0.5,,0.8 +util_total_number_of_tours_is_3_calib,Total Number of Tours = 3,tot_tours == 3,,,,0.5,,0.1,,0.5 +util_total_number_of_tours_is_4_calib,Total Number of Tours = 4,tot_tours == 4,,,,0.5,,,,0.1 +util_total_number_of_tours_is_5_calib,Total Number of Tours = 5,tot_tours == 5,,,,,,,, +util_total_number_of_tours_is_6_plus_calib,Total Number of Tours = 6+,tot_tours > 5,,,,,,,, diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.yaml rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency.yaml diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_alternatives.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/production_semcog/configs/non_mandatory_tour_scheduling.yaml similarity index 93% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling.yaml rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_scheduling.yaml index 34b5a9a8e..1dd3561ff 100644 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling.yaml +++ b/activitysim/examples/production_semcog/configs/non_mandatory_tour_scheduling.yaml @@ -11,11 +11,12 @@ preprocessor: - joint_tour_participants SIMULATE_CHOOSER_COLUMNS: - + - home_zone_id - age - female - adult - ptype + - is_university - has_pre_school_child_with_mandatory - has_driving_age_child_with_mandatory - retired_adults_only_hh @@ -32,7 +33,7 @@ SIMULATE_CHOOSER_COLUMNS: - num_add_shop_maint_tours - num_add_soc_discr_tours -#LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_SETTINGS: tour_mode_choice.yaml # map : : # segmentation of tours for processing may differ from segmentation of spec @@ -72,7 +73,7 @@ SPEC_SEGMENTS: ## alts preprocessor keyed by #ALTS_PREPROCESSOR: # escort: -# SPEC: non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv +# SPEC: tour_scheduling_nonmandatory_escort_annotate_alts_preprocessor.csv # DF: alt_tdd # shopping: # SPEC: non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv @@ -90,4 +91,4 @@ SPEC_SEGMENTS: # SPEC: non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv # DF: alt_tdd # -#DESTINATION_FOR_TOUR_PURPOSE: destination +DESTINATION_FOR_TOUR_PURPOSE: destination diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/production_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/production_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/production_semcog/configs/parking_location_choice_at_university.yaml b/activitysim/examples/production_semcog/configs/parking_location_choice_at_university.yaml new file mode 100644 index 000000000..85af3dac4 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/parking_location_choice_at_university.yaml @@ -0,0 +1,25 @@ + +# Colunn in the landuse file containing the university codes +LANDUSE_UNIV_CODE_COL_NAME: University_Name +# Specify which university codes should require parking +UNIV_CODES_THAT_REQUIRE_PARKING: + - University of Michigan + +# Parking on campus is should only be required for drive tours +# Names need to be consistent with tour modes +TOUR_MODES_THAT_REQUIRE_PARKING: + - DRIVEALONE + - SHARED2 + - SHARED3 + +# Tour Purposes that should be parked at the nearest lot instead of +# sampled based on lot size (primary_purpose field in tours table) +TOUR_PURPOSES_TO_NEAREST_LOT: + - work + - atwork + +# landuse column that contains the number of parking spaces in zone +LANDUSE_PARKING_SPACES_COL_NAME: parking_spaces +# landuse column containing the university the parking spaces belong to +# codes should match the codes in LANDUSE_UNIV_CODE_COL_NAME +LANDUSE_PARKING_UNIV_CODE_COL_NAME: University_Name diff --git a/activitysim/examples/prototype_semcog/configs/school_location.csv b/activitysim/examples/production_semcog/configs/school_location - Copy.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/school_location.csv rename to activitysim/examples/production_semcog/configs/school_location - Copy.csv diff --git a/activitysim/examples/production_semcog/configs/school_location.csv b/activitysim/examples/production_semcog/configs/school_location.csv new file mode 100644 index 000000000..808e1eb21 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/school_location.csv @@ -0,0 +1,19 @@ +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0 +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum_uni,coef_mode_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 +util_dist,Distance,@_DIST,0,coef_dist,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged +util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time +util_dist_child_0_5,"Distance,child 0 to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5 +util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12 diff --git a/activitysim/examples/prototype_semcog/configs/school_location.yaml b/activitysim/examples/production_semcog/configs/school_location.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/school_location.yaml rename to activitysim/examples/production_semcog/configs/school_location.yaml diff --git a/activitysim/examples/prototype_semcog/configs/school_location_coeffs.csv b/activitysim/examples/production_semcog/configs/school_location_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/school_location_coeffs.csv rename to activitysim/examples/production_semcog/configs/school_location_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/school_location_sample.csv b/activitysim/examples/production_semcog/configs/school_location_sample.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/school_location_sample.csv rename to activitysim/examples/production_semcog/configs/school_location_sample.csv diff --git a/activitysim/examples/prototype_semcog/configs/settings.yaml b/activitysim/examples/production_semcog/configs/settings.yaml old mode 100755 new mode 100644 similarity index 79% rename from activitysim/examples/prototype_semcog/configs/settings.yaml rename to activitysim/examples/production_semcog/configs/settings.yaml index 2aeff430a..f5c6c2e7e --- a/activitysim/examples/prototype_semcog/configs/settings.yaml +++ b/activitysim/examples/production_semcog/configs/settings.yaml @@ -1,175 +1,158 @@ -# input tables -# -# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) -# as well as for created tables (tours, joint_tour_participants, trips) -# e.g. the households table must have an index column 'household_id' and the foreign key to households in the -# persons table is also household_id. This naming convention allows activitysim to intuit the relationship -# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips -# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by -# household_id. -# -# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id -# you cannot change the well-known name of the index by modifying this directive. However, if your input file -# has a different id column name, you can rename it to the required index name with the rename_columns directive. -# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. -# -# input tables -input_table_list: - # - # households (table index 'household_id') - # - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - persons: hhsize - cars: auto_ownership - zone_id: home_zone_id - recode_columns: - home_zone_id: land_use.zone_id - # - # persons (table index 'person_id') - # - - tablename: persons - filename: persons.csv - index_col: person_id - # - # land_use (table index 'zone_id') - # - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - ZONE: zone_id - recode_columns: - zone_id: zero-based - -# convert input CSVs to HDF5 format and save to outputs directory -# create_input_store: True - -# number of households to simulate -households_sample_size: 100 -# simulate all households -#households_sample_size: 0 - -#hh_ids: override_hh_ids.csv - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - - -# - tracing - -# trace household id; comment out or leave empty for no trace -# households with all tour types -# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] -trace_hh_id: 1311364 - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -# trace_od: [5, 11] -trace_od: - - -models: - - initialize_landuse - - initialize_households - - compute_accessibility - - work_from_home - - add_size_tables - - school_location - - workplace_location - - transit_pass_subsidy - - transit_pass_ownership - - auto_ownership_simulate - - free_parking - - telecommute_frequency - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - - write_data_dictionary - - track_skim_usage - - write_tables - - write_trip_matrices - -# to resume after last successful checkpoint, specify resume_after: _ -resume_after: - -output_tables: - h5_store: False - action: include - prefix: final_ - tables: - - checkpoints - - accessibility - - tablename: land_use - decode_columns: - zone_id: land_use.zone_id - - tablename: households - decode_columns: - home_zone_id: land_use.zone_id - - tablename: persons - decode_columns: - home_zone_id: land_use.zone_id - school_zone_id: nonnegative | land_use.zone_id - workplace_zone_id: nonnegative | land_use.zone_id - - tablename: tours - decode_columns: - origin: land_use.zone_id - destination: land_use.zone_id - - tablename: trips - decode_columns: - origin: land_use.zone_id - destination: land_use.zone_id - - joint_tour_participants - -# area_types less than this are considered urban -urban_threshold: 4 -cbd_threshold: 2 -rural_threshold: 6 - -# - value of time - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - -min_value_of_time: 1 -max_value_of_time: 50 -distributed_vot_mu: 0.684 -distributed_vot_sigma: 0.85 - -household_median_value_of_time: - 1: 6.01 - 2: 8.81 - 3: 10.44 - 4: 12.86 - +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + persons: hhsize + cars: auto_ownership + maz: home_zone_id + taz: TAZ + # + # persons (table index 'person_id') + # + - tablename: persons + filename: persons.csv + index_col: person_id + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + +want_dest_choice_presampling: True +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +# number of households to simulate +households_sample_size: 100 +# simulate all households +#households_sample_size: 0 + +#hh_ids: override_hh_ids.csv + +chunk_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - university_location_zone_override + - workplace_location + - work_from_home + - transit_pass_subsidy + - transit_pass_ownership + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_destination_univ_zone_override + - parking_location_choice_at_university + - trip_scheduling + - stop_frequency_university_parking + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_tables + - write_trip_matrices + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/production_semcog/configs/settings_source.yaml b/activitysim/examples/production_semcog/configs/settings_source.yaml new file mode 100644 index 000000000..a4f28f9ac --- /dev/null +++ b/activitysim/examples/production_semcog/configs/settings_source.yaml @@ -0,0 +1,158 @@ +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: %hh_file% + index_col: household_id + rename_columns: + persons: hhsize + cars: auto_ownership + maz: home_zone_id + taz: TAZ + # + # persons (table index 'person_id') + # + - tablename: persons + filename: %person_file% + index_col: person_id + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: %landuse_file% + index_col: zone_id + rename_columns: + MAZ: zone_id + +want_dest_choice_presampling: True +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +# number of households to simulate +households_sample_size: 100 +# simulate all households +#households_sample_size: 0 + +#hh_ids: override_hh_ids.csv + +chunk_size: 0 + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - university_location_zone_override + - workplace_location + - work_from_home + - transit_pass_subsidy + - transit_pass_ownership + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_destination_univ_zone_override + - parking_location_choice_at_university + - trip_scheduling + - stop_frequency_university_parking + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_tables + - write_trip_matrices + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/prototype_semcog/configs/shadow_pricing.yaml b/activitysim/examples/production_semcog/configs/shadow_pricing.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/shadow_pricing.yaml rename to activitysim/examples/production_semcog/configs/shadow_pricing.yaml diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency.yaml b/activitysim/examples/production_semcog/configs/stop_frequency.yaml old mode 100755 new mode 100644 similarity index 70% rename from activitysim/examples/prototype_semcog/configs/stop_frequency.yaml rename to activitysim/examples/production_semcog/configs/stop_frequency.yaml index 5831f72e7..ad71d2d6e --- a/activitysim/examples/prototype_semcog/configs/stop_frequency.yaml +++ b/activitysim/examples/production_semcog/configs/stop_frequency.yaml @@ -13,34 +13,34 @@ SEGMENT_COL: primary_purpose SPEC_SEGMENTS: - primary_purpose: work SPEC: stop_frequency_work.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_work.csv - primary_purpose: school SPEC: stop_frequency_school.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_school.csv - primary_purpose: univ SPEC: stop_frequency_univ.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_univ.csv - primary_purpose: social SPEC: stop_frequency_social.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_social.csv - primary_purpose: shopping SPEC: stop_frequency_shopping.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_shopping.csv - primary_purpose: eatout SPEC: stop_frequency_eatout.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_eatout.csv - primary_purpose: escort SPEC: stop_frequency_escort.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_escort.csv - primary_purpose: othmaint SPEC: stop_frequency_othmaint.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_othmaint.csv - primary_purpose: othdiscr SPEC: stop_frequency_othdiscr.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv - primary_purpose: atwork SPEC: stop_frequency_atwork.csv - COEFFICIENTS: _dummy_coefficients.csv + COEFFICIENTS: stop_frequency_coefficients_atwork.csv CONSTANTS: TRANSIT_MODES: diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_alternatives.csv b/activitysim/examples/production_semcog/configs/stop_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/stop_frequency_alternatives.csv rename to activitysim/examples/production_semcog/configs/stop_frequency_alternatives.csv diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/production_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/production_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv index 989f3abe8..89cebd7aa --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/production_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -20,6 +20,7 @@ Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= ,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) ,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) ,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +Major University Students as determined by univ location zone override model,is_major_univ_student,df.original_school_zone_id.notna() #,, #num_work_tours already defined,, school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_atwork.csv b/activitysim/examples/production_semcog/configs/stop_frequency_atwork.csv new file mode 100644 index 000000000..68bfdd522 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_atwork.csv @@ -0,0 +1,13 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,income_in_thousands<50000,,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_ +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person,coef_number_of_eating_tours_tours_undertaken_by_the_person +util_subtour_departure_less_than_or_equal_to_11am,Subtour departure less than or equal to 11AM,start<12,,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am,coef_subtour_departure_less_than_or_equal_to_11am +util_subtour_return_time_greater_or_equal_to_2pm,Subtour return time greater or equal to 2PM,end>16,,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm,coef_subtour_return_time_greater_or_equal_to_2pm +util_subtour_duration_in_hours_integer_,Subtour duration in hours (integer),end-start,,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_,coef_subtour_duration_in_hours_integer_ +util_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,dummy for subtour origin (tour destination) at Exurban or Rual (AreaTypes = 6 or 7),destination_area_type >5,,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_ +util_primary_destination_accessibility_log_of_it_,Primary Destination Accessibility (LOG of it),pracc,,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_,coef_primary_destination_accessibility_log_of_it_ +util_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,subtour distance in miles (from tour destination to subtour primary destination one way),distance_in_miles,,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +util_Phase1_Calibration,Phase1_Calibration,1,,-0.214873516,-0.08829587,-0.021076701,-0.583908707,1.759594513,1.768617768,0.015908847,-0.496833561,0.742087614,0.721158833,-0.038026423,1.210255745,0.858858431,2.537970801,3.355148354 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_atwork.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_atwork.csv new file mode 100644 index 000000000..09cd0fd29 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_atwork.csv @@ -0,0 +1,17 @@ +Description,value,coefficient_name +Middle to Low Income HH ,0.45,coef_middle_to_low_income_hh_ +Number of eating tours tours undertaken by the person,-0.28,coef_number_of_eating_tours_tours_undertaken_by_the_person +Subtour departure less than or equal to 11AM,0.31,coef_subtour_departure_less_than_or_equal_to_11am +Subtour return time greater or equal to 2PM,0.34,coef_subtour_return_time_greater_or_equal_to_2pm +Subtour duration in hours (integer),0.56,coef_subtour_duration_in_hours_integer_ +dummy for subtour origin (tour destination) at Exurban or Rual (AreaTypes = 6 or 7),0.27,coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_ +Primary Destination Accessibility (LOG of it),0.18,coef_primary_destination_accessibility_log_of_it_ +subtour distance in miles (from tour destination to subtour primary destination one way),0.02,coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ +Alternative specific constant for return stops,-3.6710000000000003,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops,-5.388,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-6.21,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-3.8960000000000004,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for the total number of stops,2.127,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for outbound stops,-5.709,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops,-7.361000000000001,coef_alternative_specific_constant_for_outbound_stops_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_eatout.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_eatout.csv new file mode 100644 index 000000000..a8cd76292 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_eatout.csv @@ -0,0 +1,33 @@ +Description,value,coefficient_name +Number of Vehicles,-0.19,coef_number_of_vehicles +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.73,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.28,coef_number_of_work_tours_undertaken_by_the_person +Number of shop tours undertaken by the person,-0.24,coef_number_of_shop_tours_undertaken_by_the_person +At least one kid and one adult participate in the tour,0.37,coef_at_least_one_kid_and_one_adult_participate_in_the_tour +Arrival later than 17:00.,-0.45,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,1.31,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ +dummy for distance in miles,-0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-1.761,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Dummy for a return visiting tour,-0.64,coef_dummy_for_a_return_visiting_tour +Alternative specific constant for return stops,-3.697,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-4.717,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Number of persons participating in the tour.Outgoing stops interaction,-0.46,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction +Alternative specific constant for outbound stops,-2.19,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Dummy for an outbound visiting tour,-0.69,coef_dummy_for_an_outbound_visiting_tour +Dummy for a visiting tour with both outbound and return leg,0.44,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg +Alternative specific constant for the total number of stops,0.94,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-4.516,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,2.026,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-5.255,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_escort.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_escort.csv new file mode 100644 index 000000000..e78dcf2a2 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_escort.csv @@ -0,0 +1,28 @@ +Description,value,coefficient_name +Number of HH Persons,-0.24,coef_number_of_hh_persons +Number of Students in HH,0.19,coef_number_of_students_in_hh +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.91,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.29,coef_number_of_work_tours_undertaken_by_the_person +Number of escort tours tours undertaken by the person,-0.15,coef_number_of_escort_tours_tours_undertaken_by_the_person +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.59,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance less than 5 Miles,0.32,coef_dummy_for_distance_less_than_5_miles +dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.968,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Alternative specific constant for return stops,-2.41,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-3.0239999999999996,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Alternative specific constant for outbound stops,-2.173,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-4.294,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,-1.807,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-4.758,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othdiscr.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othdiscr.csv new file mode 100644 index 000000000..ccc3d25eb --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othdiscr.csv @@ -0,0 +1,30 @@ +Description,value,coefficient_name +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-2.4578,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.6153,coef_number_of_work_tours_undertaken_by_the_person +Number of shool tours tours undertaken by the person,-0.8176,coef_number_of_shool_tours_tours_undertaken_by_the_person +Number of shop tours undertaken by the person,-0.629,coef_number_of_shop_tours_undertaken_by_the_person +Number of maintenace tours tours undertaken by the person,-0.3715,coef_number_of_maintenace_tours_tours_undertaken_by_the_person +Arrival later than 17:00.,-0.6383,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.8335,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance less than 10 Miles ,0.3756,coef_dummy_for_distance_less_than_10_miles_ +dummy for distance in miles,-0.0225,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.9209999999999999,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Alternative specific constant for return stops,-2.336,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-2.927,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Alternative specific constant for outbound stops,-1.581,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Alternative specific constant for the total number of stops,0.863,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-3.323,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,0.9390000000000001,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-4.623,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othmaint.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othmaint.csv new file mode 100644 index 000000000..1ef7c5e24 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_othmaint.csv @@ -0,0 +1,38 @@ +Description,value,coefficient_name +Middle to Low Income HH ,0.17,coef_middle_to_low_income_hh_ +Mid to High Income HH,0.23,coef_mid_to_high_income_hh +High Income HH,0.24,coef_high_income_hh +Number of HH Persons,-0.31,coef_number_of_hh_persons +Number of Students in HH,0.21,coef_number_of_students_in_hh +Presence of Kids between 0 and 4 (including) years old,0.74,coef_presence_of_kids_between_0_and_4_including_years_old +Dummy for female,0.3012,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.4329,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.364,coef_number_of_work_tours_undertaken_by_the_person +Number of university tours tours undertaken by the person,-0.6252,coef_number_of_university_tours_tours_undertaken_by_the_person +Number of shool tours tours undertaken by the person,-1.4135,coef_number_of_shool_tours_tours_undertaken_by_the_person +Number of shop tours undertaken by the person,-0.1428,coef_number_of_shop_tours_undertaken_by_the_person +Number of maintenace tours undertaken by the houshold,-0.0468,coef_number_of_maintenace_tours_undertaken_by_the_houshold +Number of persons participating in the tour.Return stops interaction,0.4904,coef_number_of_persons_participating_in_the_tour_return_stops_interaction +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.5134,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance less than 20 Miles ,-0.408,coef_dummy_for_distance_less_than_20_miles_ +dummy for distance in miles,0.0273,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.585,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Alternative specific constant for return stops,-1.48,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-2.4619999999999997,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Alternative specific constant for outbound stops,-1.761,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Alternative specific constant for the total number of stops,0.414,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-3.661,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,0.488,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-5.426,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_school.csv new file mode 100644 index 000000000..9eab1b104 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_school.csv @@ -0,0 +1,21 @@ +Description,value,coefficient_name +Number of HH Persons,-0.506,coef_number_of_hh_persons +Presence of kids between 5 and 15 (including) years old,0.3299,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Cars > Number of Workers,0.5331,coef_number_of_cars_number_of_workers +Dummy for female,0.4099,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.8163,coef_dummy_for_walking_to_all_stops +Number of escort tours tours undertaken by the person,1.2365,coef_number_of_escort_tours_tours_undertaken_by_the_person +Arrival later than 17:00.,1.8377,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.9549,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance in miles,0.0438,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-1.206,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops,-2.6719999999999997,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-3.364,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-2.123,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for the total number of stops,0.701,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for outbound stops,-3.798,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,1.135,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-5.85,coef_alternative_specific_constant_for_outbound_stops_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_shopping.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_shopping.csv new file mode 100644 index 000000000..62ce58d76 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_shopping.csv @@ -0,0 +1,33 @@ +Description,value,coefficient_name +Number of HH Persons,-0.1522,coef_number_of_hh_persons +Num kids between 5 and 15 (including) years old,0.0482,coef_num_kids_between_5_and_15_including_years_old +Dummy for female,0.1721,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.4908,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.5479999999999999,coef_number_of_work_tours_undertaken_by_the_person +Number of university tours tours undertaken by the person,-0.6709,coef_number_of_university_tours_tours_undertaken_by_the_person +Number of maintenace tours tours undertaken by the person,-0.1977,coef_number_of_maintenace_tours_tours_undertaken_by_the_person +Number of shop tours undertaken by the houshold,-0.0733,coef_number_of_shop_tours_undertaken_by_the_houshold +Dummy for only adults participate in the tour,0.1902,coef_dummy_for_only_adults_participate_in_the_tour +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.9056,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +dummy for distance less than 5 Miles,0.3768,coef_dummy_for_distance_less_than_5_miles +dummy for distance in miles,0.0289,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-1.179,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Alternative specific constant for return stops,-2.305,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-3.0239999999999996,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Alternative specific constant for outbound stops,-1.339,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Alternative specific constant for the total number of stops,0.252,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-3.11,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,0.514,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-4.487,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_social.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_social.csv new file mode 100644 index 000000000..ea05f6d1f --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_social.csv @@ -0,0 +1,33 @@ +Description,value,coefficient_name +Number of Vehicles,-0.19,coef_number_of_vehicles +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.73,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.28,coef_number_of_work_tours_undertaken_by_the_person +Number of shop tours undertaken by the person,-0.24,coef_number_of_shop_tours_undertaken_by_the_person +At least one kid and one adult participate in the tour,0.37,coef_at_least_one_kid_and_one_adult_participate_in_the_tour +Arrival later than 17:00.,-0.45,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,1.31,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ +dummy for distance in miles,-0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-1.12,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops on joint tours,-1.329,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in +Dummy for a return visiting tour,-0.64,coef_dummy_for_a_return_visiting_tour +Alternative specific constant for return stops,-2.764,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops on joint tours,-2.7960000000000003,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in +Alternative specific constant for the total number of stops on joint tours,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in +Alternative specific constant for return stops,-3.451,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for return stops on joint tours,-3.3789999999999996,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +Number of persons participating in the tour.Outgoing stops interaction,-0.46,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction +Alternative specific constant for outbound stops,-1.081,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops on joint tours,-1.7830000000000001,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in +Dummy for an outbound visiting tour,-0.69,coef_dummy_for_an_outbound_visiting_tour +Dummy for a visiting tour with both outbound and return leg,0.44,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg +Alternative specific constant for the total number of stops,0.496,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for the total number of stops on joint tours,0.518,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in +Alternative specific constant for outbound stops,-2.8739999999999997,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for outbound stops on joint tours,-4.067,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in +Alternative specific constant for the total number of stops,0.882,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for the total number of stops on joint tours,1.4969999999999999,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +Alternative specific constant for outbound stops,-4.552,coef_alternative_specific_constant_for_outbound_stops_3out_0in +Alternative specific constant for outbound stops on joint tours,-4.998,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_univ.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_univ.csv new file mode 100644 index 000000000..cfdbbce41 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_univ.csv @@ -0,0 +1,20 @@ +Description,value,coefficient_name +Number of HH Persons,-0.2827,coef_number_of_hh_persons +Presence of kids between 5 and 15 (including) years old,0.6823,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Vehicles,0.1703,coef_number_of_vehicles +Dummy for female,0.7349,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Number of escort tours tours undertaken by the person,0.9018,coef_number_of_escort_tours_tours_undertaken_by_the_person +Arrival later than 17:00.,0.389,coef_arrival_later_than_17_00_ +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,0.8434,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +HH accesibility for inbound tours. Interaction,0.2481,coef_hh_accesibility_for_inbound_tours_interaction +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-2.003,coef_alternative_specific_constant_for_return_stops_0out_1in +Alternative specific constant for return stops,-3.51,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-3.677,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-2.628,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for the total number of stops,1.272,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in +Alternative specific constant for outbound stops,-3.741,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,1.871,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-4.981,coef_alternative_specific_constant_for_outbound_stops_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_work.csv new file mode 100644 index 000000000..dd3ebe7d1 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_coefficients_work.csv @@ -0,0 +1,34 @@ +Description,value,coefficient_name +Middle to Low Income HH,0.17,coef_middle_to_low_income_hh +Mid to High Income HH,0.23,coef_mid_to_high_income_hh +High Income HH,0.24,coef_high_income_hh +Number of HH Persons,-0.31,coef_number_of_hh_persons +Number of Students in HH,0.21,coef_number_of_students_in_hh +Presence of Kids between 0 and 4 (including) years old,0.74,coef_presence_of_kids_between_0_and_4_including_years_old +Num kids between 5 and 15 (including) years old,0.08,coef_num_kids_between_5_and_15_including_years_old +Presence of kids between 5 and 15 (including) years old,0.26,coef_presence_of_kids_between_5_and_15_including_years_old +Number of Adults (>= 16 years old),0.03,coef_number_of_adults_16_years_old_ +Number of Cars > Number of Workers,0.16,coef_number_of_cars_number_of_workers +Dummy for female,0.22,coef_dummy_for_female +Dummy for all stops made by transit,-0.7,coef_dummy_for_all_stops_made_by_transit +Dummy for walking to all stops,-1.54,coef_dummy_for_walking_to_all_stops +Number of work tours undertaken by the person,-0.15,coef_number_of_work_tours_undertaken_by_the_person +Number of university tours tours undertaken by the person,-0.48,coef_number_of_university_tours_tours_undertaken_by_the_person +Number of school tours tours undertaken by the person,-1.55,coef_number_of_school_tours_tours_undertaken_by_the_person +Number of escort tours tours undertaken by the person,0.2,coef_number_of_escort_tours_tours_undertaken_by_the_person +Number of shop tours undertaken by the houshold,-0.05,coef_number_of_shop_tours_undertaken_by_the_houshold +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,-1.93,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours +Evening Arrival (>=19:00) Interacted with return tours,0.31,coef_evening_arrival_19_00_interacted_with_return_tours +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,0.6,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours +dummy for distance less than 20 Miles,-0.22,coef_dummy_for_distance_less_than_20_miles +dummy for distance in miles,0.01,coef_dummy_for_distance_in_miles +No stops if tour mode is driveTransit,-999.0,coef_no_stops_if_tour_mode_is_drivetransit +Alternative specific constant for return stops,-0.445,coef_alternative_specific_constant_for_return_stops_0out_1in +Number of subtours in the tour,0.19,coef_number_of_subtours_in_the_tour +Alternative specific constant for return stops,-1.775,coef_alternative_specific_constant_for_return_stops_0out_2in +Alternative specific constant for the total number of stops,0.0,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in +Alternative specific constant for return stops,-2.1390000000000002,coef_alternative_specific_constant_for_return_stops_0out_3in +Alternative specific constant for outbound stops,-0.833,coef_alternative_specific_constant_for_outbound_stops_1out_0in +Alternative specific constant for outbound stops,-2.613,coef_alternative_specific_constant_for_outbound_stops_2out_0in +Alternative specific constant for the total number of stops,0.695,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +Alternative specific constant for outbound stops,-3.9339999999999997,coef_alternative_specific_constant_for_outbound_stops_3out_0in diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_eatout.csv b/activitysim/examples/production_semcog/configs/stop_frequency_eatout.csv new file mode 100644 index 000000000..70be2a12d --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_eatout.csv @@ -0,0 +1,53 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles +util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_at_least_one_kid_and_one_adult_participate_in_the_tour,At least one kid and one adult participate in the tour,composition=='mixed',,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_dummy_for_an_outbound_visiting_tour,Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour +util_dummy_for_a_return_visiting_tour,Dummy for a return visiting tour,primary_purpose == 'social',,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour +util_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,0.402777606,0.769044419,1.547375234,0.17724742,1.523737838,1.704085866,1.219450496,1.50935493,2.583402672,2.411844468,0.759903236,1.610165394,2.324582352,2.016696053,2.783482815 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_escort.csv b/activitysim/examples/production_semcog/configs/stop_frequency_escort.csv new file mode 100644 index 000000000..629b1ea1a --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_escort.csv @@ -0,0 +1,47 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_5_miles,dummy for distance less than 5 Miles,(distance_in_miles < 5),,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,-0.281471428,-0.016095822,0.633091127,0.54790928,0.034142435,0.648536014,1.179253849,1.588769694,1.014803541,1.362137736,4.47587972,1.689728522,1.349848675,3.365045965,4.461548606 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_othdiscr.csv b/activitysim/examples/production_semcog/configs/stop_frequency_othdiscr.csv new file mode 100644 index 000000000..bf6b6f281 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_othdiscr.csv @@ -0,0 +1,49 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_10_miles_,dummy for distance less than 10 Miles ,(distance_in_miles < 10),,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_,coef_dummy_for_distance_less_than_10_miles_ +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,0.187263987,0.670000983,0.992092361,0.283823906,0.461537002,0.958158373,-0.156485013,0.806611419,0.872137091,0.343003767,0.7657564,2.063744949,1.13525473,1.376716611,2.113754844 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_othmaint.csv b/activitysim/examples/production_semcog/configs/stop_frequency_othmaint.csv new file mode 100644 index 000000000..2c8397388 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_othmaint.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_,coef_middle_to_low_income_hh_ +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old +util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,~is_joint & female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person +util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person,coef_number_of_shool_tours_tours_undertaken_by_the_person +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_undertaken_by_the_houshold,Number of maintenace tours undertaken by the houshold,num_hh_maint_tours,,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold,coef_number_of_maintenace_tours_undertaken_by_the_houshold +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction,coef_number_of_persons_participating_in_the_tour_return_stops_interaction +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_,coef_dummy_for_distance_less_than_20_miles_ +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,0.475190752,0.530588025,1.389497026,0.960593185,0.775245847,1.122815367,1.226361438,1.683900584,1.594911845,1.101961687,2.302278146,3.35277309,2.734475828,2.746850954,3.93916549 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_school.csv b/activitysim/examples/production_semcog/configs/stop_frequency_school.csv new file mode 100644 index 000000000..1511de25e --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_school.csv @@ -0,0 +1,45 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_15_miles,dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,-2.672,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_Phase1_Calibration,Phase1_Calibration,1,,-0.462185603,0.034598859,0.143710811,-0.107820253,0.247416913,0.525399532,0.065742876,-0.264881606,-0.844194184,-0.191651123,-0.295391166,1.390171934,-1.256414914,0.034312538,0.662067578 +#,,,,,,,,,,,,,,,,,, diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_shopping.csv b/activitysim/examples/production_semcog/configs/stop_frequency_shopping.csv new file mode 100644 index 000000000..48944217d --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_shopping.csv @@ -0,0 +1,50 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,~is_joint & female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person,coef_number_of_maintenace_tours_tours_undertaken_by_the_person +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_dummy_for_only_adults_participate_in_the_tour,Dummy for only adults participate in the tour,composition=='adults',,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour,coef_dummy_for_only_adults_participate_in_the_tour +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_5_miles,dummy for distance less than 5 Miles,(distance_in_miles < 5),,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles,coef_dummy_for_distance_less_than_5_miles +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,0.061908526,0.211807872,0.269959028,0.159052037,0.386325269,0.209133272,0.563685874,1.002319475,1.182476858,1.537241286,1.004078003,2.038636069,1.69019331,2.060858768,2.809711477 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_social.csv b/activitysim/examples/production_semcog/configs/stop_frequency_social.csv new file mode 100644 index 000000000..35d3b41fc --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_social.csv @@ -0,0 +1,53 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh_,Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +util_number_of_full_time_workes_in_hh,Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_4_and_15_including_years_old,Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles +util_dummy_for_female,Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,~is_joint * num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_shool_tours_tours_undertaken_by_the_person,Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person,coef_number_of_shop_tours_undertaken_by_the_person +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction +util_number_of_persons_participating_in_the_tour_return_stops_interaction,Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +util_at_least_one_kid_and_one_adult_participate_in_the_tour,At least one kid and one adult participate in the tour,composition=='mixed',,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour,coef_at_least_one_kid_and_one_adult_participate_in_the_tour +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles_,dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,~is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,~is_joint,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,~is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_alternative_specific_constant_for_outbound_stops_on_joint_tours,Alternative specific constant for outbound stops on joint tours,is_joint,,,,,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in +util_alternative_specific_constant_for_return_stops_on_joint_tours,Alternative specific constant for return stops on joint tours,is_joint,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours,Alternative specific constant for the total number of stops on joint tours,is_joint,,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in +util_dummy_for_an_outbound_visiting_tour,Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour,coef_dummy_for_an_outbound_visiting_tour +util_dummy_for_a_return_visiting_tour,Dummy for a return visiting tour,primary_purpose == 'social',,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour,coef_dummy_for_a_return_visiting_tour +util_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg +util_Phase1_Calibration,Phase1_Calibration,~is_joint,,0.38246443,0.839705649,1.208071759,0.577945215,0.470848758,0.683396345,0.561543342,1.335624596,0.972779572,1.949517699,0.318404726,3.210420697,0.539522475,0.864775287,3.503722118 diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_univ.csv b/activitysim/examples/production_semcog/configs/stop_frequency_univ.csv new file mode 100644 index 000000000..c059baa07 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_univ.csv @@ -0,0 +1,46 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,,,,,,,,,,,,,,, +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +util_number_of_vehicles,Number of Vehicles,auto_ownership,,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles,coef_number_of_vehicles +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,, +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>5) & (start<8),,,,,,,,,,,,,,,, +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 16),,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_,coef_arrival_later_than_17_00_ +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 18),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 10),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 8),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 2),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction,coef_hh_accesibility_for_inbound_tours_interaction +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,, +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,, +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_Phase1_Calibration,Phase1_Calibration,1,,0.420011539,0.781344394,0.733748807,0.712121201,1.545847839,1.973538669,0.675841505,0.652388889,0.802044327,0.807649045,0.537484229,1.764449963,1.356930627,0.668833391,1.667362221 +util_Major University Calibration,Major University Calibration,is_major_univ_student,,-0.199248949,0.652388897,0.625258509,-1.850257358,-1.049976118,-0.922350027,0.617946267,-1.566065939,-0.401448626,-0.508496898,-0.026438159,-3.878745091,-5,0.876560807,-1.094489763 +#,,,,,,,,,,,,,,,,,, diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_university_parking.yaml b/activitysim/examples/production_semcog/configs/stop_frequency_university_parking.yaml new file mode 100644 index 000000000..0cf1111c6 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_university_parking.yaml @@ -0,0 +1,3 @@ + +# name of parking purpose +PARKING_TRIP_NAME: parking diff --git a/activitysim/examples/production_semcog/configs/stop_frequency_work.csv b/activitysim/examples/production_semcog/configs/stop_frequency_work.csv new file mode 100644 index 000000000..e4e03d7f4 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/stop_frequency_work.csv @@ -0,0 +1,45 @@ +Label,Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +util_middle_to_low_income_hh,Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh,coef_middle_to_low_income_hh +util_mid_to_high_income_hh,Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh,coef_mid_to_high_income_hh +util_high_income_hh,High Income HH,(income_in_thousands>=100000),,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh,coef_high_income_hh +util_number_of_hh_persons,Number of HH Persons,hhsize,,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons,coef_number_of_hh_persons +util_number_of_full_time_workers_in_hh,Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +util_number_of_students_in_hh,Number of Students in HH,num_student,,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh,coef_number_of_students_in_hh +util_num_kids_between_0_and_4_including_years_old,Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +util_presence_of_kids_between_0_and_4_including_years_old,Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old,coef_presence_of_kids_between_0_and_4_including_years_old +util_num_kids_between_5_and_15_including_years_old,Num kids between 5 and 15 (including) years old,num_age_5_15,,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old,coef_num_kids_between_5_and_15_including_years_old +util_presence_of_kids_between_5_and_15_including_years_old,Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old,coef_presence_of_kids_between_5_and_15_including_years_old +util_number_of_adults_16_years_old_,Number of Adults (>= 16 years old),num_adult,,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_,coef_number_of_adults_16_years_old_ +util_dummy_for_single_parent_household,Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +util_number_of_cars_number_of_workers,Number of Cars > Number of Workers,more_cars_than_workers,,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers,coef_number_of_cars_number_of_workers +util_number_of_vehicles,Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +util_dummy_for_female,Dummy for female,female,,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female,coef_dummy_for_female +util_dummy_for_all_stops_made_by_transit,Dummy for all stops made by transit,tour_mode_is_transit,,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit,coef_dummy_for_all_stops_made_by_transit +util_dummy_for_walking_to_all_stops,Dummy for walking to all stops,tour_mode_is_non_motorized,,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops,coef_dummy_for_walking_to_all_stops +util_number_of_work_tours_undertaken_by_the_person,Number of work tours undertaken by the person,num_work_tours,,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person,coef_number_of_work_tours_undertaken_by_the_person +util_number_of_university_tours_tours_undertaken_by_the_person,Number of university tours tours undertaken by the person,num_univ_tours,,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person,coef_number_of_university_tours_tours_undertaken_by_the_person +util_number_of_school_tours_tours_undertaken_by_the_person,Number of school tours tours undertaken by the person,num_school_tours,,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person,coef_number_of_school_tours_tours_undertaken_by_the_person +util_number_of_escort_tours_tours_undertaken_by_the_person,Number of escort tours tours undertaken by the person,num_escort_tours,,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person,coef_number_of_escort_tours_tours_undertaken_by_the_person +util_number_of_shop_tours_undertaken_by_the_person,Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +util_number_of_maintenace_tours_tours_undertaken_by_the_person,Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +util_number_of_eating_tours_tours_undertaken_by_the_person,Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +util_number_of_visit_tours_tours_undertaken_by_the_person,Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +util_number_of_shop_tours_undertaken_by_the_houshold,Number of shop tours undertaken by the houshold,num_hh_shop_tours,,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold,coef_number_of_shop_tours_undertaken_by_the_houshold +util_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours +util_arrival_later_than_17_00_,Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, +util_evening_arrival_19_00_interacted_with_return_tours,Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours,coef_evening_arrival_19_00_interacted_with_return_tours +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, +util_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +util_hh_accesibility_for_outbound_tours_interaction,HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_hh_accesibility_for_inbound_tours_interaction,HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_outbound_tours_interaction,Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_primary_destination_accessibility_for_return_tours_interaction,Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +util_dummy_for_distance_less_than_20_miles,dummy for distance less than 20 Miles,(distance_in_miles < 20),,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles,coef_dummy_for_distance_less_than_20_miles +util_dummy_for_distance_in_miles,dummy for distance in miles,distance_in_miles,,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles,coef_dummy_for_distance_in_miles +util_no_stops_if_tour_mode_is_drivetransit,No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit,coef_no_stops_if_tour_mode_is_drivetransit +util_alternative_specific_constant_for_outbound_stops,Alternative specific constant for outbound stops,1,,,,,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_1out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_2out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in,coef_alternative_specific_constant_for_outbound_stops_3out_0in +util_alternative_specific_constant_for_return_stops,Alternative specific constant for return stops,1,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in,,coef_alternative_specific_constant_for_return_stops_0out_1in,coef_alternative_specific_constant_for_return_stops_0out_2in,coef_alternative_specific_constant_for_return_stops_0out_3in +util_alternative_specific_constant_for_the_total_number_of_stops,Alternative specific constant for the total number of stops,1,,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in +util_number_of_subtours_in_the_tour,Number of subtours in the tour,num_atwork_subtours,,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour,coef_number_of_subtours_in_the_tour +util_Phase1_Calibration,Phase1_Calibration,1,,0.223196473,0.325510562,0.203854333,-0.303375802,-0.391244072,-0.093964049,-0.096568307,-0.097190886,-0.494486563,0.334444913,-0.145593481,0.40493641,-0.229067675,0.390561162,0.076806162 diff --git a/activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv b/activitysim/examples/production_semcog/configs/telecommute_frequency.csv old mode 100755 new mode 100644 similarity index 69% rename from activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv rename to activitysim/examples/production_semcog/configs/telecommute_frequency.csv index 265be1b50..5fbe09a99 --- a/activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv +++ b/activitysim/examples/production_semcog/configs/telecommute_frequency.csv @@ -1,8 +1,8 @@ Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week -#util_Services,Services,@df.occup==52,0,coef_Services_1day,coef_Services_23day,coef_Services_4day -#util_SalesOffice,SalesOffice,@df.occup==53,0,coef_SalesOffice_1day,coef_SalesOffice_23day,coef_SalesOffice_4day -#util_ResourceConstruct,ResourceConstruct,@df.occup==54,0,coef_ResourceConstruct_1day,coef_ResourceConstruct_23day,coef_ResourceConstruct_4day -#util_TransportMat,TransportMat,@df.occup==55,0,coef_TransportMat_1day,coef_TransportMat_23day,coef_TransportMat_4day +util_Services,Services,"@df.naics_category.isin(['56','61','71','72','81'])",0,coef_Services_1day,coef_Services_23day,coef_Services_4day +util_Sales,SalesOffice,"@df.naics_category.isin(['44', '45'])",0,coef_SalesOffice_1day,coef_SalesOffice_23day,coef_SalesOffice_4day +util_Construct,ResourceConstruct,@df.naics_category == '23',0,coef_ResourceConstruct_1day,coef_ResourceConstruct_23day,coef_ResourceConstruct_4day +util_Transport,TransportMat,"@df.naics_category.isin(['48', '49'])",0,coef_TransportMat_1day,coef_TransportMat_23day,coef_TransportMat_4day util_HasChildren0to5,Has children 0 to 5 years old,@df.has_young_children,0,coef_HasChildren0to5_1day,coef_HasChildren0to5_23day,coef_HasChildren0to5_4day util_HasChildren6to12,Has children 6 to 12 years old,@df.has_children_6_to_12,0,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day util_OneAdultInHH,One adult in hh,@df.num_adults==1,0,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day @@ -10,7 +10,7 @@ util_2plusAdultsInHH,2 or more adults in hh,@df.num_adults==2,0,coef_2plusAdults util_Female,female,@df.sex==2,0,coef_Female_1day,coef_Female_23day,coef_Female_4day util_PartTimeWorker,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_PartTimeWorker_1day,coef_PartTimeWorker_23day,coef_PartTimeWorker_4day util_CollegeStudent,College student,@df.ptype==PTYPE_UNIVERSITY,0,coef_CollegeStudent_1day,coef_CollegeStudent_23day,coef_CollegeStudent_4day -util_PaysToPark,Pays to park,@~df.free_parking_at_work,0,coef_PaysToPark_1day,coef_PaysToPark_23day,coef_PaysToPark_4day +#util_PaysToPark,Pays to park,@~df.free_parking_at_work,0,coef_PaysToPark_1day,coef_PaysToPark_23day,coef_PaysToPark_4day util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",0,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",0,coef_Income100to150k_1day,coef_Income100to150k_23day,coef_Income100to150k_4day util_Income150kplus,Income 150k+,@df.income > 150000,0,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day @@ -18,4 +18,4 @@ util_0Autos,0 Autos,@df.auto_ownership==0,0,coef_0Autos_1day,coef_0Autos_23day,c util_1Auto,1 Auto,@df.auto_ownership==1,0,coef_1Auto_1day,coef_1Auto_23day,coef_1Auto_4day util_3plusAutos,3+ Autos,@df.auto_ownership>=3,0,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day util_DistanceToWork,Distance to work,@df.distance_to_work,0,coef_DistanceToWork_1day,coef_DistanceToWork_23day,coef_DistanceToWork_4day -util_temp,temp_calibration,1,0.00,-4.00,-4.25,-6.00 +util_calibration_ASC,Calibration constant,1,coef_calib_No_Telecommute_asc,coef_calib_1_day_week_asc,coef_calib_2_3_days_week_asc,coef_calib_4_days_week_asc diff --git a/activitysim/examples/prototype_semcog/configs/telecommute_frequency.yaml b/activitysim/examples/production_semcog/configs/telecommute_frequency.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/telecommute_frequency.yaml rename to activitysim/examples/production_semcog/configs/telecommute_frequency.yaml diff --git a/activitysim/examples/production_semcog/configs/telecommute_frequency_coeffs.csv b/activitysim/examples/production_semcog/configs/telecommute_frequency_coeffs.csv new file mode 100644 index 000000000..c74e32bae --- /dev/null +++ b/activitysim/examples/production_semcog/configs/telecommute_frequency_coeffs.csv @@ -0,0 +1,62 @@ +coefficient_name,value,constrain +coef_Services_1day,-1.624,F +coef_SalesOffice_1day,-0.62,F +coef_ResourceConstruct_1day,-1.57,F +coef_TransportMat_1day,-14.747,F +coef_HasChildren0to5_1day,0,F +coef_HasChildren6to12_1day,0,F +coef_OneAdultInHH_1day,0.177,F +coef_2plusAdultsInHH_1day,0,F +coef_Female_1day,0,F +coef_PartTimeWorker_1day,0,F +coef_CollegeStudent_1day,0,F +coef_PaysToPark_1day,0.457,F +coef_Income60to100k_1day,0.56,F +coef_Income100to150k_1day,0.644,F +coef_Income150kplus_1day,0.92,F +coef_0Autos_1day,0,F +coef_1Auto_1day,0,F +coef_3plusAutos_1day,0,F +coef_DistanceToWork_1day,0.016,F +coef_Services_23day,-0.651,F +coef_SalesOffice_23day,-0.738,F +coef_ResourceConstruct_23day,0,F +coef_TransportMat_23day,0,F +coef_HasChildren0to5_23day,0,F +coef_HasChildren6to12_23day,0.517,F +coef_OneAdultInHH_23day,0,F +coef_2plusAdultsInHH_23day,0,F +coef_Female_23day,0,F +coef_PartTimeWorker_23day,0.425,F +coef_CollegeStudent_23day,0.6,F +coef_PaysToPark_23day,0,F +coef_Income60to100k_23day,0.389,F +coef_Income100to150k_23day,0.193,F +coef_Income150kplus_23day,0.765,F +coef_0Autos_23day,0.407,F +coef_1Auto_23day,0,F +coef_3plusAutos_23day,-0.73,F +coef_DistanceToWork_23day,0,F +coef_Services_4day,0,F +coef_SalesOffice_4day,-0.894,F +coef_ResourceConstruct_4day,0,F +coef_TransportMat_4day,0,F +coef_HasChildren0to5_4day,-0.864,F +coef_HasChildren6to12_4day,-0.81,F +coef_OneAdultInHH_4day,-0.043,F +coef_2plusAdultsInHH_4day,0,F +coef_Female_4day,0,F +coef_PartTimeWorker_4day,1.112,F +coef_CollegeStudent_4day,0,F +coef_PaysToPark_4day,0,F +coef_Income60to100k_4day,0,F +coef_Income100to150k_4day,0,F +coef_Income150kplus_4day,0,F +coef_0Autos_4day,0,F +coef_1Auto_4day,0,F +coef_3plusAutos_4day,0,F +coef_DistanceToWork_4day,0,F +coef_calib_No_Telecommute_asc,-0.013380448,F +coef_calib_1_day_week_asc,-3.5028756,F +coef_calib_2_3_days_week_asc,-4.205827551,F +coef_calib_4_days_week_asc,-6.218810634,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/production_semcog/configs/tour_departure_and_duration_alternatives.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/production_semcog/configs/tour_departure_and_duration_alternatives.csv diff --git a/activitysim/examples/production_semcog/configs/tour_departure_and_duration_segments.csv b/activitysim/examples/production_semcog/configs/tour_departure_and_duration_segments.csv new file mode 100644 index 000000000..03bb92869 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_departure_and_duration_segments.csv @@ -0,0 +1,60 @@ +tour_purpose,time_period,start,end +work,EA,1,6 +work,AM,9,10 +work,MD,13,24 +work,PM,25,27 +work,EV,33,35 +#,,, +school,EA,1,6 +school,AM,9,10 +school,MD,13,24 +school,PM,25,27 +school,EV,33,35 +#,,, +univ,EA,1,6 +univ,AM,9,10 +univ,MD,13,24 +univ,PM,25,27 +univ,EV,33,35 +#,,, +shopping,EA,1,6 +shopping,AM,9,10 +shopping,MD,13,24 +shopping,PM,25,27 +shopping,EV,33,35 +#,,, +escort,EA,1,6 +escort,AM,9,10 +escort,MD,13,24 +escort,PM,25,27 +escort,EV,33,35 +#,,, +othmaint,EA,1,6 +othmaint,AM,9,10 +othmaint,MD,13,24 +othmaint,PM,25,27 +othmaint,EV,33,35 +#,,, +othdiscr,EA,1,6 +othdiscr,AM,9,10 +othdiscr,MD,13,24 +othdiscr,PM,25,27 +othdiscr,EV,33,35 +#,,, +social,EA,1,6 +social,AM,9,10 +social,MD,13,24 +social,PM,25,27 +social,EV,33,35 +#,,, +eatout,EA,1,6 +eatout,AM,9,10 +eatout,MD,13,24 +eatout,PM,25,27 +eatout,EV,33,35 +#,,, +atwork,EA,1,6 +atwork,AM,9,10 +atwork,MD,13,24 +atwork,PM,25,27 +atwork,EV,33,35 diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice.csv old mode 100755 new mode 100644 similarity index 82% rename from activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv rename to activitysim/examples/production_semcog/configs/tour_mode_choice.csv index dfeb0a526..0ec02a337 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice.csv @@ -5,29 +5,26 @@ util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable fo util_DRIVEALONE_Unavailable_forJoint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,0,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,,,,,,, util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,, util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, -#util_DRIVEALONE_Bridge_toll_,DRIVEALONE - Bridge toll ,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),0,,,,,,,,,,,,,,,,, util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,, #Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,0,,,,,,,,,,,,,,,, +util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,,,,,,, util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,coef_ivt,,,,,,,,,,,,,,,, util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, -#util_SHARED2_Bridge_toll,SHARED2 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,0,,,,,,,,,,,,,,,, util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, #Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,, util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,0,,,,,,,,,,,,,,, +util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,,,,,,, util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,coef_ivt,,,,,,,,,,,,,,, util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, -#util_SHARED3_Bridge_toll,SHARED3 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,0,,,,,,,,,,,,,,, util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, @@ -36,14 +33,12 @@ util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,coef_ivt,,,,,,,,,,,,,, #util_BIKE,#Bike,,,,,,,,,,,,,,,,,,, #FIXME_skims_aren't_symmetrical,#FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, util_BIKE_Unavailable_if_didn't_BIKE_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,coef_ivt,,,,,,,,,,,,, #util_WALK_to_Local,#Walk to Local,,,,,,,,,,,,,,,,,,, util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_IVT'] + dot_skims['WLK_LOC_IVT']),,,,,,coef_ivt,,,,,,,,,,,, @@ -51,12 +46,11 @@ util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multipl util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_LOC_IWAIT']-waitThresh).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_XWAIT'] + dot_skims['WLK_LOC_XWAIT']),,,,,,coef_ivt,,,,,,,,,,,, util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_NT']).clip(0) + (dot_skims['WLK_LOC_NT']).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@wacc_multiplier * 2 * (df.origin_local_time + df.dest_local_time),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@wegr_multiplier * 2 * (df.origin_local_time + df.dest_local_time),,,,,,coef_ivt,,,,,,,,,,,, util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WAUX'] + dot_skims['WLK_LOC_WAUX']),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_LOC_FARE'] + dot_skims['WLK_LOC_FARE']),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.WLKLOC_fare_discount * df.ivot * (odt_skims['WLK_LOC_FARE'] + dot_skims['WLK_LOC_FARE']),,,,,,coef_ivt,,,,,,,,,,,, util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,coef_ivt,,,,,,,,,,,, util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, #util_WALK_to_Premium,#Walk to Premium,,,,,,,,,,,,,,,,,,, util_WALK_PRM_Unavailable,WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, @@ -70,12 +64,11 @@ util_WALK_PRM_Short_iwait_time,WALK_PRM - Short iwait time,@short_i_wait_multipl util_WALK_PRM_Long_iwait_time,WALK_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_PRM_IWAIT']-waitThresh).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_PRM_XWAIT'] + dot_skims['WLK_PRM_XWAIT']),,,,,,,coef_ivt,,,,,,,,,,, util_WALK_PRM_number_of_transfers,WALK_PRM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_PRM_NT']).clip(0) + (dot_skims['WLK_PRM_NT']).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@wacc_multiplier * 2 * (df.origin_prm_time + df.dest_prm_time),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@wegr_multiplier * 2 * (df.origin_prm_time + df.dest_prm_time),,,,,,,coef_ivt,,,,,,,,,,, util_WALK_PRM_Walk_other_time,WALK_PRM - Walk other time,@waux_multiplier * (odt_skims['WLK_PRM_WAUX'] + dot_skims['WLK_PRM_WAUX']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_PRM_FARE'] + dot_skims['WLK_PRM_FARE']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.WLKPRM_fare_discount * df.ivot * (odt_skims['WLK_PRM_FARE'] + dot_skims['WLK_PRM_FARE']),,,,,,,coef_ivt,,,,,,,,,,, util_WALK_PRM_Destination_zone_densityIndex,WALK_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Topology,WALK_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,, util_WALK_PRM_Person_is_less_than_10_years_old,WALK_PRM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, #util_WALK_to_MIX,#Walk to MIX,,,,,,,,,,,,,,,,,,, util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, @@ -90,12 +83,11 @@ util_WALK_MIX_Short_iwait_time,WALK_MIX - Short iwait time,@short_i_wait_multipl util_WALK_MIX_Long_iwait_time,WALK_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@xwait_multiplier * (odt_skims['WLK_MIX_XWAIT'] + dot_skims['WLK_MIX_XWAIT']),,,,,,,,coef_ivt,,,,,,,,,, util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_MIX_NT']).clip(0) + (dot_skims['WLK_MIX_NT']).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@wacc_multiplier * 2 * (df.origin_mix_time + df.dest_mix_time),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@wegr_multiplier * 2 * (df.origin_mix_time + df.dest_mix_time),,,,,,,,coef_ivt,,,,,,,,,, util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@waux_multiplier * (odt_skims['WLK_MIX_WAUX'] + dot_skims['WLK_MIX_WAUX']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_MIX_FARE'] + dot_skims['WLK_MIX_FARE']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.WLKMIX_fare_discount * df.ivot * (odt_skims['WLK_MIX_FARE'] + dot_skims['WLK_MIX_FARE']),,,,,,,,coef_ivt,,,,,,,,,, util_WALK_MIX_Destination_zone_densityIndex,WALK_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Topology,WALK_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, util_WALK_MIX_Person_is_less_than_10_years_old,WALK_MIX - Person is less than 10 years old,@(df.age <= 10),,,,,,,,1,,,,,,,,,, #util_PNR_to_Local,#PNR to Local,,,,,,,,,,,,,,,,,,, util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,pnr_local_available == False,,,,,,,,,-999,,,,,,,,, @@ -106,13 +98,12 @@ util_PNR_LOC_Long_iwait_time,PNR_LOC - Long iwait time,@long_i_wait_multiplier * util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['PNR_LOC_XWAIT'] + dot_skims['PNRE_LOC_XWAIT']),,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['PNR_LOC_NT']).clip(0) + (dot_skims['PNRE_LOC_NT']).clip(0)),,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@dtim_multiplier * (odt_skims['PNR_LOC_DTIME'] + dot_skims['PNRE_LOC_DTIME']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Walk_access_time,PNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Walk_access_time,PNR_LOC - Walk access time,@wacc_multiplier * (df.dest_local_time),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * (df.dest_local_time),,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@waux_multiplier * (odt_skims['PNR_LOC_WAUX'] + dot_skims['PNRE_LOC_WAUX']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_LOC_FARE'] + dot_skims['PNRE_LOC_FARE']) + ((odt_skims['PNR_LOC_DDIST']+dot_skims['PNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.PNRLOC_fare_discount * (odt_skims['PNR_LOC_FARE'] + dot_skims['PNRE_LOC_FARE']) + ((odt_skims['PNR_LOC_DDIST']+dot_skims['PNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_Ratio_of_PNR_access_distance_to_OD_distance,PNR_LOC - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_LOC_DDIST']+ dot_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_Destination_zone_densityIndex,PNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Topology,PNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,, util_PNR_LOC_Person_is_less_than_10_years_old,PNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, #util_PNR_to_Premium,#PNR to Premium,,,,,,,,,,,,,,,,,,, util_PNR_PRM_Unavailable,PNR_PRM - Unavailable,pnr_premium_available == False,,,,,,,,,,-999,,,,,,,, @@ -128,13 +119,12 @@ util_PNR_PRM_Long_iwait_time,PNR_PRM - Long iwait time,@long_i_wait_multiplier * util_PNR_PRM_transfer_wait_time,PNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['PNR_PRM_XWAIT'] + dot_skims['PNRE_PRM_XWAIT']),,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_number_of_transfers,PNR_PRM - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_PRM_NT']).clip(0) + (dot_skims['PNRE_PRM_NT']).clip(0)),,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_PNR_time,PNR_PRM - PNR time,@dtim_multiplier * (odt_skims['PNR_PRM_DTIME'] + dot_skims['PNRE_PRM_DTIME']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Walk_access_time_(at_attraction_end),PNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Walk_access_time_(at_attraction_end),PNR_PRM - Walk access time (at attraction end),@wacc_multiplier * (df.dest_prm_time),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * (df.dest_prm_time),,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@waux_multiplier * (odt_skims['PNR_PRM_WAUX'] + dot_skims['PNRE_PRM_WAUX']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Fare_and_operating_cost,PNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_PRM_FARE']+dot_skims['PNRE_PRM_FARE']) + ((odt_skims['PNR_PRM_DDIST']+dot_skims['PNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Fare_and_operating_cost,PNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.PNRPRM_fare_discount * (odt_skims['PNR_PRM_FARE']+dot_skims['PNRE_PRM_FARE']) + ((odt_skims['PNR_PRM_DDIST']+dot_skims['PNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_Ratio_of_PNR_access_distance_to_OD_distance,PNR_PRM - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_PRM_DDIST']+ dot_skims['PNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_Destination_zone_densityIndex,PNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Topology,PNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,, util_PNR_PRM_Person_is_less_than_10_years_old,PNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, #util_PNR_to_MIX,#PNR to MIX,,,,,,,,,,,,,,,,,,, util_PNR_MIX_Unavailable,PNR_MIX - Unavailable,pnr_mix_available == False,,,,,,,,,,,-999,,,,,,, @@ -151,13 +141,12 @@ util_PNR_MIX_Long_iwait_time,PNR_MIX - Long iwait time,@long_i_wait_multiplier * util_PNR_MIX_transfer_wait_time,PNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['PNR_MIX_XWAIT'] + dot_skims['PNRE_MIX_XWAIT']),,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_number_of_transfers,PNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_MIX_NT']).clip(0) + (dot_skims['PNRE_MIX_NT']).clip(0)),,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_PNR_time,PNR_MIX - PNR time,@dtim_multiplier * (odt_skims['PNR_MIX_DTIME'] + dot_skims['PNRE_MIX_DTIME']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Walk_access_time_(at_attraction_end),PNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Walk_egress_ime_(at_attraction_end),PNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Walk_access_time_(at_attraction_end),PNR_MIX - Walk access time (at attraction end),@wacc_multiplier * (df.dest_mix_time),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Walk_egress_ime_(at_attraction_end),PNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_Walk_other_time,PNR_MIX - Walk other time,@waux_multiplier * (odt_skims['PNR_MIX_WAUX'] + dot_skims['PNRE_MIX_WAUX']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Fare_and_operating_cost,PNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_MIX_FARE']+dot_skims['PNRE_MIX_FARE']) + ((odt_skims['PNR_MIX_DDIST']+dot_skims['PNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Fare_and_operating_cost,PNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.PNRMIX_fare_discount * (odt_skims['PNR_MIX_FARE']+dot_skims['PNRE_MIX_FARE']) + ((odt_skims['PNR_MIX_DDIST']+dot_skims['PNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_Ratio_of_PNR_access_distance_to_OD_distance,PNR_MIX - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_MIX_DDIST']+ dot_skims['PNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_Destination_zone_densityIndex,PNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Topology,PNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,, util_PNR_MIX_Person_is_less_than_10_years_old,PNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, #util_KNR_to_Local,#KNR to Local,,,,,,,,,,,,,,,,,,, util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,knr_local_available == False,,,,,,,,,,,,-999,,,,,, @@ -168,13 +157,12 @@ util_KNR_LOC_Long_iwait_time,KNR_LOC - Long iwait time,@long_i_wait_multiplier * util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['KNR_LOC_XWAIT'] + dot_skims['KNRE_LOC_XWAIT']),,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['KNR_LOC_NT']).clip(0) + (dot_skims['KNRE_LOC_NT']).clip(0)),,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@dtim_multiplier * (odt_skims['KNR_LOC_DTIME'] + dot_skims['KNRE_LOC_DTIME']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Walk_access_time,KNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Walk_access_time,KNR_LOC - Walk access time,@wacc_multiplier * (df.dest_local_time),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * (df.dest_local_time),,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@waux_multiplier * (odt_skims['KNR_LOC_WAUX'] + dot_skims['KNRE_LOC_WAUX']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Fare_and_operating_cost,KNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_LOC_FARE'] + dot_skims['KNRE_LOC_FARE']) + ((odt_skims['KNR_LOC_DDIST']+dot_skims['KNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Fare_and_operating_cost,KNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.KNRLOC_fare_discount * (odt_skims['KNR_LOC_FARE'] + dot_skims['KNRE_LOC_FARE']) + ((odt_skims['KNR_LOC_DDIST']+dot_skims['KNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_Ratio_of_KNR_access_distance_to_OD_distance,KNR_LOC - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_LOC_DDIST']+ dot_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_Destination_zone_densityIndex,KNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Topology,KNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,, util_KNR_LOC_Person_is_less_than_10_years_old,KNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, #util_KNR_to_Premium,#KNR to Premium,,,,,,,,,,,,,,,,,,, util_KNR_PRM_Unavailable,KNR_PRM - Unavailable,knr_premium_available == False,,,,,,,,,,,,,-999,,,,, @@ -190,13 +178,12 @@ util_KNR_PRM_Long_iwait_time,KNR_PRM - Long iwait time,@long_i_wait_multiplier * util_KNR_PRM_transfer_wait_time,KNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['KNR_PRM_XWAIT'] + dot_skims['KNRE_PRM_XWAIT']),,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_number_of_transfers,KNR_PRM - number of transfers,@xfers_drv_multiplier *((odt_skims['KNR_PRM_NT']).clip(0) + (dot_skims['KNRE_PRM_NT']).clip(0)),,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_KNR_time,KNR_PRM - KNR time,@dtim_multiplier * (odt_skims['KNR_PRM_DTIME'] + dot_skims['KNRE_PRM_DTIME']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Walk_access_time_(at_attraction_end),KNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Walk_access_time_(at_attraction_end),KNR_PRM - Walk access time (at attraction end),@wacc_multiplier * (df.dest_prm_time),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * (df.dest_prm_time),,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_Walk_other_time,KNR_PRM - Walk other time,@waux_multiplier * (odt_skims['KNR_PRM_WAUX'] + dot_skims['KNRE_PRM_WAUX']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Fare_and_operating_cost,KNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_PRM_FARE']+dot_skims['KNRE_PRM_FARE']) + ((odt_skims['KNR_PRM_DDIST']+dot_skims['KNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Fare_and_operating_cost,KNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.KNRPRM_fare_discount * (odt_skims['KNR_PRM_FARE']+dot_skims['KNRE_PRM_FARE']) + ((odt_skims['KNR_PRM_DDIST']+dot_skims['KNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_Ratio_of_KNR_access_distance_to_OD_distance,KNR_PRM - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_PRM_DDIST']+ dot_skims['KNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_Destination_zone_densityIndex,KNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Topology,KNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,, util_KNR_PRM_Person_is_less_than_10_years_old,KNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, #util_KNR_to_MIX,#KNR to MIX,,,,,,,,,,,,,,,,,,, util_KNR_MIX_Unavailable,KNR_MIX - Unavailable,knr_mix_available == False,,,,,,,,,,,,,,-999,,,, @@ -213,13 +200,12 @@ util_KNR_MIX_Long_iwait_time,KNR_MIX - Long iwait time,@long_i_wait_multiplier * util_KNR_MIX_transfer_wait_time,KNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['KNR_MIX_XWAIT'] + dot_skims['KNRE_MIX_XWAIT']),,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_number_of_transfers,KNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['KNR_MIX_NT']).clip(0) + (dot_skims['KNRE_MIX_NT']).clip(0)),,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_KNR_time,KNR_MIX - KNR time,@dtim_multiplier * (odt_skims['KNR_MIX_DTIME'] + dot_skims['KNRE_MIX_DTIME']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Walk_access_time_(at_attraction_end),KNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Walk_egress_ime_(at_attraction_end),KNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Walk_access_time_(at_attraction_end),KNR_MIX - Walk access time (at attraction end),@wacc_multiplier * (df.dest_mix_time),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Walk_egress_time_(at_attraction_end),KNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_Walk_other_time,KNR_MIX - Walk other time,@waux_multiplier * (odt_skims['KNR_MIX_WAUX'] + dot_skims['KNRE_MIX_WAUX']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Fare_and_operating_cost,KNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_MIX_FARE']+dot_skims['KNRE_MIX_FARE']) + ((odt_skims['KNR_MIX_DDIST']+dot_skims['KNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Fare_and_operating_cost,KNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.KNRMIX_fare_discount * (odt_skims['KNR_MIX_FARE']+dot_skims['KNRE_MIX_FARE']) + ((odt_skims['KNR_MIX_DDIST']+dot_skims['KNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_Ratio_of_KNR_access_distance_to_OD_distance,KNR_MIX - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_MIX_DDIST']+ dot_skims['KNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_Destination_zone_densityIndex,KNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Topology,KNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,, util_KNR_MIX_Person_is_less_than_10_years_old,KNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, #,Taxi,,,,,,,,,,,,,,,,,,, util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,coef_ivt,, @@ -263,6 +249,9 @@ util_PNR_to_Transit_Auto_sufficient,PNR to Transit - Auto Sufficient,@(df.is_ind util_KNR_to_Transit_Zero_auto,KNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,,,, util_KNR_to_Transit_Auto_deficient,KNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,,,, util_KNR_to_Transit_Auto_sufficient,KNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,,,, +util_ridehail_Zero_auto,ridehail - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,ridehail_ASC_no_auto,ridehail_ASC_no_auto,ridehail_ASC_no_auto +util_ridehail_Auto_deficient,ridehail - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,ridehail_ASC_auto_deficient,ridehail_ASC_auto_deficient,ridehail_ASC_auto_deficient +util_ridehail_Auto_sufficient,ridehail - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,ridehail_ASC_auto_sufficient,ridehail_ASC_auto_sufficient,ridehail_ASC_auto_sufficient util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,taxi_ASC_no_auto,, util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, @@ -294,6 +283,9 @@ util_Joint_PNR_to_Transit_Auto_sufficient,Joint - PNR to Transit - Auto Sufficie util_Joint_KNR_to_Transit_Zero_auto,Joint - KNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,,,, util_Joint_KNR_to_Transit_Auto_deficient,Joint - KNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,,,, util_Joint_KNR_to_Transit_Auto_sufficient,Joint - KNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,,,, +util_Joint_ridehail_Zero_auto,Joint - ridehail - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,joint_ridehail_ASC_no_auto,joint_ridehail_ASC_no_auto,joint_ridehail_ASC_no_auto +util_Joint_ridehail_Auto_deficient,Joint - ridehail - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_ridehail_ASC_auto_deficient,joint_ridehail_ASC_auto_deficient,joint_ridehail_ASC_auto_deficient +util_Joint_ridehail_Auto_sufficient,Joint - ridehail - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_ridehail_ASC_auto_sufficient,joint_ridehail_ASC_auto_sufficient,joint_ridehail_ASC_auto_sufficient util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, @@ -321,3 +313,33 @@ School_Bus_Wait_Time,School Bus - Wait Time,@short_i_wait_multiplier*10,,,,,,,,, util_schoolbus_ASC_no_auto,School Bus No Auto ASC,@(df.auto_ownership == 0),,,,,,,,,,,,,,,schoolbus_ASC_no_auto,,, util_schoolbus_ASC_auto_deficient,School Bus Auto Deficient ASC,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_deficient,,, util_schoolbus_ASC_auto_sufficient,School Bus Auto Sufficient ASC,@((df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_sufficient,,, +util_umt_unavailable_wlk,UMT unavailable for non affiliates WALK,@((odt_skims['WLK_LOC_IVTT_UMT'] + dot_skims['WLK_LOC_IVTT_UMT'])>0) & (df.umich_affiliate==False),,,,,,-999,,,,,,,,,,,, +util_umt_unavailable_pnr,UMT unavailable for non affiliates PNR,@((odt_skims['PNR_LOC_IVTT_UMT'] + dot_skims['PNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate==False),,,,,,,,,-999,,,,,,,,, +util_umt_unavailable_knr,UMT unavailable for non affiliates KNR,@((odt_skims['KNR_LOC_IVTT_UMT'] + dot_skims['KNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate==False),,,,,,,,,,,,-999,,,,,, +#forincome,,,,,,,,,,,,,,,,,,,, +util_WALK_to_Transit_inc_low,Walk to Transit - Low income,@(df.is_indiv & (df.income <= 25000)),,,,,,walk_transit_ASC_inc_low,walk_transit_ASC_inc_low,walk_transit_ASC_inc_low,,,,,,,,,, +util_WALK_to_Transit_inc_mid,Walk to Transit - Mid income,@(df.is_indiv & (df.income <= 75000) & (df.income > 25000)),,,,,,walk_transit_ASC_inc_mid,walk_transit_ASC_inc_mid,walk_transit_ASC_inc_mid,,,,,,,,,, +util_WALK_to_Transit_inc_high,Walk to Transit - High income,@(df.is_indiv & (df.income >75000)),,,,,,walk_transit_ASC_inc_high,walk_transit_ASC_inc_high,walk_transit_ASC_inc_high,,,,,,,,,, +util_PNR_to_Transit_inc_low,PNR to Transit - Low income,@(df.is_indiv & (df.income <= 25000)),,,,,,,,,pnr_transit_ASC_inc_low,pnr_transit_ASC_inc_low,pnr_transit_ASC_inc_low,,,,,,, +util_PNR_to_Transit_inc_mid,PNR to Transit - Mid income,@(df.is_indiv & (df.income <= 75000) & (df.income > 25000)),,,,,,,,,pnr_transit_ASC_inc_mid,pnr_transit_ASC_inc_mid,pnr_transit_ASC_inc_mid,,,,,,, +util_PNR_to_Transit_inc_high,PNR to Transit - High income,@(df.is_indiv & (df.income >75000)),,,,,,,,,pnr_transit_ASC_inc_high,pnr_transit_ASC_inc_high,pnr_transit_ASC_inc_high,,,,,,, +util_KNR_to_Transit_inc_low,KNR to Transit - Low income,@(df.is_indiv & (df.income <= 25000)),,,,,,,,,,,,knr_transit_ASC_inc_low,knr_transit_ASC_inc_low,knr_transit_ASC_inc_low,,,, +util_KNR_to_Transit_inc_mid,KNR to Transit - Mid income,@(df.is_indiv & (df.income <= 75000) & (df.income > 25000)),,,,,,,,,,,,knr_transit_ASC_inc_mid,knr_transit_ASC_inc_mid,knr_transit_ASC_inc_mid,,,, +util_KNR_to_Transit_inc_high,KNR to Transit - High income,@(df.is_indiv & (df.income >75000)),,,,,,,,,,,,knr_transit_ASC_inc_high,knr_transit_ASC_inc_high,knr_transit_ASC_inc_high,,,, +#util_Joint_tour_ASCs,,,,,,,,,,,,,,,,,,,, +util_Joint_WALK_to_Transit_inc_low,Joint - Walk to Transit - Low income,@(df.is_joint & (df.income <= 25000)),,,,,,joint_walk_transit_ASC_inc_low,joint_walk_transit_ASC_inc_low,joint_walk_transit_ASC_inc_low,,,,,,,,,, +util_Joint_WALK_to_Transit_inc_mid,Joint - Walk to Transit - Mid income,@(df.is_joint & (df.income <= 75000) & (df.income > 25000)),,,,,,joint_walk_transit_ASC_inc_mid,joint_walk_transit_ASC_inc_mid,joint_walk_transit_ASC_inc_mid,,,,,,,,,, +util_Joint_WALK_to_Transit_inc_high,Joint - Walk to Transit - High income,@(df.is_joint & (df.income >75000)),,,,,,joint_walk_transit_ASC_inc_high,joint_walk_transit_ASC_inc_high,joint_walk_transit_ASC_inc_high,,,,,,,,,, +util_Joint_PNR_to_Transit_inc_low,Joint - PNR to Transit - Low income,@(df.is_joint & (df.income <= 25000)),,,,,,,,,joint_pnr_transit_ASC_inc_low,joint_pnr_transit_ASC_inc_low,joint_pnr_transit_ASC_inc_low,,,,,,, +util_Joint_PNR_to_Transit_inc_mid,Joint - PNR to Transit - Mid income,@(df.is_joint & (df.income <= 75000) & (df.income > 40000)),,,,,,,,,joint_pnr_transit_ASC_inc_mid,joint_pnr_transit_ASC_inc_mid,joint_pnr_transit_ASC_inc_mid,,,,,,, +util_Joint_PNR_to_Transit_inc_high,Joint - PNR to Transit - High income,@(df.is_joint & (df.income >75000)),,,,,,,,,joint_pnr_transit_ASC_inc_high,joint_pnr_transit_ASC_inc_high,joint_pnr_transit_ASC_inc_high,,,,,,, +util_Joint_KNR_to_Transit_inc_low,Joint - KNR to Transit - Low income,@(df.is_joint & (df.income <= 25000)),,,,,,,,,,,,joint_knr_transit_ASC_inc_low,joint_knr_transit_ASC_inc_low,joint_knr_transit_ASC_inc_low,,,, +util_Joint_KNR_to_Transit_inc_mid,Joint - KNR to Transit - Mid income,@(df.is_joint & (df.income <= 75000) & (df.income > 25000)),,,,,,,,,,,,joint_knr_transit_ASC_inc_mid,joint_knr_transit_ASC_inc_mid,joint_knr_transit_ASC_inc_mid,,,, +util_Joint_KNR_to_Transit_inc_high,Joint - KNR to Transit - High income,@(df.is_joint & (df.income >75000)),,,,,,,,,,,,joint_knr_transit_ASC_inc_high,joint_knr_transit_ASC_inc_high,joint_knr_transit_ASC_inc_high,,,, +#,,,,,,,,,,,,,,,,,,,, +util_district1_2_cte,districts1_2_cte,districts1_2_cte,,,,,,1,1,1,1,1,1,1,1,1,,,, +util_district1_8_13_cte,districtS_8 to 13 cte,districts8_13_cte,,,,,,1,1,1,1,1,1,1,1,1,,,, +util_district1_15_cte,districts15_cte,districts15_cte,,,,,,coef_transit_district15,,,coef_transit_district15,,,coef_transit_district15,,,,,, +#,,,,,,,,,,,,,,,,,,,, +util_walk_DPM_asc,,PMOV_transit,,,,,,,0.8,,,0.8,,,0.8,,,,, +util_walk_StCar_asc,,StCar_transit,,,,,,,0.7,,,0.7,,,0.7,,,,, diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml b/activitysim/examples/production_semcog/configs/tour_mode_choice.yaml old mode 100755 new mode 100644 similarity index 89% rename from activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml rename to activitysim/examples/production_semcog/configs/tour_mode_choice.yaml index 59f4067bf..19b352c85 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice.yaml @@ -53,17 +53,6 @@ COEFFICIENTS: tour_mode_choice_coeffs.csv COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.50 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 maxCbdAreaTypeThresh: 2 indivTour: 1.00000 upperEA: 5 @@ -123,8 +112,8 @@ CONSTANTS: max_waitTime: 50 ivt_cost_multiplier: 0.6 - ivt_pmov_multiplier: 1.0 - ivt_stcar_multiplier: 0.9 + ivt_pmov_multiplier: 0.8 + ivt_stcar_multiplier: 0.8 ivt_brt_multiplier: 0.9 ivt_urb_multiplier: 0.8 ivt_com_multiplier: 0.7 @@ -166,11 +155,6 @@ CONSTANTS: 8: 1.0 transit_subsidy_fare_discount_percent_worktour: 0.9 -# joint_sr2_ASC_no_auto: 0 -# joint_sr2_ASC_auto_deficient: 0 -# joint_sr2_ASC_auto_sufficient: 0 -# joint_drive_transit_ASC_no_auto: 0 - # so far, we can use the same spec as for non-joint tours preprocessor: SPEC: tour_mode_choice_annotate_choosers_preprocessor @@ -199,6 +183,8 @@ LOGSUM_CHOOSER_COLUMNS: - num_workers - value_of_time - free_parking_at_work + - umich_affiliate + - income - transit_pass_ownership - transit_pass_subsidy -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum \ No newline at end of file +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/production_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv new file mode 100644 index 000000000..b676ec554 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -0,0 +1,193 @@ +Description,Target,Expression +#,, +local,_DF_IS_TOUR,'tour_type' in df.columns +,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False +#,, +,orig_district,"reindex(land_use.DISTRICT20, df[orig_col_name])" +,dest_district,"reindex(land_use.DISTRICT20, df[dest_col_name])" +,districts1_2_cte,"np.where((orig_district.isin([1,2])) & (dest_district.isin([1,2])), 0.1,0)" +,districts8_13_cte,"np.where((orig_district.isin([8,9,10,13])) & (dest_district.isin([8,9,10,13])), -2,0)" +,districts15_cte,"np.where((orig_district==15) & (dest_district==15), 2.2,0)" +#,, + local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns +,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" +,work_tour_is_drive,_parent_tour_mode.isin(['DRIVEALONE']) +,work_tour_is_bike,_parent_tour_mode=='BIKE' +,work_tour_is_SOV,_parent_tour_mode.isin(['DRIVEALONE']) +#,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False +,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False +,is_indiv,~is_joint +,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False +,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False +,is_school,(df.tour_type=='school') & (df.is_university==False) if _DF_IS_TOUR else False +#,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time +#,, +,ivot,1.0/df.value_of_time +,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,pnr_transit_available,_walk_transit_destination & (df.auto_ownership > 0) +,knr_transit_available,_walk_transit_origin & _walk_transit_destination +,origin_walk_time,shortWalk*60/walkSpeed +,destination_walk_time,shortWalk*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)" +,dest_density_measure,"(reindex(land_use.tot_pop, df[dest_col_name]) + reindex(land_use.tot_emp, df[dest_col_name])) / (reindex(land_use.tot_acres, df[dest_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False +,_dest_daily_parking_cost,"reindex(land_use.parking_daily, df[dest_col_name])" +,_dest_hourly_parking_cost_temp,"reindex(land_use.parking_hourly, df[dest_col_name])" +"if hourlycost option not exist, set it to high so daily would be selected below",_dest_hourly_parking_cost,"np.where(_dest_hourly_parking_cost_temp.isna(), 999, _dest_hourly_parking_cost_temp)" +,_daily_parking_cost,"np.where(is_mandatory, np.minimum(_dest_hourly_parking_cost * df.duration/2*100, _dest_daily_parking_cost*100), np.where(_dest_hourly_parking_cost!=999, _dest_hourly_parking_cost * df.duration/2 * 100,0))" +,daily_parking_cost,"np.where(_free_parking_available, 0, _daily_parking_cost)" +#,, +,distance,od_skims['DIST'] +,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) +,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 +,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) +#,, +destination in central business district,destination_in_cbd,"(reindex(land_use.AreaType, df[dest_col_name]) < setting('cbd_threshold')) * 1" +#,,FIXME diagnostic +#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) +,umich_affiliate,"df.get('umich_worker', default=False) | df.get('umich_student', default=False)" +#,,fare discounts (no discount for use in logsums) +#,ptype,df.ptype if _DF_IS_TOUR else 1 +#,transit_pass_ownership,df.transit_pass_ownership if _DF_IS_TOUR else 0 +#,transit_pass_subsidy,df.transit_pass_subsidy if _DF_IS_TOUR else 0 +#,tour_type,df.tour_type if _DF_IS_TOUR else 'other' +#,fare_nopass_discount_percent,"ptype.map({k: v for k, v in transit_nopass_fare_discount_percent.items()})" +#,fare_pass_discount_percent,"ptype.map({k: v for k, v in transit_pass_fare_discount_percent.items()})" +#,fare_subsidy_discount_percent,"np.where(tour_type=='work', transit_subsidy_fare_discount_percent_worktour, 0)" +#,fare_discount_percent_ff,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), 0, 1)" +#,fare_discount_percent_tf,"np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), fare_pass_discount_percent, 1)" +#,fare_discount_percent_ft,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), fare_nopass_discount_percent * (1-fare_subsidy_discount_percent), 1)" +#,fare_discount_percent_tt,"np.where((transit_pass_ownership == True ) & (transit_pass_subsidy == True), fare_pass_discount_percent * (1-fare_subsidy_discount_percent), 1)" +#,fare_discount_percent,fare_discount_percent_ff * fare_discount_percent_tf * fare_discount_percent_ft * fare_discount_percent_tt +#,, +,ptype,df.ptype if _DF_IS_TOUR else 1 +,transit_pass_ownership,df.transit_pass_ownership if _DF_IS_TOUR else 0 +,transit_pass_subsidy,df.transit_pass_subsidy if _DF_IS_TOUR else 0 +cash discounts for seniors and students and preKs,transit_cash_discount_factor,"np.where((df.age>64) | (ptype>5), cash_discount_factor, 1)" +100% subsidy if own subsidy,transit_subsidy_factor,"np.where(transit_pass_subsidy, 1, 0)" +,pass_discount_factor_ddot,"ptype.map({k: v for k, v in transit_pass_discount_factor_ddot.items()})" +,pass_discount_factor_smart,"ptype.map({k: v for k, v in transit_pass_discount_factor_smart.items()})" +,pass_discount_factor_aata,"ptype.map({k: v for k, v in transit_pass_discount_factor_aata.items()})" +,pass_discount_factor_um,"ptype.map({k: v for k, v in transit_pass_discount_factor_um.items()})" +,pass_discount_factor_other,"ptype.map({k: v for k, v in transit_pass_discount_factor_other.items()})" +#,pass discount differs by ptype and agency, +,WLKLOC_pass_discount_DDOT,"np.where((odt_skims['WLK_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNRLOC_pass_discount_DDOT,"np.where((odt_skims['PNR_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNRLOC_pass_discount_DDOT,"np.where((odt_skims['KNR_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,WLKLOC_pass_discount_SMART,"np.where((odt_skims['WLK_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNRLOC_pass_discount_SMART,"np.where((odt_skims['PNR_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNRLOC_pass_discount_SMART,"np.where((odt_skims['KNR_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,WLKLOC_pass_discount_AATA,"np.where((odt_skims['WLK_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNRLOC_pass_discount_AATA,"np.where((odt_skims['PNR_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNRLOC_pass_discount_AATA,"np.where((odt_skims['KNR_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,WLKLOC_pass_discount_UM,"np.where((odt_skims['WLK_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNRLOC_pass_discount_UM,"np.where((odt_skims['PNR_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNRLOC_pass_discount_UM,"np.where((odt_skims['KNR_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,WLKLOC_pass_discount_OTHER,"np.where((odt_skims['WLK_LOC_IVTT_BWAT']>0) | (odt_skims['WLK_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,PNRLOC_pass_discount_OTHER,"np.where((odt_skims['PNR_LOC_IVTT_BWAT']>0) | (odt_skims['PNR_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,KNRLOC_pass_discount_OTHER,"np.where((odt_skims['KNR_LOC_IVTT_BWAT']>0) | (odt_skims['KNR_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,WLKPRM_pass_discount_OTHER,"np.where((odt_skims['WLK_PRM_IVTT_PMov']>0) | (odt_skims['WLK_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,PNRPRM_pass_discount_OTHER,"np.where((odt_skims['PNR_PRM_IVTT_PMov']>0) | (odt_skims['PNR_PRM_IVTT_LET']>0), pass_discount_factor_other, 1)" +,KNRPRM_pass_discount_OTHER,"np.where((odt_skims['KNR_PRM_IVTT_PMov']>0) | (odt_skims['KNR_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,WLKMIX_pass_discount_DDOT,"np.where((odt_skims['WLK_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNRMIX_pass_discount_DDOT,"np.where((odt_skims['PNR_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNRMIX_pass_discount_DDOT,"np.where((odt_skims['KNR_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,WLKMIX_pass_discount_SMART,"np.where((odt_skims['WLK_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNRMIX_pass_discount_SMART,"np.where((odt_skims['PNR_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNRMIX_pass_discount_SMART,"np.where((odt_skims['KNR_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,WLKMIX_pass_discount_AATA,"np.where((odt_skims['WLK_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNRMIX_pass_discount_AATA,"np.where((odt_skims['PNR_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNRMIX_pass_discount_AATA,"np.where((odt_skims['KNR_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,WLKMIX_pass_discount_UM,"np.where((odt_skims['WLK_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNRMIX_pass_discount_UM,"np.where((odt_skims['PNR_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNRMIX_pass_discount_UM,"np.where((odt_skims['KNR_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,WLKMIX_pass_discount_OTHER,"np.where((odt_skims['WLK_MIX_IVTT_BWAT']>0) | (odt_skims['WLK_MIX_IVTT_LET']>0) | (odt_skims['WLK_PRM_IVTT_PMov']>0) | (odt_skims['WLK_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,PNRMIX_pass_discount_OTHER,"np.where((odt_skims['PNR_MIX_IVTT_BWAT']>0) | (odt_skims['PNR_MIX_IVTT_LET']>0) | (odt_skims['PNR_MIX_IVTT_PMov']>0) | (odt_skims['PNR_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,KNRMIX_pass_discount_OTHER,"np.where((odt_skims['KNR_MIX_IVTT_BWAT']>0) | (odt_skims['KNR_MIX_IVTT_LET']>0) | (odt_skims['KNR_MIX_IVTT_PMov']>0) | (odt_skims['KNR_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +#,Use the defined hierarchy to pick the pass discount for each transit alt, +,WLKLOC_pass_discount,"np.where(WLKLOC_pass_discount_DDOT<1, WLKLOC_pass_discount_DDOT, np.where(WLKLOC_pass_discount_SMART<1, WLKLOC_pass_discount_SMART, np.where(WLKLOC_pass_discount_AATA<1, WLKLOC_pass_discount_AATA, WLKLOC_pass_discount_OTHER)))" +,WLKPRM_pass_discount,WLKPRM_pass_discount_OTHER +,WLKMIX_pass_discount,"np.where(WLKMIX_pass_discount_DDOT<1, WLKMIX_pass_discount_DDOT, np.where(WLKMIX_pass_discount_SMART<1, WLKMIX_pass_discount_SMART, np.where(WLKMIX_pass_discount_AATA<1, WLKMIX_pass_discount_AATA, WLKMIX_pass_discount_OTHER)))" +,PNRLOC_pass_discount,"np.where(PNRLOC_pass_discount_DDOT<1, PNRLOC_pass_discount_DDOT, np.where(PNRLOC_pass_discount_SMART<1, PNRLOC_pass_discount_SMART, np.where(PNRLOC_pass_discount_AATA<1, PNRLOC_pass_discount_AATA, PNRLOC_pass_discount_OTHER)))" +,PNRPRM_pass_discount,PNRPRM_pass_discount_OTHER +,PNRMIX_pass_discount,"np.where(PNRMIX_pass_discount_DDOT<1, PNRMIX_pass_discount_DDOT, np.where(PNRMIX_pass_discount_SMART<1, PNRMIX_pass_discount_SMART, np.where(PNRMIX_pass_discount_AATA<1, PNRMIX_pass_discount_AATA, PNRMIX_pass_discount_OTHER)))" +,KNRLOC_pass_discount,"np.where(KNRLOC_pass_discount_DDOT<1, KNRLOC_pass_discount_DDOT, np.where(KNRLOC_pass_discount_SMART<1, KNRLOC_pass_discount_SMART, np.where(KNRLOC_pass_discount_AATA<1, KNRLOC_pass_discount_AATA, KNRLOC_pass_discount_OTHER)))" +,KNRPRM_pass_discount,KNRPRM_pass_discount_OTHER +,KNRMIX_pass_discount,"np.where(KNRMIX_pass_discount_DDOT<1, KNRMIX_pass_discount_DDOT, np.where(KNRMIX_pass_discount_SMART<1, KNRMIX_pass_discount_SMART, np.where(KNRMIX_pass_discount_AATA<1, KNRMIX_pass_discount_AATA, KNRMIX_pass_discount_OTHER)))" +#,"Now compute the fare discounts using cash, subsidy, and pass discounts", +,WLKLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,WLKPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,WLKMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +#,, +Determining Tour Origin,origin,df.origin if 'origin' in df.columns else df.home_zone_id if 'home_zone_id' in df.columns else df.workplace_zone_id +Determining Tour Destination,destination,df.destination if 'destination' in df.columns else df.alt_dest +#access egress distances,, +,origin_local_dist,"reindex(land_use.AE_LOCAL, origin)" +,origin_prm_dist,"reindex(land_use.AE_PRM, origin)" +min of local and premium dist,origin_mix_dist,"np.minimum(origin_local_dist, origin_prm_dist)" +,dest_local_dist,"reindex(land_use.AE_LOCAL, destination)" +,dest_prm_dist,"reindex(land_use.AE_PRM, destination)" +,dest_mix_dist,"np.minimum(dest_local_dist, dest_prm_dist)" +#transit availability,, +,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0) & (dot_skims['WLK_LOC_IVT']>0) & (origin_local_dist < max_local_walk_dist) & (dest_local_dist < max_local_walk_dist) +,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0) & (dot_skims['WLK_PRM_IVT']>0) & (origin_prm_dist < max_prm_walk_dist) & (dest_prm_dist < max_prm_walk_dist) +,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0) & (dot_skims['WLK_MIX_IVT']>0) & (origin_mix_dist < max_mix_walk_dist) & (dest_mix_dist < max_mix_walk_dist) +,pnr_local_available,pnr_transit_available & (odt_skims['PNR_LOC_IVT']>0) & (dot_skims['PNRE_LOC_IVT']>0) & (dest_local_dist < max_local_walk_dist) +,pnr_premium_available,pnr_transit_available & (odt_skims['PNR_PRM_IVT']>0) & (dot_skims['PNRE_PRM_IVT']>0) & (dest_prm_dist < max_prm_walk_dist) +,pnr_mix_available,pnr_transit_available & (odt_skims['PNR_MIX_IVT']>0) & (dot_skims['PNRE_MIX_IVT']>0) & (dest_mix_dist < max_mix_walk_dist) +,knr_local_available,knr_transit_available & (odt_skims['KNR_LOC_IVT']>0) & (dot_skims['KNRE_LOC_IVT']>0) & (dest_local_dist < max_local_walk_dist) +,knr_premium_available,knr_transit_available & (odt_skims['KNR_PRM_IVT']>0) & (dot_skims['KNRE_PRM_IVT']>0) & (dest_prm_dist < max_prm_walk_dist) +,knr_mix_available,knr_transit_available & (odt_skims['KNR_MIX_IVT']>0) & (dot_skims['KNRE_MIX_IVT']>0) & (dest_mix_dist < max_mix_walk_dist) +#access egress times,, +,origin_local_time,origin_local_dist * 60/walk_speed +,origin_prm_time,origin_prm_dist * 60/walk_speed +,origin_mix_time,origin_mix_dist * 60/walk_speed +,dest_local_time,dest_local_dist * 60/walk_speed +,dest_prm_time,dest_prm_dist * 60/walk_speed +,dest_mix_time,dest_mix_dist * 60/walk_speed +#,, +,PMOV_transit,"np.where(((odt_skims['WLK_PRM_IVTT_PMov']>0) & (dot_skims['WLK_PRM_IVTT_PMov']>0)) | ((odt_skims['PNR_PRM_IVTT_PMov']>0) & (dot_skims['PNRE_PRM_IVTT_PMov']>0)) | ((odt_skims['KNR_PRM_IVTT_PMov']>0) & (dot_skims['KNRE_PRM_IVTT_PMov']>0)) & ((odt_skims['WLK_MIX_NT']==0)&(odt_skims['PNR_MIX_NT']==0)&(odt_skims['KNR_MIX_NT']==0)&(odt_skims['PNRE_MIX_NT']==0)&(odt_skims['KNRE_MIX_NT']==0)),1,0)" +,PMOV_transit_wTransfer,"np.where(((odt_skims['WLK_PRM_IVTT_PMov']>0) & (dot_skims['WLK_PRM_IVTT_PMov']>0)) | ((odt_skims['PNR_PRM_IVTT_PMov']>0) & (dot_skims['PNRE_PRM_IVTT_PMov']>0)) | ((odt_skims['KNR_PRM_IVTT_PMov']>0) & (dot_skims['KNRE_PRM_IVTT_PMov']>0)) & ((odt_skims['WLK_PRM_NT']>0)|(odt_skims['PNR_PRM_NT']>0)|(odt_skims['KNR_PRM_NT']>0)|(odt_skims['PNRE_PRM_NT']>0)|(odt_skims['KNRE_PRM_NT']>0)),1,0)" +,StCar_transit,"np.where((((odt_skims['WLK_PRM_IVTT_StCar']>0) & (dot_skims['WLK_PRM_IVTT_StCar']>0)) | ((odt_skims['PNR_PRM_IVTT_StCar']>0) & (dot_skims['PNRE_PRM_IVTT_StCar']>0)) | ((odt_skims['KNR_PRM_IVTT_StCar']>0) & (dot_skims['KNRE_PRM_IVTT_StCar']>0))) & ((odt_skims['WLK_MIX_NT']==0)&(odt_skims['PNR_MIX_NT']==0)&(odt_skims['KNR_MIX_NT']==0)&(odt_skims['PNRE_MIX_NT']==0)&(odt_skims['KNRE_MIX_NT']==0)),1,0)" +,StCar_transit_wTransfer,"np.where((((odt_skims['WLK_PRM_IVTT_StCar']>0) & (dot_skims['WLK_PRM_IVTT_StCar']>0)) | ((odt_skims['PNR_PRM_IVTT_StCar']>0) & (dot_skims['PNRE_PRM_IVTT_StCar']>0)) | ((odt_skims['KNR_PRM_IVTT_StCar']>0) & (dot_skims['KNRE_PRM_IVTT_StCar']>0))) & ((odt_skims['WLK_PRM_NT']>0)&(odt_skims['PNR_PRM_NT']>0)&(odt_skims['KNR_PRM_NT']>0)&(odt_skims['PNRE_PRM_NT']>0)&(odt_skims['KNRE_PRM_NT']>0)),1,0)" +,AATA_transit,"np.where(((odt_skims['WLK_LOC_IVTT_AAATA']>0) & (dot_skims['WLK_LOC_IVTT_AAATA']>0)) | ((odt_skims['PNR_LOC_IVTT_AAATA']>0) & (dot_skims['PNRE_LOC_IVTT_AAATA']>0)) | ((odt_skims['KNR_LOC_IVTT_AAATA']>0) & (dot_skims['KNRE_LOC_IVTT_AAATA']>0)),1,0)" diff --git a/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs.csv new file mode 100644 index 000000000..9fb2d0eda --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs.csv @@ -0,0 +1,485 @@ +coefficient_name,value,constrain +coef_nest_root,1,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_KNRACCESS,0.5,T +coef_nest_TRANSIT_PNRACCESS,0.5,T +coef_nest_SCHOOL_BUS,0.72,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,7.245135654,F +walk_ASC_no_auto_escort,5.377811059,F +walk_ASC_no_auto_othdiscr,5.386612954,F +walk_ASC_no_auto_othmaint,3.863903259,F +walk_ASC_no_auto_school,12.61921094,F +walk_ASC_no_auto_shopping,4.953481559,F +walk_ASC_no_auto_social,3.988109854,F +walk_ASC_no_auto_univ,3.283262773,F +walk_ASC_no_auto_work,6.163475276,F +walk_ASC_no_auto_atwork,1.891282209,F +walk_ASC_auto_deficient_eatout,3.078855796,F +walk_ASC_auto_deficient_escort,-1.36860007,F +walk_ASC_auto_deficient_othdiscr,2.053658296,F +walk_ASC_auto_deficient_othmaint,0.90248689,F +walk_ASC_auto_deficient_school,1.058407835,F +walk_ASC_auto_deficient_shopping,1.80361979,F +walk_ASC_auto_deficient_social,2.674434796,F +walk_ASC_auto_deficient_univ,6.147162109,F +walk_ASC_auto_deficient_work,2.159453616,F +walk_ASC_auto_deficient_atwork,-4.856915223,F +walk_ASC_auto_sufficient_eatout,2.09444032,F +walk_ASC_auto_sufficient_escort,-1.573050181,F +walk_ASC_auto_sufficient_othdiscr,1.80609762,F +walk_ASC_auto_sufficient_othmaint,0.038519819,F +walk_ASC_auto_sufficient_school,-1.250294494,F +walk_ASC_auto_sufficient_shopping,-0.030177281,F +walk_ASC_auto_sufficient_social,2.24996862,F +walk_ASC_auto_sufficient_univ,3.586216606,F +walk_ASC_auto_sufficient_work,-0.509785407,F +walk_ASC_auto_sufficient_atwork,0.761535047,F +bike_ASC_no_auto_eatout,0.642751048,F +bike_ASC_no_auto_escort,-2.349130024,F +bike_ASC_no_auto_othdiscr,-0.601743112,F +bike_ASC_no_auto_othmaint,-0.093484624,F +bike_ASC_no_auto_school,0,F +bike_ASC_no_auto_shopping,-0.798762524,F +bike_ASC_no_auto_social,-0.204736702,F +bike_ASC_no_auto_univ,1.140656827,F +bike_ASC_no_auto_work,2.704438894,F +bike_ASC_no_auto_atwork,11.45762899,F +bike_ASC_auto_deficient_eatout,-1.02631018,F +bike_ASC_auto_deficient_escort,-3.864296367,F +bike_ASC_auto_deficient_othdiscr,0.45033208,F +bike_ASC_auto_deficient_othmaint,-0.854833267,F +bike_ASC_auto_deficient_school,-0.628649477,F +bike_ASC_auto_deficient_shopping,-0.212213027,F +bike_ASC_auto_deficient_social,1.17732182,F +bike_ASC_auto_deficient_univ,0.721995349,F +bike_ASC_auto_deficient_work,-0.021302925,F +bike_ASC_auto_deficient_atwork,-10,F +bike_ASC_auto_sufficient_eatout,-1.245442859,F +bike_ASC_auto_sufficient_escort,-4.512371773,F +bike_ASC_auto_sufficient_othdiscr,-1.116555459,F +bike_ASC_auto_sufficient_othmaint,-2.257565773,F +bike_ASC_auto_sufficient_school,-3.048928862,F +bike_ASC_auto_sufficient_shopping,-2.015473673,F +bike_ASC_auto_sufficient_social,-1.413166759,F +bike_ASC_auto_sufficient_univ,-0.386788169,F +bike_ASC_auto_sufficient_work,-2.09112205,F +bike_ASC_auto_sufficient_atwork,-0.675198411,F +sr2_ASC_auto_deficient_eatout,0.154370234,F +sr2_ASC_auto_deficient_escort,0.638036424,F +sr2_ASC_auto_deficient_othdiscr,0.226287034,F +sr2_ASC_auto_deficient_othmaint,0.900189124,F +sr2_ASC_auto_deficient_school,0.007268111,F +sr2_ASC_auto_deficient_shopping,0.882133984,F +sr2_ASC_auto_deficient_social,1.421988534,F +sr2_ASC_auto_deficient_univ,0.023547615,F +sr2_ASC_auto_deficient_work,-0.330451823,F +sr2_ASC_auto_deficient_atwork,-9.627336365,F +sr2_ASC_auto_sufficient_eatout,0.77961169,F +sr2_ASC_auto_sufficient_escort,0.053737881,F +sr2_ASC_auto_sufficient_othdiscr,0.41365231,F +sr2_ASC_auto_sufficient_othmaint,0.311916711,F +sr2_ASC_auto_sufficient_school,-1.925159966,F +sr2_ASC_auto_sufficient_shopping,0.251444951,F +sr2_ASC_auto_sufficient_social,0.44040864,F +sr2_ASC_auto_sufficient_univ,-0.577536319,F +sr2_ASC_auto_sufficient_work,-0.762064286,F +sr2_ASC_auto_sufficient_atwork,-1.677110787,F +sr3p_ASC_no_auto_eatout,1.919648853,F +sr3p_ASC_no_auto_escort,0.141779955,F +sr3p_ASC_no_auto_othdiscr,1.869818073,F +sr3p_ASC_no_auto_othmaint,1.151521255,F +sr3p_ASC_no_auto_school,9.534872554,F +sr3p_ASC_no_auto_shopping,1.674917175,F +sr3p_ASC_no_auto_social,0.193958853,F +sr3p_ASC_no_auto_univ,-0.004632104,F +sr3p_ASC_no_auto_work,-0.958625298,F +sr3p_ASC_no_auto_atwork,-0.515769726,F +sr3p_ASC_auto_deficient_eatout,0.006033604,F +sr3p_ASC_auto_deficient_escort,0.407928534,F +sr3p_ASC_auto_deficient_othdiscr,1.007077844,F +sr3p_ASC_auto_deficient_othmaint,-0.533276306,F +sr3p_ASC_auto_deficient_school,0.540571768,F +sr3p_ASC_auto_deficient_shopping,0.742746028,F +sr3p_ASC_auto_deficient_social,1.460705544,F +sr3p_ASC_auto_deficient_univ,0.310548307,F +sr3p_ASC_auto_deficient_work,-0.810446929,F +sr3p_ASC_auto_deficient_atwork,-9.60253331,F +sr3p_ASC_auto_sufficient_eatout,0.575245084,F +sr3p_ASC_auto_sufficient_escort,0.00612872,F +sr3p_ASC_auto_sufficient_othdiscr,0.316887534,F +sr3p_ASC_auto_sufficient_othmaint,-0.01195742,F +sr3p_ASC_auto_sufficient_school,-1.319156496,F +sr3p_ASC_auto_sufficient_shopping,-0.014030045,F +sr3p_ASC_auto_sufficient_social,0.234564344,F +sr3p_ASC_auto_sufficient_univ,-0.773804624,F +sr3p_ASC_auto_sufficient_work,-1.403842021,F +sr3p_ASC_auto_sufficient_atwork,-2.092459793,F +walk_transit_ASC_no_auto_eatout,1.155234091,F +walk_transit_ASC_no_auto_escort,-2.959133756,F +walk_transit_ASC_no_auto_othdiscr,0.805375791,F +walk_transit_ASC_no_auto_othmaint,1.822419944,F +walk_transit_ASC_no_auto_school,8.545499008,F +walk_transit_ASC_no_auto_shopping,1.364821944,F +walk_transit_ASC_no_auto_social,-0.056937609,F +walk_transit_ASC_no_auto_univ,2.528979018,F +walk_transit_ASC_no_auto_work,4.142510201,F +walk_transit_ASC_no_auto_atwork,-2.411553685,F +walk_transit_ASC_auto_deficient_eatout,-0.128346613,F +walk_transit_ASC_auto_deficient_escort,-2.554647163,F +walk_transit_ASC_auto_deficient_othdiscr,0.863705027,F +walk_transit_ASC_auto_deficient_othmaint,-0.653668963,F +walk_transit_ASC_auto_deficient_school,1.877685198,F +walk_transit_ASC_auto_deficient_shopping,1.558399937,F +walk_transit_ASC_auto_deficient_social,0.885061497,F +walk_transit_ASC_auto_deficient_univ,4.708304361,F +walk_transit_ASC_auto_deficient_work,1.633394791,F +walk_transit_ASC_auto_deficient_atwork,-10.41361322,F +walk_transit_ASC_auto_sufficient_eatout,-3.270192236,F +walk_transit_ASC_auto_sufficient_escort,-6.111068613,F +walk_transit_ASC_auto_sufficient_othdiscr,-2.963869566,F +walk_transit_ASC_auto_sufficient_othmaint,-2.72333881,F +walk_transit_ASC_auto_sufficient_school,-2.973138685,F +walk_transit_ASC_auto_sufficient_shopping,-3.37990141,F +walk_transit_ASC_auto_sufficient_social,-2.502877536,F +walk_transit_ASC_auto_sufficient_univ,0.236918226,F +walk_transit_ASC_auto_sufficient_work,-2.639528024,F +walk_transit_ASC_auto_sufficient_atwork,-6.16180819,F +ridehail_ASC_no_auto_eatout,-2.045321096,F +ridehail_ASC_no_auto_escort,-0.167281413,F +ridehail_ASC_no_auto_othdiscr,-2.045321096,F +ridehail_ASC_no_auto_othmaint,-0.167281413,F +ridehail_ASC_no_auto_school,0,F +ridehail_ASC_no_auto_shopping,-0.167281413,F +ridehail_ASC_no_auto_social,-2.045321096,F +ridehail_ASC_no_auto_univ,-2,F +ridehail_ASC_no_auto_work,-4.816096715,F +ridehail_ASC_no_auto_atwork,-6.233631424,F +ridehail_ASC_auto_deficient_eatout,2.640431996,F +ridehail_ASC_auto_deficient_escort,-4.464633613,F +ridehail_ASC_auto_deficient_othdiscr,2.640431996,F +ridehail_ASC_auto_deficient_othmaint,-4.464633613,F +ridehail_ASC_auto_deficient_school,-8,F +ridehail_ASC_auto_deficient_shopping,-4.464633613,F +ridehail_ASC_auto_deficient_social,2.640431996,F +ridehail_ASC_auto_deficient_univ,-4.235125902,F +ridehail_ASC_auto_deficient_work,-0.674654999,F +ridehail_ASC_auto_deficient_atwork,-6.501686194,F +ridehail_ASC_auto_sufficient_eatout,1.259325587,F +ridehail_ASC_auto_sufficient_escort,-3.809040237,F +ridehail_ASC_auto_sufficient_othdiscr,1.259325587,F +ridehail_ASC_auto_sufficient_othmaint,-3.809040237,F +ridehail_ASC_auto_sufficient_school,-10,F +ridehail_ASC_auto_sufficient_shopping,-3.809040237,F +ridehail_ASC_auto_sufficient_social,1.259325587,F +ridehail_ASC_auto_sufficient_univ,-3.592479947,F +ridehail_ASC_auto_sufficient_work,-1.366285294,F +ridehail_ASC_auto_sufficient_atwork,-1.09678927,F +joint_walk_ASC_no_auto_all,0.187552654,F +joint_walk_ASC_auto_deficient_all,0.920969324,F +joint_walk_ASC_auto_sufficient_all,-2.318049556,F +joint_bike_ASC_no_auto_all,-2.436803748,F +joint_bike_ASC_auto_deficient_all,-1.988554538,F +joint_bike_ASC_auto_sufficient_all,-6.14466696,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,-0.780430525,F +joint_sr3p_ASC_auto_deficient_all,-1.058527735,F +joint_sr3p_ASC_auto_sufficient_all,-3.128634943,F +joint_walk_transit_ASC_no_auto_all,-1.887321235,F +joint_walk_transit_ASC_auto_deficient_all,-3.750881156,F +joint_walk_transit_ASC_auto_sufficient_all,-8.858079666,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F +joint_knr_transit_ASC_auto_deficient_all,-0.253706151,F +joint_knr_transit_ASC_auto_sufficient_all,-9.070832511,F +joint_knr_transit_ASC_no_auto_all,1.114290704,F +joint_pnr_transit_ASC_auto_deficient_all,-5.785321634,F +joint_pnr_transit_ASC_auto_sufficient_all,-11.59627446,F +joint_pnr_transit_ASC_no_auto_all,0,T +knr_transit_ASC_auto_deficient_atwork,1.209179807,F +knr_transit_ASC_auto_deficient_eatout,-1.349765692,F +knr_transit_ASC_auto_deficient_escort,-2.442640714,F +knr_transit_ASC_auto_deficient_othdiscr,-0.357714052,F +knr_transit_ASC_auto_deficient_othmaint,-0.541662514,F +knr_transit_ASC_auto_deficient_school,2.51160049,F +knr_transit_ASC_auto_deficient_shopping,1.670406386,F +knr_transit_ASC_auto_deficient_social,-0.336357582,F +knr_transit_ASC_auto_deficient_univ,0.326785116,F +knr_transit_ASC_auto_deficient_work,-0.161159036,F +knr_transit_ASC_auto_sufficient_atwork,-8.401027,F +knr_transit_ASC_auto_sufficient_eatout,-4.530870958,F +knr_transit_ASC_auto_sufficient_escort,-6.70815857,F +knr_transit_ASC_auto_sufficient_othdiscr,-4.224548288,F +knr_transit_ASC_auto_sufficient_othmaint,-3.32042877,F +knr_transit_ASC_auto_sufficient_school,-2.140979117,F +knr_transit_ASC_auto_sufficient_shopping,-3.97699137,F +knr_transit_ASC_auto_sufficient_social,-3.763556258,F +knr_transit_ASC_auto_sufficient_univ,-4.764851235,F +knr_transit_ASC_auto_sufficient_work,-4.605345843,F +knr_transit_ASC_no_auto_atwork,9.920920461,F +knr_transit_ASC_no_auto_eatout,-0.974474564,F +knr_transit_ASC_no_auto_escort,0.343505332,F +knr_transit_ASC_no_auto_othdiscr,-0.974474564,F +knr_transit_ASC_no_auto_othmaint,0.343505332,F +knr_transit_ASC_no_auto_school,10.55224125,F +knr_transit_ASC_no_auto_shopping,0.343505332,F +knr_transit_ASC_no_auto_social,-0.974474564,F +knr_transit_ASC_no_auto_univ,-1.114321226,F +knr_transit_ASC_no_auto_work,2.483257905,F +local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F +local_ASC_school_univ,0,F +local_ASC_work,0,F +mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F +mix_ASC_school_univ,0,F +mix_ASC_work,0,F +pnr_transit_ASC_auto_deficient_atwork,-998.8196,F +pnr_transit_ASC_auto_deficient_eatout,-0.52292005,F +pnr_transit_ASC_auto_deficient_escort,-1.530423928,F +pnr_transit_ASC_auto_deficient_othdiscr,-0.80279535,F +pnr_transit_ASC_auto_deficient_othmaint,-0.676149508,F +pnr_transit_ASC_auto_deficient_school,1.831621993,F +pnr_transit_ASC_auto_deficient_shopping,-0.795209008,F +pnr_transit_ASC_auto_deficient_social,0.43999335,F +pnr_transit_ASC_auto_deficient_univ,2.538231083,F +pnr_transit_ASC_auto_deficient_work,-0.691994719,F +pnr_transit_ASC_auto_sufficient_atwork,-999.21466,F +pnr_transit_ASC_auto_sufficient_eatout,-4.071057003,F +pnr_transit_ASC_auto_sufficient_escort,-6.639637962,F +pnr_transit_ASC_auto_sufficient_othdiscr,-3.480132843,F +pnr_transit_ASC_auto_sufficient_othmaint,-4.663161062,F +pnr_transit_ASC_auto_sufficient_school,-3.549444013,F +pnr_transit_ASC_auto_sufficient_shopping,-4.210107062,F +pnr_transit_ASC_auto_sufficient_social,-3.717396893,F +pnr_transit_ASC_auto_sufficient_univ,0.209549886,F +pnr_transit_ASC_auto_sufficient_work,-2.835680786,F +pnr_transit_ASC_no_auto_atwork,0,F +pnr_transit_ASC_no_auto_eatout,0,F +pnr_transit_ASC_no_auto_escort,0,F +pnr_transit_ASC_no_auto_othdiscr,0,F +pnr_transit_ASC_no_auto_othmaint,0,F +pnr_transit_ASC_no_auto_school,0,F +pnr_transit_ASC_no_auto_shopping,0,F +pnr_transit_ASC_no_auto_social,0,F +pnr_transit_ASC_no_auto_univ,0,F +pnr_transit_ASC_no_auto_work,0,F +premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F +premium_ASC_school_univ,0,F +premium_ASC_work,0,F +schoolbus_ASC_auto_deficient_school,0.700834542,F +schoolbus_ASC_auto_sufficient_school,-1.304297646,F +schoolbus_ASC_no_auto_school,10.02392266,F +schoolbus_ASC_not_school,0,T +sr2_ASC_no_auto_atwork,-0.458737013,F +sr2_ASC_no_auto_eatout,1.213600735,F +sr2_ASC_no_auto_escort,1.552940632,F +sr2_ASC_no_auto_othdiscr,1.213600735,F +sr2_ASC_no_auto_othmaint,1.552940632,F +sr2_ASC_no_auto_school,8.193011953,F +sr2_ASC_no_auto_shopping,1.552940632,F +sr2_ASC_no_auto_social,1.213600735,F +sr2_ASC_no_auto_univ,0.470068308,F +sr2_ASC_no_auto_work,0.746531489,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_ridehail_ASC_no_auto_all,-4,F +joint_ridehail_ASC_auto_deficient_all,6.156570253,F +joint_ridehail_ASC_auto_sufficient_all,7.140732878,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +walk_transit_ASC_inc_low_eatout,0.249268821,F +walk_transit_ASC_inc_low_escort,0.418897353,F +walk_transit_ASC_inc_low_othdiscr,0.249268821,F +walk_transit_ASC_inc_low_othmaint,0.418897353,F +walk_transit_ASC_inc_low_school,1.014378998,F +walk_transit_ASC_inc_low_shopping,0.418897353,F +walk_transit_ASC_inc_low_social,0.249268821,F +walk_transit_ASC_inc_low_univ,2.289414794,F +walk_transit_ASC_inc_low_work,1.747005245,F +walk_transit_ASC_inc_low_atwork,1.167959856,F +walk_transit_ASC_inc_mid_eatout,-0.121462274,F +walk_transit_ASC_inc_mid_escort,0.214797352,F +walk_transit_ASC_inc_mid_othdiscr,-0.121462274,F +walk_transit_ASC_inc_mid_othmaint,0.214797352,F +walk_transit_ASC_inc_mid_school,-0.353164785,F +walk_transit_ASC_inc_mid_shopping,0.214797352,F +walk_transit_ASC_inc_mid_social,-0.121462274,F +walk_transit_ASC_inc_mid_univ,-1.19203085,F +walk_transit_ASC_inc_mid_work,-0.128411222,F +walk_transit_ASC_inc_mid_atwork,-2.081379825,F +walk_transit_ASC_inc_high_eatout,-1.174572867,F +walk_transit_ASC_inc_high_escort,-1.373952127,F +walk_transit_ASC_inc_high_othdiscr,-1.174572867,F +walk_transit_ASC_inc_high_othmaint,-1.373952127,F +walk_transit_ASC_inc_high_school,-2.817962265,F +walk_transit_ASC_inc_high_shopping,-1.373952127,F +walk_transit_ASC_inc_high_social,-1.174572867,F +walk_transit_ASC_inc_high_univ,-0.598055046,F +walk_transit_ASC_inc_high_work,-2.538809251,F +walk_transit_ASC_inc_high_atwork,-5.862742158,F +pnr_transit_ASC_inc_low_eatout,1.509446923,F +pnr_transit_ASC_inc_low_escort,1.502330638,F +pnr_transit_ASC_inc_low_othdiscr,1.509446923,F +pnr_transit_ASC_inc_low_othmaint,1.502330638,F +pnr_transit_ASC_inc_low_school,-0.99779427,F +pnr_transit_ASC_inc_low_shopping,1.502330638,F +pnr_transit_ASC_inc_low_social,1.509446923,F +pnr_transit_ASC_inc_low_univ,2.160687357,F +pnr_transit_ASC_inc_low_work,1.183944595,F +pnr_transit_ASC_inc_low_atwork,0,F +pnr_transit_ASC_inc_mid_eatout,-0.393853639,F +pnr_transit_ASC_inc_mid_escort,-0.014404566,F +pnr_transit_ASC_inc_mid_othdiscr,-0.393853639,F +pnr_transit_ASC_inc_mid_othmaint,-0.014404566,F +pnr_transit_ASC_inc_mid_school,-0.179710517,F +pnr_transit_ASC_inc_mid_shopping,-0.014404566,F +pnr_transit_ASC_inc_mid_social,-0.393853639,F +pnr_transit_ASC_inc_mid_univ,-0.339971203,F +pnr_transit_ASC_inc_mid_work,0.538595407,F +pnr_transit_ASC_inc_mid_atwork,66,F +pnr_transit_ASC_inc_high_eatout,-0.411400233,F +pnr_transit_ASC_inc_high_escort,-1.521732376,F +pnr_transit_ASC_inc_high_othdiscr,-0.411400233,F +pnr_transit_ASC_inc_high_othmaint,-1.521732376,F +pnr_transit_ASC_inc_high_school,-0.467173335,F +pnr_transit_ASC_inc_high_shopping,-1.521732376,F +pnr_transit_ASC_inc_high_social,-0.411400233,F +pnr_transit_ASC_inc_high_univ,-0.829494594,F +pnr_transit_ASC_inc_high_work,-0.780647012,F +pnr_transit_ASC_inc_high_atwork,0,F +knr_transit_ASC_inc_low_eatout,-0.983447279,F +knr_transit_ASC_inc_low_escort,0.467701556,F +knr_transit_ASC_inc_low_othdiscr,-0.983447279,F +knr_transit_ASC_inc_low_othmaint,0.467701556,F +knr_transit_ASC_inc_low_school,1.235223075,F +knr_transit_ASC_inc_low_shopping,0.467701556,F +knr_transit_ASC_inc_low_social,-0.983447279,F +knr_transit_ASC_inc_low_univ,1.612527976,F +knr_transit_ASC_inc_low_work,1.488447771,F +knr_transit_ASC_inc_low_atwork,-12,F +knr_transit_ASC_inc_mid_eatout,-2.824223783,F +knr_transit_ASC_inc_mid_escort,-0.718143315,F +knr_transit_ASC_inc_mid_othdiscr,-2.824223783,F +knr_transit_ASC_inc_mid_othmaint,-0.718143315,F +knr_transit_ASC_inc_mid_school,-1.197934766,F +knr_transit_ASC_inc_mid_shopping,-0.718143315,F +knr_transit_ASC_inc_mid_social,-2.824223783,F +knr_transit_ASC_inc_mid_univ,0.984455325,F +knr_transit_ASC_inc_mid_work,-0.121947176,F +knr_transit_ASC_inc_mid_atwork,-9.318317361,F +knr_transit_ASC_inc_high_eatout,-2.72665114,F +knr_transit_ASC_inc_high_escort,-4.500580376,F +knr_transit_ASC_inc_high_othdiscr,-2.72665114,F +knr_transit_ASC_inc_high_othmaint,-4.500580376,F +knr_transit_ASC_inc_high_school,-3.051932364,F +knr_transit_ASC_inc_high_shopping,-4.500580376,F +knr_transit_ASC_inc_high_social,-2.72665114,F +knr_transit_ASC_inc_high_univ,1.895479231,F +knr_transit_ASC_inc_high_work,-2.817727332,F +knr_transit_ASC_inc_high_atwork,-16,F +joint_walk_transit_ASC_inc_low_all,0.136230432,F +joint_walk_transit_ASC_inc_mid_all,0.53125371,F +joint_walk_transit_ASC_inc_high_all,-0.473894293,F +joint_pnr_transit_ASC_inc_low_all,1.010477438,F +joint_pnr_transit_ASC_inc_mid_all,-0.049747835,F +joint_pnr_transit_ASC_inc_high_all,0.144894615,F +joint_knr_transit_ASC_inc_low_all,-3.14140084,F +joint_knr_transit_ASC_inc_mid_all,-2.154652426,F +joint_knr_transit_ASC_inc_high_all,-2.725154069,F +coef_transit_district15_univ,1,T +coef_transit_district15_work,1,T +coef_transit_district15_school,1,T +coef_transit_district15_escort,1,T +coef_transit_district15_shopping,1,T +coef_transit_district15_eatout,1,T +coef_transit_district15_othmaint,1,T +coef_transit_district15_social,1,T +coef_transit_district15_othdiscr,1,T +coef_transit_district15_atwork,1,T diff --git a/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_.csv new file mode 100644 index 000000000..8f4e7792c --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_.csv @@ -0,0 +1,475 @@ +coefficient_name,value,constrain +coef_nest_root,1.0,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_KNRACCESS,0.5,T +coef_nest_TRANSIT_PNRACCESS,0.5,T +coef_nest_SCHOOL_BUS,0.72,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0.0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0.0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0.0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0.0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0.0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,6.679066631523342,F +walk_ASC_no_auto_escort,4.882867039849283,F +walk_ASC_no_auto_othdiscr,4.820543931523342,F +walk_ASC_no_auto_othmaint,3.368959239849282,F +walk_ASC_no_auto_school,12.225978613445273,F +walk_ASC_no_auto_shopping,4.458537539849282,F +walk_ASC_no_auto_social,3.4220408315233444,F +walk_ASC_no_auto_univ,3.910159101302879,F +walk_ASC_no_auto_work,4.159023185762584,F +walk_ASC_no_auto_atwork,1.544217384663518,F +walk_ASC_auto_deficient_eatout,2.9407128853961284,F +walk_ASC_auto_deficient_escort,-1.5008955985254078,F +walk_ASC_auto_deficient_othdiscr,1.9155153853961302,F +walk_ASC_auto_deficient_othmaint,0.7701913614745923,F +walk_ASC_auto_deficient_school,1.1133914430542913,F +walk_ASC_auto_deficient_shopping,1.6713242614745927,F +walk_ASC_auto_deficient_social,2.536291885396129,F +walk_ASC_auto_deficient_univ,5.969815972382048,F +walk_ASC_auto_deficient_work,1.9673254619764007,F +walk_ASC_auto_deficient_atwork,-4.319605328334406,F +walk_ASC_auto_sufficient_eatout,2.066920561656495,F +walk_ASC_auto_sufficient_escort,-1.5108470410440904,F +walk_ASC_auto_sufficient_othdiscr,1.7785778616564947,F +walk_ASC_auto_sufficient_othmaint,0.10072295895590931,F +walk_ASC_auto_sufficient_school,-1.0979961835986936,F +walk_ASC_auto_sufficient_shopping,0.032025858955909314,F +walk_ASC_auto_sufficient_social,2.222448861656495,F +walk_ASC_auto_sufficient_univ,3.7939305088461226,F 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+walk_transit_ASC_inc_mid_univ,-2.444820175971375,F +walk_transit_ASC_inc_mid_work,0.6020380658763193,F +walk_transit_ASC_inc_mid_atwork,-0.5807624613666531,F +walk_transit_ASC_inc_high_eatout,-0.9034984838833938,F +walk_transit_ASC_inc_high_escort,-1.4748301234406895,F +walk_transit_ASC_inc_high_othdiscr,-0.9034984838833938,F +walk_transit_ASC_inc_high_othmaint,-1.4748301234406895,F +walk_transit_ASC_inc_high_school,-2.5740216646033427,F +walk_transit_ASC_inc_high_shopping,-1.4748301234406895,F +walk_transit_ASC_inc_high_social,-0.9034984838833938,F +walk_transit_ASC_inc_high_univ,-1.1918101962706145,F +walk_transit_ASC_inc_high_work,-2.068393912808487,F +walk_transit_ASC_inc_high_atwork,-0.7763801733229068,F +pnr_transit_ASC_inc_low_eatout,1.3562545460706699,F +pnr_transit_ASC_inc_low_escort,2.0561480128221867,F +pnr_transit_ASC_inc_low_othdiscr,1.3562545460706699,F +pnr_transit_ASC_inc_low_othmaint,2.0561480128221867,F +pnr_transit_ASC_inc_low_school,2.2534975281576393,F +pnr_transit_ASC_inc_low_shopping,2.0561480128221867,F +pnr_transit_ASC_inc_low_social,1.3562545460706699,F +pnr_transit_ASC_inc_low_univ,1.4729631493150355,F +pnr_transit_ASC_inc_low_work,0.8820676061911268,F +pnr_transit_ASC_inc_low_atwork,0.0,F +pnr_transit_ASC_inc_mid_eatout,-0.5050375355698602,F +pnr_transit_ASC_inc_mid_escort,0.029450166916340538,F +pnr_transit_ASC_inc_mid_othdiscr,-0.5050375355698602,F +pnr_transit_ASC_inc_mid_othmaint,0.029450166916340538,F +pnr_transit_ASC_inc_mid_school,2.181266035808401,F +pnr_transit_ASC_inc_mid_shopping,0.029450166916340538,F +pnr_transit_ASC_inc_mid_social,-0.5050375355698602,F +pnr_transit_ASC_inc_mid_univ,-0.13530405576209265,F +pnr_transit_ASC_inc_mid_work,0.750400695688767,F +pnr_transit_ASC_inc_mid_atwork,60.0,F +pnr_transit_ASC_inc_high_eatout,0.045237764173873596,F +pnr_transit_ASC_inc_high_escort,-0.39543968441506877,F +pnr_transit_ASC_inc_high_othdiscr,0.045237764173873596,F +pnr_transit_ASC_inc_high_othmaint,-0.39543968441506877,F +pnr_transit_ASC_inc_high_school,0.05605691672284524,F +pnr_transit_ASC_inc_high_shopping,-0.39543968441506877,F +pnr_transit_ASC_inc_high_social,0.045237764173873596,F +pnr_transit_ASC_inc_high_univ,-0.7895054217492846,F +pnr_transit_ASC_inc_high_work,-0.60578093335835,F +pnr_transit_ASC_inc_high_atwork,0.0,F +knr_transit_ASC_inc_low_eatout,0.3476156239319593,F +knr_transit_ASC_inc_low_escort,0.6764229575641028,F +knr_transit_ASC_inc_low_othdiscr,0.3476156239319593,F +knr_transit_ASC_inc_low_othmaint,0.6764229575641028,F +knr_transit_ASC_inc_low_school,1.304388356086608,F +knr_transit_ASC_inc_low_shopping,0.6764229575641028,F +knr_transit_ASC_inc_low_social,0.3476156239319593,F +knr_transit_ASC_inc_low_univ,2.253938004330797,F +knr_transit_ASC_inc_low_work,1.8606733008375007,F +knr_transit_ASC_inc_low_atwork,-12.0,F +knr_transit_ASC_inc_mid_eatout,-0.28184905727780607,F +knr_transit_ASC_inc_mid_escort,-0.08639791364917668,F +knr_transit_ASC_inc_mid_othdiscr,-0.28184905727780607,F +knr_transit_ASC_inc_mid_othmaint,-0.08639791364917668,F +knr_transit_ASC_inc_mid_school,-0.2053781593474727,F +knr_transit_ASC_inc_mid_shopping,-0.08639791364917668,F +knr_transit_ASC_inc_mid_social,-0.28184905727780607,F +knr_transit_ASC_inc_mid_univ,1.5299186506541744,F +knr_transit_ASC_inc_mid_work,0.5253846211623675,F +knr_transit_ASC_inc_mid_atwork,-10.31831736063357,F +knr_transit_ASC_inc_high_eatout,-0.5007121631463423,F +knr_transit_ASC_inc_high_escort,-2.9736306589568766,F +knr_transit_ASC_inc_high_othdiscr,-0.5007121631463423,F +knr_transit_ASC_inc_high_othmaint,-2.9736306589568766,F +knr_transit_ASC_inc_high_school,-1.5411067400994698,F +knr_transit_ASC_inc_high_shopping,-2.9736306589568766,F +knr_transit_ASC_inc_high_social,-0.5007121631463423,F +knr_transit_ASC_inc_high_univ,-0.6831350878504734,F +knr_transit_ASC_inc_high_work,-3.095941382990495,F +knr_transit_ASC_inc_high_atwork,-14.0,F +joint_walk_transit_ASC_inc_low_all,1.3825348433784466,F +joint_walk_transit_ASC_inc_mid_all,1.8128690564511136,F +joint_walk_transit_ASC_inc_high_all,-0.5760980130913169,F +joint_pnr_transit_ASC_inc_low_all,4.221198468962727,F +joint_pnr_transit_ASC_inc_mid_all,4.038393153909887,F +joint_pnr_transit_ASC_inc_high_all,2.0098920525093384,F +joint_knr_transit_ASC_inc_low_all,-1.8979669665306436,F +joint_knr_transit_ASC_inc_mid_all,-1.4102345809692918,F +joint_knr_transit_ASC_inc_high_all,-3.725154069244148,F diff --git a/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_old.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_old.csv new file mode 100644 index 000000000..b1fb4ae15 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_old.csv @@ -0,0 +1,481 @@ +coefficient_name,value,constrain +coef_nest_root,1.0,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_KNRACCESS,0.5,T +coef_nest_TRANSIT_PNRACCESS,0.5,T +coef_nest_SCHOOL_BUS,0.72,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15.0,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20.0,F +coef_topology_bike_multiplier_atwork,10.0,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2.0,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0.0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0.0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0.0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0.0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0.0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,6.822718443624304,F +walk_ASC_no_auto_escort,5.140624297832461,F +walk_ASC_no_auto_othdiscr,4.9641957436243045,F +walk_ASC_no_auto_othmaint,3.6267164978324593,F +walk_ASC_no_auto_school,12.044782075575325,F +walk_ASC_no_auto_shopping,4.716294797832461,F +walk_ASC_no_auto_social,3.5656926436243057,F +walk_ASC_no_auto_univ,3.5434244072425534,F +walk_ASC_no_auto_work,5.006266772560758,F +walk_ASC_no_auto_atwork,1.2113351486434998,F +walk_ASC_auto_deficient_eatout,2.9441403667443993,F +walk_ASC_auto_deficient_escort,-1.4531445575493998,F +walk_ASC_auto_deficient_othdiscr,1.9189428667444006,F +walk_ASC_auto_deficient_othmaint,0.8179424024506006,F +walk_ASC_auto_deficient_school,1.0854046599057734,F +walk_ASC_auto_deficient_shopping,1.7190753024506003,F +walk_ASC_auto_deficient_social,2.5397193667444005,F +walk_ASC_auto_deficient_univ,4.8529803578438315,F +walk_ASC_auto_deficient_work,1.8266005380418766,F +walk_ASC_auto_deficient_atwork,-2.756728272586131,F +walk_ASC_auto_sufficient_eatout,2.0744244797405047,F +walk_ASC_auto_sufficient_escort,-1.513812590584619,F +walk_ASC_auto_sufficient_othdiscr,1.786081779740504,F +walk_ASC_auto_sufficient_othmaint,0.09775740941538068,F +walk_ASC_auto_sufficient_school,-1.1940436954695723,F +walk_ASC_auto_sufficient_shopping,0.02906030941538071,F +walk_ASC_auto_sufficient_social,2.2299527797405045,F +walk_ASC_auto_sufficient_univ,3.5325037049643253,F +walk_ASC_auto_sufficient_work,-0.37642523521579613,F +walk_ASC_auto_sufficient_atwork,0.6523221280218748,F +bike_ASC_no_auto_eatout,1.2484113670609545,F +bike_ASC_no_auto_escort,-1.8136780907589327,F +bike_ASC_no_auto_othdiscr,0.00391720706095456,F +bike_ASC_no_auto_othmaint,0.4419673092410671,F +bike_ASC_no_auto_school,0.0,F +bike_ASC_no_auto_shopping,-0.2633105907589328,F +bike_ASC_no_auto_social,0.4009236170609546,F +bike_ASC_no_auto_univ,1.6666927392176571,F +bike_ASC_no_auto_work,2.6481439662397532,F +bike_ASC_no_auto_atwork,6.844910054593578,F +bike_ASC_auto_deficient_eatout,-0.748819236843087,F +bike_ASC_auto_deficient_escort,-3.529730509836597,F +bike_ASC_auto_deficient_othdiscr,0.7278230231569129,F +bike_ASC_auto_deficient_othmaint,-0.5202674098365967,F +bike_ASC_auto_deficient_school,-0.5821269052952013,F +bike_ASC_auto_deficient_shopping,0.1223528301634032,F +bike_ASC_auto_deficient_social,1.454812763156913,F +bike_ASC_auto_deficient_univ,1.0379552356554334,F +bike_ASC_auto_deficient_work,0.23373836624404265,F +bike_ASC_auto_deficient_atwork,-8.0,F +bike_ASC_auto_sufficient_eatout,-0.8298621739812653,F +bike_ASC_auto_sufficient_escort,-4.2092191575402556,F +bike_ASC_auto_sufficient_othdiscr,-0.7009747739812653,F +bike_ASC_auto_sufficient_othmaint,-1.954413157540255,F +bike_ASC_auto_sufficient_school,-2.8080078700499853,F +bike_ASC_auto_sufficient_shopping,-1.712321057540255,F +bike_ASC_auto_sufficient_social,-0.997586073981265,F +bike_ASC_auto_sufficient_univ,0.14982748313573938,F +bike_ASC_auto_sufficient_work,-1.7010532227732524,F +bike_ASC_auto_sufficient_atwork,-0.3372705417600544,F +sr2_ASC_auto_deficient_eatout,0.3283268368064485,F +sr2_ASC_auto_deficient_escort,0.6753440177109278,F +sr2_ASC_auto_deficient_othdiscr,0.40024363680644853,F +sr2_ASC_auto_deficient_othmaint,0.9374967177109281,F +sr2_ASC_auto_deficient_school,-0.03264950525910893,F +sr2_ASC_auto_deficient_shopping,0.9194415777109277,F +sr2_ASC_auto_deficient_social,1.5959451368064488,F +sr2_ASC_auto_deficient_univ,-0.0891759944049673,F +sr2_ASC_auto_deficient_work,-0.34479225816168135,F +sr2_ASC_auto_deficient_atwork,-6.641279846487017,F +sr2_ASC_auto_sufficient_eatout,0.8630316969909022,F +sr2_ASC_auto_sufficient_escort,0.16349867582109043,F +sr2_ASC_auto_sufficient_othdiscr,0.49707231699090215,F +sr2_ASC_auto_sufficient_othmaint,0.4216775058210906,F +sr2_ASC_auto_sufficient_school,-1.9642045985365642,F +sr2_ASC_auto_sufficient_shopping,0.36120574582109055,F +sr2_ASC_auto_sufficient_social,0.5238286469909023,F +sr2_ASC_auto_sufficient_univ,-0.5421064732339067,F +sr2_ASC_auto_sufficient_work,-0.7529876643074862,F +sr2_ASC_auto_sufficient_atwork,-1.64512879601755,F +sr3p_ASC_no_auto_eatout,2.01931406595253,F +sr3p_ASC_no_auto_escort,0.14358505312905812,F +sr3p_ASC_no_auto_othdiscr,1.96948328595253,F +sr3p_ASC_no_auto_othmaint,1.153326353129058,F +sr3p_ASC_no_auto_school,9.605191087285755,F +sr3p_ASC_no_auto_shopping,1.676722273129058,F +sr3p_ASC_no_auto_social,0.29362406595253043,F +sr3p_ASC_no_auto_univ,0.20644886565625387,F +sr3p_ASC_no_auto_work,-0.9726826501627635,F +sr3p_ASC_no_auto_atwork,-0.2343953580869902,F +sr3p_ASC_auto_deficient_eatout,0.23022226974659646,F +sr3p_ASC_auto_deficient_escort,0.40426721601079396,F +sr3p_ASC_auto_deficient_othdiscr,1.2312665097465971,F +sr3p_ASC_auto_deficient_othmaint,-0.5369376239892061,F +sr3p_ASC_auto_deficient_school,0.48673173416884724,F +sr3p_ASC_auto_deficient_shopping,0.739084710010794,F +sr3p_ASC_auto_deficient_social,1.6848942097465964,F +sr3p_ASC_auto_deficient_univ,0.1419264846890286,F +sr3p_ASC_auto_deficient_work,-0.8275275080507102,F +sr3p_ASC_auto_deficient_atwork,-6.5560187920511295,F +sr3p_ASC_auto_sufficient_eatout,0.6609446067163997,F +sr3p_ASC_auto_sufficient_escort,0.12040379981892055,F +sr3p_ASC_auto_sufficient_othdiscr,0.40258705671639955,F +sr3p_ASC_auto_sufficient_othmaint,0.10231765981892059,F +sr3p_ASC_auto_sufficient_school,-1.363180446509713,F +sr3p_ASC_auto_sufficient_shopping,0.10024503481892061,F +sr3p_ASC_auto_sufficient_social,0.3202638667163996,F +sr3p_ASC_auto_sufficient_univ,-0.7073673240702565,F +sr3p_ASC_auto_sufficient_work,-1.3878846636146276,F +sr3p_ASC_auto_sufficient_atwork,-2.062889350039879,F +walk_transit_ASC_no_auto_eatout,1.883891071393701,F +walk_transit_ASC_no_auto_escort,-2.4723966663474184,F +walk_transit_ASC_no_auto_othdiscr,1.5340327713937003,F +walk_transit_ASC_no_auto_othmaint,2.309157033652583,F +walk_transit_ASC_no_auto_school,9.193263987645029,F +walk_transit_ASC_no_auto_shopping,1.8515590336525818,F +walk_transit_ASC_no_auto_social,0.6717193713937002,F +walk_transit_ASC_no_auto_univ,2.58947715599033,F +walk_transit_ASC_no_auto_work,3.15363325904082,F +walk_transit_ASC_no_auto_atwork,-1.7552240151655452,F +walk_transit_ASC_auto_deficient_eatout,0.7109988628015919,F +walk_transit_ASC_auto_deficient_escort,-1.8391559665941768,F +walk_transit_ASC_auto_deficient_othdiscr,1.7030505028015923,F +walk_transit_ASC_auto_deficient_othmaint,0.06182223340582346,F +walk_transit_ASC_auto_deficient_school,2.3451227895424895,F +walk_transit_ASC_auto_deficient_shopping,2.273891133405824,F +walk_transit_ASC_auto_deficient_social,1.7244069728015925,F +walk_transit_ASC_auto_deficient_univ,4.399473739917048,F +walk_transit_ASC_auto_deficient_work,1.8307109378575375,F +walk_transit_ASC_auto_deficient_atwork,-7.0545399009686935,F +walk_transit_ASC_auto_sufficient_eatout,-2.271860628726926,F +walk_transit_ASC_auto_sufficient_escort,-5.135468562605636,F +walk_transit_ASC_auto_sufficient_othdiscr,-1.965537958726925,F +walk_transit_ASC_auto_sufficient_othmaint,-1.747738759605638,F +walk_transit_ASC_auto_sufficient_school,-2.20756344839373,F +walk_transit_ASC_auto_sufficient_shopping,-2.404301359605639,F +walk_transit_ASC_auto_sufficient_social,-1.504545928726925,F +walk_transit_ASC_auto_sufficient_univ,1.1148885216440756,F +walk_transit_ASC_auto_sufficient_work,-1.6847937888346383,F +walk_transit_ASC_auto_sufficient_atwork,-5.197111849510012,F +ridehail_ASC_no_auto_eatout,-2.007457319022973,F +ridehail_ASC_no_auto_escort,-0.2754848992648131,F +ridehail_ASC_no_auto_othdiscr,-2.007457319022973,F +ridehail_ASC_no_auto_othmaint,-0.2754848992648131,F +ridehail_ASC_no_auto_school,0.0,F +ridehail_ASC_no_auto_shopping,-0.2754848992648131,F +ridehail_ASC_no_auto_social,-2.007457319022973,F +ridehail_ASC_no_auto_univ,-2.0,F +ridehail_ASC_no_auto_work,-4.976396020924195,F +ridehail_ASC_no_auto_atwork,-7.163452593395215,F +ridehail_ASC_auto_deficient_eatout,2.6746181368480064,F +ridehail_ASC_auto_deficient_escort,-4.2279054377439005,F +ridehail_ASC_auto_deficient_othdiscr,2.6746181368480064,F +ridehail_ASC_auto_deficient_othmaint,-4.2279054377439005,F +ridehail_ASC_auto_deficient_school,-8.0,F 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+drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F +joint_knr_transit_ASC_auto_deficient_all,-2.670080297969336,F +joint_knr_transit_ASC_auto_sufficient_all,-5.626656841852157,F +joint_knr_transit_ASC_no_auto_all,-0.814936205856911,F +joint_pnr_transit_ASC_auto_deficient_all,-5.706106879142635,F +joint_pnr_transit_ASC_auto_sufficient_all,-8.412260786548387,F +joint_pnr_transit_ASC_no_auto_all,0.0,T +knr_transit_ASC_auto_deficient_atwork,1.5089055851759707,F +knr_transit_ASC_auto_deficient_eatout,-0.9470216377816388,F +knr_transit_ASC_auto_deficient_escort,-2.5857161098394186,F +knr_transit_ASC_auto_deficient_othdiscr,0.04503000221836112,F +knr_transit_ASC_auto_deficient_othmaint,-0.6847379098394176,F +knr_transit_ASC_auto_deficient_school,2.7946164302215064,F +knr_transit_ASC_auto_deficient_shopping,1.5273309901605825,F +knr_transit_ASC_auto_deficient_social,0.06638647221836119,F 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+taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7.0,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7.0,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7.0,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_ridehail_ASC_no_auto_all,-4.0,F +joint_ridehail_ASC_auto_deficient_all,5.859969356147283,F +joint_ridehail_ASC_auto_sufficient_all,7.306961330349286,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +walk_transit_ASC_inc_low_eatout,1.2339363031963198,F +walk_transit_ASC_inc_low_escort,1.201103015803608,F +walk_transit_ASC_inc_low_othdiscr,1.2339363031963198,F +walk_transit_ASC_inc_low_othmaint,1.201103015803608,F +walk_transit_ASC_inc_low_school,1.8923995616433105,F +walk_transit_ASC_inc_low_shopping,1.201103015803608,F +walk_transit_ASC_inc_low_social,1.2339363031963198,F +walk_transit_ASC_inc_low_univ,2.486946107198816,F +walk_transit_ASC_inc_low_work,1.4359827680857389,F +walk_transit_ASC_inc_low_atwork,2.016150482811873,F +walk_transit_ASC_inc_mid_eatout,0.7222532298117899,F +walk_transit_ASC_inc_mid_escort,0.88803873919672,F +walk_transit_ASC_inc_mid_othdiscr,0.7222532298117899,F +walk_transit_ASC_inc_mid_othmaint,0.88803873919672,F +walk_transit_ASC_inc_mid_school,0.37219217050789505,F +walk_transit_ASC_inc_mid_shopping,0.88803873919672,F +walk_transit_ASC_inc_mid_social,0.7222532298117899,F +walk_transit_ASC_inc_mid_univ,-0.25982241797484223,F +walk_transit_ASC_inc_mid_work,0.23587952366576392,F +walk_transit_ASC_inc_mid_atwork,-1.0396975919999343,F +walk_transit_ASC_inc_high_eatout,-0.480437971951284,F +walk_transit_ASC_inc_high_escort,-0.9166434368001927,F +walk_transit_ASC_inc_high_othdiscr,-0.480437971951284,F +walk_transit_ASC_inc_high_othmaint,-0.9166434368001927,F +walk_transit_ASC_inc_high_school,-2.611132785383946,F +walk_transit_ASC_inc_high_shopping,-0.9166434368001927,F +walk_transit_ASC_inc_high_social,-0.480437971951284,F +walk_transit_ASC_inc_high_univ,0.21432963246903436,F +walk_transit_ASC_inc_high_work,-1.4229695811930847,F +walk_transit_ASC_inc_high_atwork,-2.328322914707847,F +pnr_transit_ASC_inc_low_eatout,0.7545408345619884,F +pnr_transit_ASC_inc_low_escort,1.797694266149767,F +pnr_transit_ASC_inc_low_othdiscr,0.7545408345619884,F +pnr_transit_ASC_inc_low_othmaint,1.797694266149767,F +pnr_transit_ASC_inc_low_school,3.320804266359301,F +pnr_transit_ASC_inc_low_shopping,1.797694266149767,F +pnr_transit_ASC_inc_low_social,0.7545408345619884,F +pnr_transit_ASC_inc_low_univ,1.7413858014213,F +pnr_transit_ASC_inc_low_work,0.7037737316861775,F +pnr_transit_ASC_inc_low_atwork,0.0,F +pnr_transit_ASC_inc_mid_eatout,-0.5123135950656624,F +pnr_transit_ASC_inc_mid_escort,0.35109387934760067,F +pnr_transit_ASC_inc_mid_othdiscr,-0.5123135950656624,F +pnr_transit_ASC_inc_mid_othmaint,0.35109387934760067,F +pnr_transit_ASC_inc_mid_school,2.110436137790761,F +pnr_transit_ASC_inc_mid_shopping,0.35109387934760067,F +pnr_transit_ASC_inc_mid_social,-0.5123135950656624,F +pnr_transit_ASC_inc_mid_univ,0.15154649938507608,F +pnr_transit_ASC_inc_mid_work,0.6016256542742084,F +pnr_transit_ASC_inc_mid_atwork,44.0,F +pnr_transit_ASC_inc_high_eatout,-0.02558831850501536,F +pnr_transit_ASC_inc_high_escort,-0.4820109337930607,F +pnr_transit_ASC_inc_high_othdiscr,-0.02558831850501536,F +pnr_transit_ASC_inc_high_othmaint,-0.4820109337930607,F +pnr_transit_ASC_inc_high_school,0.6099351997190907,F +pnr_transit_ASC_inc_high_shopping,-0.4820109337930607,F +pnr_transit_ASC_inc_high_social,-0.02558831850501536,F +pnr_transit_ASC_inc_high_univ,-0.4004018570120217,F +pnr_transit_ASC_inc_high_work,-0.4470041532895622,F +pnr_transit_ASC_inc_high_atwork,0.0,F +knr_transit_ASC_inc_low_eatout,0.3960804410090851,F +knr_transit_ASC_inc_low_escort,0.473325448826442,F +knr_transit_ASC_inc_low_othdiscr,0.3960804410090851,F +knr_transit_ASC_inc_low_othmaint,0.473325448826442,F +knr_transit_ASC_inc_low_school,1.2652102812649755,F +knr_transit_ASC_inc_low_shopping,0.473325448826442,F +knr_transit_ASC_inc_low_social,0.3960804410090851,F +knr_transit_ASC_inc_low_univ,2.0592780276713056,F +knr_transit_ASC_inc_low_work,0.7015730545809349,F +knr_transit_ASC_inc_low_atwork,-8.0,F +knr_transit_ASC_inc_mid_eatout,-1.25655658411655,F +knr_transit_ASC_inc_mid_escort,-0.7338367086680577,F +knr_transit_ASC_inc_mid_othdiscr,-1.25655658411655,F +knr_transit_ASC_inc_mid_othmaint,-0.7338367086680577,F +knr_transit_ASC_inc_mid_school,-0.7317745333754034,F +knr_transit_ASC_inc_mid_shopping,-0.7338367086680577,F +knr_transit_ASC_inc_mid_social,-1.25655658411655,F +knr_transit_ASC_inc_mid_univ,1.3116869811404306,F +knr_transit_ASC_inc_mid_work,-0.3848852965876042,F +knr_transit_ASC_inc_mid_atwork,-9.467438153368075,F +knr_transit_ASC_inc_high_eatout,-0.8036452570070582,F +knr_transit_ASC_inc_high_escort,-3.7264828267495282,F +knr_transit_ASC_inc_high_othdiscr,-0.8036452570070582,F +knr_transit_ASC_inc_high_othmaint,-3.7264828267495282,F +knr_transit_ASC_inc_high_school,-2.555396666750429,F +knr_transit_ASC_inc_high_shopping,-3.7264828267495282,F +knr_transit_ASC_inc_high_social,-0.8036452570070582,F +knr_transit_ASC_inc_high_univ,3.054602894113314,F +knr_transit_ASC_inc_high_work,-2.489908212600246,F +knr_transit_ASC_inc_high_atwork,-14.0,F +joint_walk_transit_ASC_inc_low_all,1.2653105263763236,F +joint_walk_transit_ASC_inc_mid_all,0.9689702099436578,F +joint_walk_transit_ASC_inc_high_all,-0.40494433479936387,F +joint_pnr_transit_ASC_inc_low_all,5.0707139490363975,F +joint_pnr_transit_ASC_inc_mid_all,1.2303917824356008,F +joint_pnr_transit_ASC_inc_high_all,0.33304098953890393,F +joint_knr_transit_ASC_inc_low_all,-0.9672797857327882,F +joint_knr_transit_ASC_inc_mid_all,-0.37375387945065697,F +joint_knr_transit_ASC_inc_high_all,-3.561907968837347,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_template.csv old mode 100755 new mode 100644 similarity index 78% rename from activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv rename to activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_template.csv index 347e23e8f..f25be4f13 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv +++ b/activitysim/examples/production_semcog/configs/tour_mode_choice_coeffs_template.csv @@ -12,9 +12,6 @@ coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACC coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork -coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork -coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork -coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork @@ -83,15 +80,33 @@ joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,jo local_ASC,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_work,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork premium_ASC,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_work,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork mix_ASC,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_work,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork schoolbus_ASC_no_auto,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_no_auto_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school schoolbus_ASC_auto_deficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_deficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school schoolbus_ASC_auto_sufficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_sufficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school +ridehail_ASC_no_auto,ridehail_ASC_no_auto_eatout,ridehail_ASC_no_auto_escort,ridehail_ASC_no_auto_othdiscr,ridehail_ASC_no_auto_othmaint,ridehail_ASC_no_auto_school,ridehail_ASC_no_auto_shopping,ridehail_ASC_no_auto_social,ridehail_ASC_no_auto_univ,ridehail_ASC_no_auto_work,ridehail_ASC_no_auto_atwork +ridehail_ASC_auto_deficient,ridehail_ASC_auto_deficient_eatout,ridehail_ASC_auto_deficient_escort,ridehail_ASC_auto_deficient_othdiscr,ridehail_ASC_auto_deficient_othmaint,ridehail_ASC_auto_deficient_school,ridehail_ASC_auto_deficient_shopping,ridehail_ASC_auto_deficient_social,ridehail_ASC_auto_deficient_univ,ridehail_ASC_auto_deficient_work,ridehail_ASC_auto_deficient_atwork +ridehail_ASC_auto_sufficient,ridehail_ASC_auto_sufficient_eatout,ridehail_ASC_auto_sufficient_escort,ridehail_ASC_auto_sufficient_othdiscr,ridehail_ASC_auto_sufficient_othmaint,ridehail_ASC_auto_sufficient_school,ridehail_ASC_auto_sufficient_shopping,ridehail_ASC_auto_sufficient_social,ridehail_ASC_auto_sufficient_univ,ridehail_ASC_auto_sufficient_work,ridehail_ASC_auto_sufficient_atwork +joint_ridehail_ASC_no_auto,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all,joint_ridehail_ASC_no_auto_all +joint_ridehail_ASC_auto_deficient,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all,joint_ridehail_ASC_auto_deficient_all +joint_ridehail_ASC_auto_sufficient,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all,joint_ridehail_ASC_auto_sufficient_all +walk_transit_ASC_inc_low,walk_transit_ASC_inc_low_eatout,walk_transit_ASC_inc_low_escort,walk_transit_ASC_inc_low_othdiscr,walk_transit_ASC_inc_low_othmaint,walk_transit_ASC_inc_low_school,walk_transit_ASC_inc_low_shopping,walk_transit_ASC_inc_low_social,walk_transit_ASC_inc_low_univ,walk_transit_ASC_inc_low_work,walk_transit_ASC_inc_low_atwork +walk_transit_ASC_inc_mid,walk_transit_ASC_inc_mid_eatout,walk_transit_ASC_inc_mid_escort,walk_transit_ASC_inc_mid_othdiscr,walk_transit_ASC_inc_mid_othmaint,walk_transit_ASC_inc_mid_school,walk_transit_ASC_inc_mid_shopping,walk_transit_ASC_inc_mid_social,walk_transit_ASC_inc_mid_univ,walk_transit_ASC_inc_mid_work,walk_transit_ASC_inc_mid_atwork +walk_transit_ASC_inc_high,walk_transit_ASC_inc_high_eatout,walk_transit_ASC_inc_high_escort,walk_transit_ASC_inc_high_othdiscr,walk_transit_ASC_inc_high_othmaint,walk_transit_ASC_inc_high_school,walk_transit_ASC_inc_high_shopping,walk_transit_ASC_inc_high_social,walk_transit_ASC_inc_high_univ,walk_transit_ASC_inc_high_work,walk_transit_ASC_inc_high_atwork +pnr_transit_ASC_inc_low,pnr_transit_ASC_inc_low_eatout,pnr_transit_ASC_inc_low_escort,pnr_transit_ASC_inc_low_othdiscr,pnr_transit_ASC_inc_low_othmaint,pnr_transit_ASC_inc_low_school,pnr_transit_ASC_inc_low_shopping,pnr_transit_ASC_inc_low_social,pnr_transit_ASC_inc_low_univ,pnr_transit_ASC_inc_low_work,pnr_transit_ASC_inc_low_atwork +pnr_transit_ASC_inc_mid,pnr_transit_ASC_inc_mid_eatout,pnr_transit_ASC_inc_mid_escort,pnr_transit_ASC_inc_mid_othdiscr,pnr_transit_ASC_inc_mid_othmaint,pnr_transit_ASC_inc_mid_school,pnr_transit_ASC_inc_mid_shopping,pnr_transit_ASC_inc_mid_social,pnr_transit_ASC_inc_mid_univ,pnr_transit_ASC_inc_mid_work,pnr_transit_ASC_inc_mid_atwork +pnr_transit_ASC_inc_high,pnr_transit_ASC_inc_high_eatout,pnr_transit_ASC_inc_high_escort,pnr_transit_ASC_inc_high_othdiscr,pnr_transit_ASC_inc_high_othmaint,pnr_transit_ASC_inc_high_school,pnr_transit_ASC_inc_high_shopping,pnr_transit_ASC_inc_high_social,pnr_transit_ASC_inc_high_univ,pnr_transit_ASC_inc_high_work,pnr_transit_ASC_inc_high_atwork +knr_transit_ASC_inc_low,knr_transit_ASC_inc_low_eatout,knr_transit_ASC_inc_low_escort,knr_transit_ASC_inc_low_othdiscr,knr_transit_ASC_inc_low_othmaint,knr_transit_ASC_inc_low_school,knr_transit_ASC_inc_low_shopping,knr_transit_ASC_inc_low_social,knr_transit_ASC_inc_low_univ,knr_transit_ASC_inc_low_work,knr_transit_ASC_inc_low_atwork +knr_transit_ASC_inc_mid,knr_transit_ASC_inc_mid_eatout,knr_transit_ASC_inc_mid_escort,knr_transit_ASC_inc_mid_othdiscr,knr_transit_ASC_inc_mid_othmaint,knr_transit_ASC_inc_mid_school,knr_transit_ASC_inc_mid_shopping,knr_transit_ASC_inc_mid_social,knr_transit_ASC_inc_mid_univ,knr_transit_ASC_inc_mid_work,knr_transit_ASC_inc_mid_atwork +knr_transit_ASC_inc_high,knr_transit_ASC_inc_high_eatout,knr_transit_ASC_inc_high_escort,knr_transit_ASC_inc_high_othdiscr,knr_transit_ASC_inc_high_othmaint,knr_transit_ASC_inc_high_school,knr_transit_ASC_inc_high_shopping,knr_transit_ASC_inc_high_social,knr_transit_ASC_inc_high_univ,knr_transit_ASC_inc_high_work,knr_transit_ASC_inc_high_atwork +joint_walk_transit_ASC_inc_low,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all,joint_walk_transit_ASC_inc_low_all +joint_walk_transit_ASC_inc_mid,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all,joint_walk_transit_ASC_inc_mid_all +joint_walk_transit_ASC_inc_high,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all,joint_walk_transit_ASC_inc_high_all +joint_pnr_transit_ASC_inc_low,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all,joint_pnr_transit_ASC_inc_low_all +joint_pnr_transit_ASC_inc_mid,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all,joint_pnr_transit_ASC_inc_mid_all +joint_pnr_transit_ASC_inc_high,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all,joint_pnr_transit_ASC_inc_high_all +joint_knr_transit_ASC_inc_low,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all,joint_knr_transit_ASC_inc_low_all +joint_knr_transit_ASC_inc_mid,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all,joint_knr_transit_ASC_inc_mid_all +joint_knr_transit_ASC_inc_high,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all,joint_knr_transit_ASC_inc_high_all +coef_transit_district15,coef_transit_district15_work,coef_transit_district15_univ,coef_transit_district15_school,coef_transit_district15_escort,coef_transit_district15_shopping,coef_transit_district15_eatout,coef_transit_district15_othmaint,coef_transit_district15_social,coef_transit_district15_othdiscr,coef_transit_district15_atwork \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork.csv old mode 100755 new mode 100644 similarity index 90% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_atwork.csv index c0166ffa5..c1e9adbbe --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork.csv @@ -19,22 +19,22 @@ util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat- util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period util__Departure_constants,# Departure constants,,coef__Departure_constants util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear -util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_Before_1100_AM -util_1100_AM_1130_AM,11:00 AM - 11:30 AM,@(df.start==17),coef_1100_AM_1130_AM -util_1130_AM_1200_PM,11:30 AM - 12:00 PM,@(df.start==18),coef_1130_AM_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.start==19),coef_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.start==20),coef_1230_PM_0100_PM -util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_After_0100_PM +util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_start_Before_1100_AM +util_1100_AM_1130_AM_start,11:00 AM - 11:30 AM,@(df.start==17),coef_start_1100_AM_1130_AM +util_1130_AM_1200_PM_start,11:30 AM - 12:00 PM,@(df.start==18),coef_start_1130_AM_1200_PM +util_1200_AM_1230_PM_start,12:00 AM - 12:30 PM,@(df.start==19),coef_start_1200_AM_1230_PM +util_1230_PM_0100_PM_start,12:30 PM - 01:00 PM,@(df.start==20),coef_start_1230_PM_0100_PM +util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_start_After_0100_PM util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root util__Arrival_constants,# Arrival constants,,coef__Arrival_constants util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear -util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_Before_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.end==19),coef_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.end==20),coef_1230_PM_0100_PM -util_0100_PM_0130_PM,01:00 PM - 01:30 PM,@(df.end==21),coef_0100_PM_0130_PM -util_0130_PM_0200_PM,01:30 PM - 02:00 PM,@(df.end==22),coef_0130_PM_0200_PM -util_0200_PM_0230_PM,02:00 PM - 02:30 PM,@(df.end==23),coef_0200_PM_0230_PM -util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_After_0230_PM +util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_end_Before_1200_PM +util_1200_AM_1230_PM_end,12:00 AM - 12:30 PM,@(df.end==19),coef_end_1200_AM_1230_PM +util_1230_PM_0100_PM_end,12:30 PM - 01:00 PM,@(df.end==20),coef_end_1230_PM_0100_PM +util_0100_PM_0130_PM_end,01:00 PM - 01:30 PM,@(df.end==21),coef_end_0100_PM_0130_PM +util_0130_PM_0200_PM_end,01:30 PM - 02:00 PM,@(df.end==22),coef_end_0130_PM_0200_PM +util_0200_PM_0230_PM_end,02:00 PM - 02:30 PM,@(df.end==23),coef_end_0200_PM_0230_PM +util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_end_After_0230_PM util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear util__Duration_constants,# Duration constants,,coef__Duration_constants util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.yaml b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.yaml rename to activitysim/examples/production_semcog/configs/tour_scheduling_atwork.yaml diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork_coeffs.csv old mode 100755 new mode 100644 similarity index 81% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_atwork_coeffs.csv index 457c9e4b8..0e7ab1acb --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork_coeffs.csv @@ -18,21 +18,21 @@ coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F -coef_Before_1100_AM,-2.176744062,F -coef_1100_AM_1130_AM,-1.190017952,F -coef_1130_AM_1200_PM,-0.198229872,F -#coef_1200_AM_1230_PM,0,T -#coef_1230_PM_0100_PM,-0.084950396,F -coef_After_0100_PM,-0.205562723,F +coef_start_Before_1100_AM,-2.176744062,F +coef_start_1100_AM_1130_AM,-1.190017952,F +coef_start_1130_AM_1200_PM,-0.198229872,F +coef_start_1200_AM_1230_PM,0,T +coef_start_1230_PM_0100_PM,-0.084950396,F +coef_start_After_0100_PM,-0.205562723,F coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F -coef_Before_1200_PM,0.279351638,F -coef_1200_AM_1230_PM,-0.045281832,F -coef_1230_PM_0100_PM,0.214070736,F -coef_0100_PM_0130_PM,0,T -coef_0130_PM_0200_PM,-0.69742748,F -coef_0200_PM_0230_PM,-1.284283533,F -coef_After_0230_PM,-2.119733896,F +coef_end_Before_1200_PM,0.279351638,F +coef_end_1200_AM_1230_PM,-0.045281832,F +coef_end_1230_PM_0100_PM,0.214070736,F +coef_end_0100_PM_0130_PM,0,T +coef_end_0130_PM_0200_PM,-0.69742748,F +coef_end_0200_PM_0230_PM,-1.284283533,F +coef_end_After_0230_PM,-2.119733896,F coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F coef_0_hrs,-0.969734874,F coef_0p5_hrs,0,T diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_atwork_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_preprocessor.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_atwork_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_joint.csv old mode 100755 new mode 100644 similarity index 52% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_joint.csv index 351eaf77e..8e3a276e9 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_joint.csv @@ -1,102 +1,27 @@ Label,Description,Expression,Coefficient -,,, -#ESCORT,#ESCORT,,ESCORT -util_escort_Mode_Choice_Logsum,ESCORT - Mode Choice Logsum,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort')), df.mode_choice_logsum, 0)",coef_escort_Mode_Choice_Logsum -util_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,ESCORT - Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0))),0)",coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_escort_Distance_to_destination_Duration_greater_than_0p5_hours,ESCORT - Distance to destination - Duration greater than 0.5 hours,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours -util_escort_Fulltime_worker_Departure_after_8_00_am_Linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear -util_escort_Fulltime_worker_Departure_after_3_00_am_Linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear -util_escort_Fulltime_worker_Duration_lt_0p5_hrs,ESCORT - Full-time worker - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Fulltime_worker_Duration_lt_0p5_hrs -util_escort_Fulltime_worker_Duration_gt_0p5_hrs,ESCORT - Full-time worker - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Fulltime_worker_Duration_gt_0p5_hrs -util_escort_University_student_Duration_lt_0p5_hrs,ESCORT - University student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_University_student_Duration_lt_0p5_hrs -util_escort_Nondriving_age_student_Duration_gt_0p5_hrs,ESCORT - Non-driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs -util_escort_Driving_age_student_Duration_lt_0p5_hrs,ESCORT - Driving age student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Driving_age_student_Duration_lt_0p5_hrs -util_escort_Driving_age_student_Duration_gt_0p5_hrs,ESCORT - Driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Driving_age_student_Duration_gt_0p5_hrs -util_escort_Preschool_kid_Duration_gt_0p5_hrs,ESCORT - Pre-school kid - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Preschool_kid_Duration_gt_0p5_hrs -util_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs -util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start >10)), (np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) &( df.end>25)), (np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM -util_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs -#FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run,#FIXME - Number of non-escort tours is not known until the non-mandatory frequency model is run,,FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run -#util_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,#ESCORT - Number of Individual Tours (excluding escorting) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type != 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs -util_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,ESCORT - Number of joint tours - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs -util_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear -util_escort_Departure_Constant_Before_07_00_AM,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<9)),coef_escort_Departure_Constant_Before_07_00_AM -util_escort_Departure_Constant_07_00_AM_07_30_AM,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Departure_Constant_07_00_AM_07_30_AM -util_escort_Departure_Constant_07_30_AM_08_00_AM,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Departure_Constant_07_30_AM_08_00_AM -util_escort_Departure_Constant_08_00_AM_08_30_AM,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==11)),coef_escort_Departure_Constant_08_00_AM_08_30_AM -util_escort_Departure_Constant_08_30_AM_09_00_AM,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==12)),coef_escort_Departure_Constant_08_30_AM_09_00_AM -util_escort_Departure_Constant_After_09_00_AM,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>12)),coef_escort_Departure_Constant_After_09_00_AM -util_escort_Departure_Constant_01_30_PM_02_00_PM,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==22)),coef_escort_Departure_Constant_01_30_PM_02_00_PM -util_escort_Departure_Constant_02_00_PM_02_30_PM,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Departure_Constant_02_00_PM_02_30_PM -util_escort_Departure_Constant_02_30_PM_03_00_PM,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Departure_Constant_02_30_PM_03_00_PM -util_escort_Departure_Constant_03_00_PM_03_30_PM,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==25)),coef_escort_Departure_Constant_03_00_PM_03_30_PM -util_escort_Departure_Constant_After_03_30_PM,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>25)),coef_escort_Departure_Constant_After_03_30_PM -util_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear -util_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear -util_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear -util_escort_Arrival_Constant_Before_07_00_AM,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<9)),coef_escort_Arrival_Constant_Before_07_00_AM -util_escort_Arrival_Constant_07_00_AM_07_30_AM,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==9)),coef_escort_Arrival_Constant_07_00_AM_07_30_AM -util_escort_Arrival_Constant_07_30_AM_08_00_AM,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==10)),coef_escort_Arrival_Constant_07_30_AM_08_00_AM -util_escort_Arrival_Constant_08_00_AM_08_30_AM,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==11)),coef_escort_Arrival_Constant_08_00_AM_08_30_AM -util_escort_Arrival_Constant_08_30_AM_09_00_AM,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==12)),coef_escort_Arrival_Constant_08_30_AM_09_00_AM -util_escort_Arrival_Constant_After_09_00_AM,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>12)),coef_escort_Arrival_Constant_After_09_00_AM -util_escort_Arrival_Constant_02_30_PM_03_00_PM,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==24)),coef_escort_Arrival_Constant_02_30_PM_03_00_PM -util_escort_Arrival_Constant_03_00_PM_03_30_PM,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==25)),coef_escort_Arrival_Constant_03_00_PM_03_30_PM -util_escort_Arrival_Constant_03_30_PM_04_00_PM,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==26)),coef_escort_Arrival_Constant_03_30_PM_04_00_PM -util_escort_Arrival_Constant_04_00_PM_04_30_PM,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==27)),coef_escort_Arrival_Constant_04_00_PM_04_30_PM -util_escort_Arrival_Constant_After_04_30_PM,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>27)),coef_escort_Arrival_Constant_After_04_30_PM -util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear -util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear -util_escort_Duration_Constant_0_hrs,ESCORT - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Duration_Constant_0_hrs -util_escort_Duration_Constant_0p5_hrs,ESCORT - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Duration_Constant_0p5_hrs -util_escort_Duration_Constant_1_hrs,ESCORT - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==2)),coef_escort_Duration_Constant_1_hrs -util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==3)),coef_escort_Duration_Constant_1p5hrs -util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs -util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs -util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1 -util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 -util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9 -util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10 -util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23 -util_escort_Calibration_Constant_Departure_eq_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Calibration_Constant_Departure_eq_24 #SHOPPING,#SHOPPING,,SHOPPING -util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear -util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear -util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs -util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs -util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr -util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs -util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,27), 0)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr -util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs -util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2)), ((np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance)), 0)",coef_shop_Distance_Duration_gt_1p5_hr -util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear -util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root +util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear +util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), ((15-df.start)*(df.start<=15) + (df.start-15)*(df.start>15)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear +util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & (df.composition=='adults')), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs +util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_gt_1p5_hr +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM -util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared -util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM @@ -107,34 +32,34 @@ util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM -util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear +util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs -util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear -util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 #MAINTENANCE,#MAINTENANCE,,MAINTENANCE -util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear -util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear -util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs -util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs -util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr -util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs -util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr -util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr -util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs -util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr -util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear -util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root +util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear +util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear +util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & (df.composition=='adults')), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr +util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > reference","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs +util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM @@ -143,9 +68,8 @@ util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM -util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared -util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM @@ -154,55 +78,55 @@ util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01 util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM -util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs -util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear -util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root +util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear +util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 #EATOUT,#EAT-OUT,,EATOUT -util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs -util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs -util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs -util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs -util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs -util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs -util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs +util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs +util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs +util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs +util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs +util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM -util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM -util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM -util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM -util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear -util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,12), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more -util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,13), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 @@ -216,45 +140,45 @@ util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 #SOCIAL,#SOCIAL,,SOCIAL -util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear -util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear -util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear -util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear -util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear -util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) * (df.origin_to_destination_distance)), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear -util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0) * (df.origin_to_destination_distance)), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear -util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & ((df.composition=='children')|(df.composition=='mixed'))), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > reference,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4) & (df.number_of_participants > 2)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear +util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > reference - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM -util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM -util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM -util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear +util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM -util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear -util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear +util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more -util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)),coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 @@ -265,17 +189,17 @@ util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - D util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 #DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY -util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear -util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear -util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear -util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear -util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear -util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear -util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear -util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear +util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear +util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), ((3-df.duration)*(df.duration<=3) + (df.duration-3)*(df.duration>3)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM @@ -283,15 +207,15 @@ util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Consta util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM -util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM -util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear -util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM @@ -299,14 +223,14 @@ util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM -util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more -util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,47), 0)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_joint_coeffs.csv new file mode 100644 index 000000000..7b7374f78 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_joint_coeffs.csv @@ -0,0 +1,239 @@ +coefficient_name,value,constrain +coef_shop_mode_choice_logsum,0.266590021,F +coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,0,F +coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0,F +coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0,F +coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0,F +coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0,F +coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0,F +coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0,F +coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0,F +coef_shop_Distance_Duration_lt_1p5_hrs,-0.07242029,F +coef_shop_Distance_Duration_gt_1p5_hr,0.013215963,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.097160499,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,0,F +coef_shop_Departure_Constant_Before_09_00_AM,0,F +coef_shop_Departure_Constant_09_00_AM_09_30_AM,0,F +coef_shop_Departure_Constant_09_30_AM_10_00_AM,0,F +coef_shop_Departure_Constant_10_00_AM_10_30_AM,0,T +coef_shop_Departure_Constant_10_30_AM_11_00_AM,0,F +coef_shop_Departure_Constant_After_11_00_AM,0,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.178109475,F +coef_shop_Arrival_Constant_Before_12_30_PM,1.33565007,F +coef_shop_Arrival_Constant_12_30_PM_03_00_PM,1.123236175,F +coef_shop_Arrival_Constant_03_00_PM_03_30_PM,1.110617469,F +coef_shop_Arrival_Constant_03_30_PM_04_00_PM,0.588845741,F +coef_shop_Arrival_Constant_04_00_PM_04_30_PM,0.629754019,T +coef_shop_Arrival_Constant_04_30_PM_05_00_PM,0.741900363,F +coef_shop_Arrival_Constant_05_00_PM_05_30_PM,0.868235362,F +coef_shop_Arrival_Constant_05_30_PM_07_00_PM,0.689643357,F +coef_shop_Arrival_Constant_07_00_PM_09_30_PM,0,F +coef_shop_Arrival_Constant_After_09_30_PM,-0.774761875,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.763104981,F +coef_shop_Duration_Constant_0_hrs,-1.505127063,F +coef_shop_Duration_Constant_0p5_hrs,0,F +coef_shop_Duration_Constant_1_hrs,0,T +coef_shop_Duration_Constant_1p5hrs,0.036277026,F +coef_shop_Duration_Constant_2_hrs,-0.155375923,F +coef_shop_Duration_Constant_Longer_than_2_hrs,0,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.532837138,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,0,F +coef_shop_Calibration_Constant_Duration_eq_1,0,F +coef_shop_Calibration_Constant_Duration_eq_2,0,F +coef_shop_Calibration_Constant_Duration_eq_3,0,F +coef_maint_mode_choice_logsum,0.203572728,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.499941406,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,0.136476976,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,0,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0,F +coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,0,F +coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0,F +coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0,F +coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0,T +coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,0,F +coef_maint_Distance_Duration_lt_1p5_hrs,-0.101359184,F +coef_maint_Distance_Duration_gt_1p5_hr,0.008486296,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0,F +coef_maint_Departure_Constant_Before_08_00_AM,0,F +coef_maint_Departure_Constant_08_00_AM_08_30_AM,0,F +coef_maint_Departure_Constant_08_30_AM_09_00_AM,0,F +coef_maint_Departure_Constant_09_00_AM_09_30_AM,0,F +coef_maint_Departure_Constant_09_30_AM_10_00_AM,0,F +coef_maint_Departure_Constant_10_00_AM_10_30_AM,0,T +coef_maint_Departure_Constant_10_30_AM_11_00_AM,0,F +coef_maint_Departure_Constant_After_11_00_AM,0,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,0,F +coef_maint_Arrival_Constant_Before_10_30_AM,1.05775869,F +coef_maint_Arrival_Constant_10_30_AM_11_00_AM,1.412605821,T +coef_maint_Arrival_Constant_11_00_AM_11_30_AM,1.495880083,F +coef_maint_Arrival_Constant_11_30_AM_01_30_PM,0.645555572,F +coef_maint_Arrival_Constant_01_30_PM_02_30_PM,0.326964553,F +coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0,F +coef_maint_Arrival_Constant_04_00_PM_04_30_PM,-0.284931133,F +coef_maint_Arrival_Constant_After_04_30_PM,-0.237568932,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.426275674,F +coef_maint_Duration_Constant_0_hrs,0,F +coef_maint_Duration_Constant_0p5_hrs,0,T +coef_maint_Duration_Constant_Longer_than_0p5_hrs,0,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,-0.141561305,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0,F +coef_maint_Calibration_Constant_Duration_eq_1,0,F +coef_maint_Calibration_Constant_Duration_eq_2,0,F +coef_maint_Calibration_Constant_Duration_eq_3,0,F +coef_maint_Calibration_Constant_Duration_eq_4,0,F +coef_maint_Calibration_Constant_Duration_eq_5,0,F +coef_eatout_mode_choice_logsum,0.276069583,F +coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.200140039,F +coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.012134828,F +coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,0,F +coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,-1.311691471,F +coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0,F +coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.429062459,F +coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.169442555,F +coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.979928772,F +coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.690690081,F +coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.524666101,F +coef_eatout_Departure_Constant_Before_05_30_PM,-1.029657256,F +coef_eatout_Departure_Constant_05_30_PM_06_00_PM,0,F +coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0,T +coef_eatout_Departure_Constant_06_30_PM_07_00_PM,0.139897449,F +coef_eatout_Departure_Constant_07_00_PM_07_30_PM,0.630991142,F +coef_eatout_Departure_Constant_After_07_30_PM,0.019003231,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0.014319105,F +coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0,F +coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,1.067038322,F +coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,1.041393427,F +coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,0.363114304,F +coef_eatout_Arrival_Constant_Before_7_00_PM,1.318588562,F +coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,0.649317909,F +coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,0.741190644,F +coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T +coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.209479704,F +coef_eatout_Arrival_Constant_After_09_00_PM,0,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.985149909,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,0,F +coef_eatout_Duration_Constant_0_hours,-2.011019161,F +coef_eatout_Duration_Constant_0p5_hous,-0.318897197,F +coef_eatout_Duration_Constant_1_hour,0,T +coef_eatout_Duration_Constant_1p5_hours,0.066432668,F +coef_eatout_Duration_Constant_2_hours_or_more,-0.179946094,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,0,F +coef_eatout_Calibration_Constant_Duration_eq_1,0,F +coef_eatout_Calibration_Constant_Duration_eq_2,0,F +coef_eatout_Calibration_Constant_Duration_eq_3,0,F +coef_eatout_Calibration_Constant_Duration_eq_4,0,F +coef_eatout_Calibration_Constant_Departure_eq_1,0,F +coef_eatout_Calibration_Constant_Departure_eq_2,0,F +coef_eatout_Calibration_Constant_Departure_eq_3,0,F +coef_eatout_Calibration_Constant_Departure_eq_17,0,F +coef_eatout_Calibration_Constant_Departure_eq_18,0,F +coef_eatout_Calibration_Constant_Departure_eq_19,0,F +coef_eatout_Calibration_Constant_Departure_eq_20,0,F +coef_eatout_Calibration_Constant_Departure_eq_21,0,F +coef_social_mode_choice_logsum,0.158037279,F +coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0,F +coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0,F +coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0,F +coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0,F +coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0,F +coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0,F +coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.086087228,F +coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.007353328,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-1.252776126,F +coef_social_Departure_Constant_Before_09_00_AM,0,F +coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.343527796,F +coef_social_Departure_Constant_Before_05_30_PM,-1.65532635,F +coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.479101805,F +coef_social_Departure_Constant_06_00_PM_06_30_PM,0,T +coef_social_Departure_Constant_06_30_PM_07_00_PM,0,F +coef_social_Departure_Constant_07_00_PM_07_30_PM,0.247162691,F +coef_social_Departure_Constant_After_07_30_PM,0.117283002,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,0,F +coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0,F +coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0,F +coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0,F +coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.338514849,F +coef_social_Arrival_Constant_Before_8_30_PM,1.739447864,F +coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,1.225479315,F +coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,1.333432358,F +coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0,T +coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,0,F +coef_social_Arrival_Constant_After_10_30_PM,-1.092590046,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-0.831684531,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,-0.621915194,F +coef_social_Duration_Constant_Less_than_2_hours,0,F +coef_social_Duration_Constant_2_hours,0,F +coef_social_Duration_Constant_2p5_hours,0.420817953,T +coef_social_Duration_Constant_3_hours_or_more,0.586220378,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,0.067970995,F +coef_social_Calibration_Constant_Duration_eq_1,0,F +coef_social_Calibration_Constant_Duration_eq_2,0,F +coef_social_Calibration_Constant_Duration_eq_3,0,F +coef_social_Calibration_Constant_Duration_eq_4,0,F +coef_social_Calibration_Constant_Duration_eq_5,0,F +coef_social_Calibration_Constant_Duration_eq_6,0,F +coef_social_Calibration_Constant_Duration_eq_7,0,F +coef_social_Calibration_Constant_Duration_eq_8,0,F +coef_social_Calibration_Constant_Duration_eq_9,0,F +coef_disc_mode_choice_logsum,0.074657942,F +coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,0,F +coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,0,F +coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.293318964,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0,F +coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,0,F +coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,0,F +coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0,F +coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.320551359,F +coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.00678766,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.524087818,F +coef_disc_Departure_Constant_Before_7_30_AM_,0,F +coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,0,F +coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,0.629158335,F +coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,1.707834162,F +coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,1.475746817,F +coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,1.374276946,F +coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,1.399516534,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-2.047979958,F +coef_disc_Departure_Constant_Before_05_00_PM,-6.561139927,F +coef_disc_Departure_Constant_05_00_PM_05_30_PM,-4.39928374,F +coef_disc_Departure_Constant_05_30_PM_06_00_PM,-2.074501744,F +coef_disc_Departure_Constant_06_00_PM_06_30_PM,0,F +coef_disc_Departure_Constant_06_30_PM_07_00_PM,1.789567201,T +coef_disc_Departure_Constant_After_07_00_PM,2.98340404,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,1.428288831,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,1.984943283,F +coef_disc_Arrival_Constant_Before_6_30_PM,9.122741092,F +coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,7.491788267,F +coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,5.580592347,F +coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,3.979242005,F +coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,2.04931473,T +coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,0,F +coef_disc_Arrival_Constant_After_9_00_PM,-2.339078095,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-2.486129189,F +coef_disc_Duration_Constant_0_hours,-7.089453421,F +coef_disc_Duration_Constant_0p5_hous,-3.558800588,F +coef_disc_Duration_Constant_1_hour,-1.148558894,F +coef_disc_Duration_Constant_1p5_hours,0,F +coef_disc_Duration_Constant_2_hours,1.831185197,T +coef_disc_Duration_Constant_2p5_hours_or_more,3.582104009,F +coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,1.684187282,F +coef_disc_Calibration_Constant_Duration_eq_4,0,F +coef_disc_Calibration_Constant_Duration_eq_5,0,F +coef_disc_Calibration_Constant_Departure_eq_29,0,F +coef_disc_Calibration_Constant_Departure_eq_30,0,F +coef_disc_Calibration_Constant_Departure_eq_31,0,F +coef_disc_Calibration_Constant_Departure_eq_32,0,F diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory.csv new file mode 100644 index 000000000..0c5c1fab7 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory.csv @@ -0,0 +1,94 @@ +Label,Description,Expression,Coefficient +util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects +util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects +util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects +util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects +util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects +util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects +util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects +util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects +util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects +util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect +util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 +util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 +util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours +util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours +util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 +#,,, +#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum +#,,,# +util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour +util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours +#,#,,# +util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early +util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 +util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 +util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 +util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 +util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 +util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 +util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak +util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening +util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late +util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early +util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak +util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 +util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 +util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 +util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 +util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 +util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 +util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening +util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late +util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours +util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours +util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours +util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours +util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours +util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours +util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours \ No newline at end of file diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv new file mode 100644 index 000000000..11120c659 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv @@ -0,0 +1,94 @@ +coefficient_name,value,constrain +coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,T +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.004741,F +coef_shopping_tour_departure_shift_effects,-0.06015,F +coef_shopping_tour_duration_shift_effects,-0.1208,F +coef_maintenance_tour_departure_shift_effects,-0.1489,F +coef_maintenance_tour_duration_shift_effects,-0.08372,F +coef_visit_tour_departure_shift_effects,0.09688,F +coef_visit_tour_duration_shift_effects,0.1638,F +coef_eat_out_tour_departure_shift_effects,0.07549,F +coef_school_child_age_16_plus_departure_shift_effects,0.07266,F +coef_school_child_age_16_plus_duration_shift_effects,0.2095,F +coef_school_child_age_under_16_departure_shift_effects,0.04657,F +coef_school_child_age_under_16_duration_shift_effects,0.3272,F +coef_destination_in_cbd_duration_shift_effects,0.1067,F +coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F +coef_number_of_joint_tours_departure_shift_effects,0.05208,F +coef_number_of_escort_tours_departure_shift_effects,0.02013,F +coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.03896,F +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F +coef_maintenance_tour_depart_before_7,-0.8826,F +coef_shopping_tour_depart_before_8,-1.037,F +coef_shopping_tour_arrive_after_22,-0.6027,F +coef_school_child_under_16_arrive_after_22,-1.18,F +coef_university_student_arrive_after_22,0.5466,F +coef_shopping_tour_duration_lt_2_hours,0.5168,F +coef_discretionary_tour_duration_lt_2_hours,-0.6974,F +coef_adult_with_children_in_hh_arrive_19_21,0.336,F +#,, +#mode_choice_logsum,, +#,, +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.0593,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.02734,F +coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,-13.63,F +#,,F +coef_departure_constants_early,-1.740135661,F +coef_departure_constants_am_peak_1,-0.654163573,F +coef_departure_constants_am_peak_2,0.554282571,F +coef_departure_constants_am_peak_3,1.050561087,F +coef_departure_constants_am_peak_4,0.971568228,F +coef_departure_constants_midday_1,0.881991986,F +coef_departure_constants_midday_2,0.411103634,F +coef_departure_constants_pm_peak,0,T +coef_departure_constants_evening,-1.856475096,F +coef_departure_constants_late,-8.228880141,F +coef_arrival_constants_early,-0.051990748,F +coef_arrival_constants_am_peak,-1.814822602,F +coef_arrival_constants_midday_1,0.000371501,F +coef_arrival_constants_midday_2,0.532116031,F +coef_arrival_constants_pm_peak_1,0.628481567,F +coef_arrival_constants_pm_peak_2,0.650521416,F +coef_arrival_constants_pm_peak_3,0.402894406,F +coef_arrival_constants_pm_peak_4,0.154213293,F +coef_arrival_constants_evening,0,T +coef_arrival_constants_late,-0.866671315,F +coef_duration_constants_0_to_1_hours,0,T +coef_duration_constants_2_to_3_hours,0.051385565,F +coef_duration_constants_4_to_5_hours,-0.593951321,F +coef_duration_constants_6_to_7_hours,-0.951155328,F +coef_duration_constants_8_to_10_hours,-0.828108399,F +coef_duration_constants_11_to_13_hours,-0.955635554,F +coef_duration_constants_14_to_18_hours,-1.042580879,F +coef_escort_tour_departure_constants_early,-1.740135661,F +coef_escort_tour_departure_constants_am_peak_1,-1.112357753,F +coef_escort_tour_departure_constants_am_peak_2,0.698788185,F +coef_escort_tour_departure_constants_am_peak_3,1.196268813,F +coef_escort_tour_departure_constants_am_peak_4,-0.225258221,F +coef_escort_tour_departure_constants_midday_1,0.028662017,F +coef_escort_tour_departure_constants_midday_2,0,T +coef_escort_tour_departure_constants_pm_peak,-1.180140161,F +coef_escort_tour_departure_constants_evening,-3.948732811,F +coef_escort_tour_departure_constants_late,-8.228880141,F +coef_escort_tour_arrival_constants_early,0,T +coef_escort_tour_arrival_constants_am_peak,0,T +coef_escort_tour_arrival_constants_midday_1,0,T +coef_escort_tour_arrival_constants_midday_2,0,T +coef_escort_tour_arrival_constants_pm_peak_1,0,T +coef_escort_tour_arrival_constants_pm_peak_2,0,T +coef_escort_tour_arrival_constants_pm_peak_3,0,T +coef_escort_tour_arrival_constants_pm_peak_4,0,T +coef_escort_tour_arrival_constants_evening,-0.536918728,F +coef_escort_tour_arrival_constants_late,-1.008290213,F +coef_escort_tour_duration_constants_0_to_1_hours,0,T +coef_escort_tour_duration_constants_2_to_3_hours,-2.042013897,F +coef_escort_tour_duration_constants_4_to_5_hours,-2.880293896,F +coef_escort_tour_duration_constants_6_to_7_hours,-2.973533731,F +coef_escort_tour_duration_constants_8_to_10_hours,-3.020213758,F +coef_escort_tour_duration_constants_11_to_13_hours,-2.974364976,F +coef_escort_tour_duration_constants_14_to_18_hours,-2.507447146,F \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout.csv similarity index 63% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout.csv index 4dde9e73d..b2eb08b66 100644 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout.csv @@ -1,15 +1,16 @@ Label,Description,Expression,Coefficient #EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_eatout_distance_to_destination_duration_less_than_ref,EAT-OUT - Distance to destination - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_ref +util_eatout_distance_to_destination_duration_greater_than_ref,EAT-OUT - Distance to destination - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_ref +util_eatout_low_income_duration_less_than_ref,EAT-OUT - Low income (<25000) - Duration < Reference,"@np.where(((df.is_income_less25K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_low_income_duration_less_than_ref +util_eatout_medium_income_duration_less_than_ref,EAT-OUT - Medium (25k to 60k) - Duration < Reference,"@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_medium_income_duration_less_than_ref +util_eatout_zeroauto_HH_duration_greater_than_ref,EAT-OUT - Zero auto HH - Duration > Reference,"@np.where(((df.auto_ownership == 0) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_ref +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.start>31) & (df.ptype == 3)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_ref,EAT-OUT - Female - Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_less_than_ref +util_eatout_female_duration_greater_than_ref,EAT-OUT - Female - Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_eatout_female_duration_greater_than_ref +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.start<31)), ((31-df.start)*(df.start<=31) + (df.start-31)*(df.start>31)) * (np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_ref,EAT-OUT - Time Pressure - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_ref util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am @@ -17,13 +18,13 @@ util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 1 util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.start<30)), ((30-df.start)*(df.start<=30) + (df.start-30)*(df.start>30)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.start<31)),coef_eatout_departure_constant_before_6_pm util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.start>34)), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm @@ -31,21 +32,21 @@ util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 P util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.end<32)), ((32-df.end)*(df.end<=32) + (df.end-32)*(df.end>32)), 0)",coef_eatout_arrival_constant_before_6_30_pm util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.duration==0)),coef_eatout_duration_constant_0_hour util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.duration==1)),coef_eatout_duration_constant_30_minutes util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.duration==2)),coef_eatout_duration_constant_1_hour util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.duration==4)),coef_eatout_duration_constant_2_hours util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where((df.duration>5), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.duration==0)),coef_eatout_calibration_constant_duration_1 util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.duration==1)),coef_eatout_calibration_constant_duration_2 util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.duration==2)),coef_eatout_calibration_constant_duration_3 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv new file mode 100644 index 000000000..e5e4530bb --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv @@ -0,0 +1,60 @@ +coefficient_name,value,constrain +coef_eatout_distance_to_destination_duration_less_than_ref,-0.22213088025234518,F +coef_eatout_distance_to_destination_duration_greater_than_ref,0.006882886760254519,F +coef_eatout_low_income_duration_less_than_ref,0.0,F +coef_eatout_medium_income_duration_less_than_ref,0.0,F +coef_eatout_zeroauto_HH_duration_greater_than_ref,0.0,F +coef_eatout_university_student_departure_after_7_pm_linear,0.0,F +coef_eatout_female_duration_less_than_ref,0.0,F +coef_eatout_female_duration_greater_than_ref,0.07587262787357908,F +coef_eatout_time_pressure_departure_before_6_30_pm,0.12152468184322475,F +coef_eatout_time_pressure_duration_less_than_ref,-1.1004669059917198,F +coef_eatout_departure_constant_7_30_am_to_9_am,0.5692134031282525,F +coef_eatout_departure_constant_10_30_am_to_11_am,0.8995439384100027,F +coef_eatout_departure_constant_11_am_to_11_30_am,1.0520049600035701,F +coef_eatout_departure_constant_11_30_am_to_12_pm,1.0552687311323523,F +coef_eatout_departure_constant_12_pm_to_12_30_pm,0.0,F +coef_eatout_departure_constant_12_30_pm_to_1_pm,0.5514341640403775,F +coef_eatout_departure_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.7733197656863885,F +coef_eatout_departure_constant_before_6_pm,1.2101753740729,F +coef_eatout_departure_constant_6_pm_to_6_30_pm,0.0,F +coef_eatout_departure_constant_6_30_pm_to_7_pm,2.01511052873696,T +coef_eatout_departure_constant_7_pm_to_7_30_pm,2.154439497883138,F +coef_eatout_departure_constant_after_7_30_pm,2.3557017509401206,F +coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,0.0,F +coef_eatout_arrival_constant_9_30_am_to_11_am,0.5477594779688595,F +coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.0,F +coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.0,F +coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.6150279185861535,F +coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.0,F +coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.0,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,1.1113521693562283,F +coef_eatout_arrival_constant_before_6_30_pm,0.31077849460990015,F +coef_eatout_arrival_constant_6_30_pm_to_7_pm,1.411751007495926,F +coef_eatout_arrival_constant_7_pm_to_7_30_pm,0.580944990788283,F +coef_eatout_arrival_constant_7_30_pm_to_8_pm,0.8068518512811886,F +coef_eatout_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_eatout_arrival_constant_8_30_pm_to_9_pm,0.0,F +coef_eatout_arrival_constant_after_9_pm,-0.45642114111135573,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.5535494562837153,F +coef_eatout_duration_constant_0_hour,7.7136294531232235,F +coef_eatout_duration_constant_30_minutes,5.243585849897661,F +coef_eatout_duration_constant_1_hour,0.0,F +coef_eatout_duration_constant_1_hour_30_minutes,1.2046725893713652,T +coef_eatout_duration_constant_2_hours,1.3298595485891556,F +coef_eatout_duration_constant_2_hour_30_minutes_or_more,1.4613341980210828,F +coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,0.0,F +coef_eatout_calibration_constant_duration_1,0.0,F +coef_eatout_calibration_constant_duration_2,0.0,F +coef_eatout_calibration_constant_duration_3,0.0,F +coef_eatout_calibration_constant_duration_4,0.0,F +coef_eatout_calibration_constant_departure_1,0.0,F +coef_eatout_calibration_constant_departure_2,0.0,F +coef_eatout_calibration_constant_departure_3,0.0,F +coef_eatout_calibration_constant_departure_17,0.0,F +coef_eatout_calibration_constant_departure_18,0.0,F +coef_eatout_calibration_constant_departure_19,0.0,F +coef_eatout_calibration_constant_departure_20,0.0,F +coef_eatout_calibration_constant_departure_21,0.0,F +coef_mode_choice_logsum,0.2,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort.csv similarity index 57% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort.csv index b07727992..2fb7649b1 100644 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort.csv @@ -2,39 +2,39 @@ Label,Description,Expression,Coefficient # ESCORT,,, util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum "#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.duration<1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.duration>1)), ((df.origin_to_destination_distance) * ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after Reference - Linear,"@np.where(((df.ptype == 1) & (df.start>9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear "#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_3_pm_linear,ESCORT - Full-time worker - Departure after 3:00 pm - Linear,"@np.where(((df.ptype == 1) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_fulltime_worker_departure_after_3_pm_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.ptype == 1) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.ptype == 3) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where((((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.ptype == 6) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes "#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.ptype == 8) & (df.duration<1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.num_children == 0) & (df.start<10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.num_children == 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.num_children == 0) & (df.start<24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.num_children == 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.num_children == 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.num_children == 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.num_children == 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.num_children == 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), ((24-df.start)*(df.start<=24) + (df.start-24)*(df.start>24)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), ((11-df.end)*(df.end<=11) + (df.end-11)*(df.end>11)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), ((25-df.end)*(df.end<=25) + (df.end-25)*(df.end>25)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.num_non_escort_tours > 0) & (df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.duration>1)), ((1-df.duration)*(df.duration<=1) + (df.duration-1)*(df.duration>1)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.start<8)), ((8-df.start)*(df.start<=8) + (df.start-8)*(df.start>8)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.start<9)),coef_escort_departure_constant_before_7_am util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am @@ -46,9 +46,9 @@ util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:0 util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.start>13)), ((13-df.start)*(df.start<=13) + (df.start-13)*(df.start>13)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.start>26)), ((26-df.start)*(df.start<=26) + (df.start-26)*(df.start>26)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.end<8)), ((8-df.end)*(df.end<=8) + (df.end-8)*(df.end>8)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.end<9)),coef_escort_arrival_constant_before_7_am util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am @@ -60,8 +60,8 @@ util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.end>13)), ((13-df.end)*(df.end<=13) + (df.end-13)*(df.end>13)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.duration==0)),coef_escort_duration_constant_0_hour util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.duration==1)),coef_escort_duration_constant_30_minutes util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.duration==2)),coef_escort_duration_constant_1_hour diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv new file mode 100644 index 000000000..e711b2dba --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv @@ -0,0 +1,72 @@ +coefficient_name,value,constrain +coef_escort_mode_choice_logsum,0.2,F +coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.4733878369607008,F +coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.008568718107435931,F +coef_escort_fulltime_worker_departure_after_8_am_linear,0.0,F +coef_escort_fulltime_worker_departure_after_3_pm_linear,0.10265282779895883,F +coef_escort_fulltime_worker_duration_less_than_30_minutes,0.48717921412555765,F +coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.06360020624220894,F +coef_escort_university_student_duration_less_than_30_minutes,0.0,F +coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.13619138148103171,F +coef_escort_driving_age_student_duration_less_than_30_minutes,0.0,F +coef_escort_driving_age_student_duration_greater_than_30_minutes,0.2898061598287889,F +coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.0,F +coef_escort_med_high_income_duration_greater_than_30_minutes,0.0,F +coef_escort_households_with_no_kids_departure_before_7_30_am,0.0,F +coef_escort_households_with_no_kids_departure_after_8_00_am,0.0,F +coef_escort_households_with_no_kids_departure_before_2_30_pm,0.0,F +coef_escort_households_with_no_kids_departure_after_3_00_pm,0.0,F +coef_escort_households_with_no_kids_arrival_before_8_am,0.0,F +coef_escort_households_with_no_kids_arrival_after_8_30_am,0.0,F +coef_escort_households_with_no_kids_arrival_before_3_pm,0.0,F +coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.0,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.2193957525105046,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.33725705669741124,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-1.843839469217804,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.2844617723897163,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,0.0,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.0,F +coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,0.0,F +coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.0620079827984566,F +coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,0.0,F +coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.236018962947682,F +coef_escort_departure_constant_before_7_am,0.7319683480497287,F +coef_escort_departure_constant_7_am_to_7_30_am,0.0,F +coef_escort_departure_constant_7_30_am_to_8_am,4.122740011813239,T +coef_escort_departure_constant_8_am_to_8_30_am,4.835810362294344,F +coef_escort_departure_constant_8_30_am_to_9_am,5.122388952675412,F +coef_escort_departure_constant_after_9_am,4.283724261944397,F +coef_escort_departure_constant_1_30_pm_to_2_pm,0.0,F +coef_escort_departure_constant_2_pm_to_2_30_pm,0.7940143785524454,F +coef_escort_departure_constant_2_30_pm_to_3_pm,1.0810066025980387,F +coef_escort_departure_constant_3_pm_to_3_30_pm,1.326721246058783,F +coef_escort_departure_constant_after_3_30_pm,1.1856245248532122,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.19604399657492097,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,0.0,F +coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.0,F +coef_escort_arrival_constant_before_7_am,4.009035240130145,F +coef_escort_arrival_constant_7_am_to_7_30_am,4.18385718813814,F +coef_escort_arrival_constant_7_30_am_to_8_am,2.9375790166530416,F +coef_escort_arrival_constant_8_am_to_8_30_am,0.0,T +coef_escort_arrival_constant_8_30_am_to_9_am,0.0,F +coef_escort_arrival_constant_after_9_am,-0.8516924916440327,F +coef_escort_arrival_constant_2_30_pm_to_3_pm,0.8821519927765968,F +coef_escort_arrival_constant_3_pm_to_3_30_pm,1.3755379509692238,F +coef_escort_arrival_constant_3_30_pm_to_4_pm,1.5074800730702913,F +coef_escort_arrival_constant_4_pm_to_4_30_pm,1.6943356568396335,F +coef_escort_arrival_constant_after_4_30_pm,1.2683991826495213,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.2792532861601614,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.17999830126346059,F +coef_escort_duration_constant_0_hour,-0.2939194972058852,F +coef_escort_duration_constant_30_minutes,0.0,T +coef_escort_duration_constant_1_hour,0.0,F +coef_escort_duration_constant_1_hour_30_minutes,-0.717021512011088,F +coef_escort_duration_constant_2_hours,-1.1701511666645092,F +coef_escort_duration_constant_longer_than_2_hours,-1.1702599996232288,F +coef_escort_calibration_constant_duration_1,0.0,F +coef_escort_calibration_constant_duration_2,0.0,F +coef_escort_calibration_constant_departure_9,0.0,F +coef_escort_calibration_constant_departure_10,0.0,F +coef_escort_calibration_constant_departure_23,0.0,F +coef_escort_calibration_constant_departure_24,0.0,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv similarity index 53% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv index 12816d338..71681f7b5 100644 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv @@ -1,20 +1,20 @@ Label,Description,Expression,Coefficient #DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,DISCRETIONARY - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>4) & (df.age<18)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear +util_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<4) & (df.ptype == 5)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear +util_discretionary_zero_auto_households_duration_less_than_reference_linear,DISCRETIONARY - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_less_than_reference_linear +util_discretionary_zero_auto_households_duration_greater_than_reference_linear,DISCRETIONARY - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +util_discretionary_auto_distance_duration_less_than_reference_linear,DISCRETIONARY - Auto Distance: Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_reference_linear +util_discretionary_auto_distance_duration_greater_than_reference_linear,DISCRETIONARY - Auto Distance: Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_reference_linear +util_discretionary_time_pressure_duration_less_reference,DISCRETIONARY - Time Pressure - Duration < Reference,"@np.where(((df.duration<4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_less_reference +util_discretionary_time_pressure_duration_greater_reference,DISCRETIONARY - Time Pressure - Duration > Reference,"@np.where(((df.duration>4)), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_reference +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < Reference,"@np.where((df.duration<4), ((4-df.duration)*(df.duration<=4) + (df.duration-4)*(df.duration>4))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.start<9)), ((9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.start<10)),coef_discretionary_departure_constant_before_7_30_am util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am @@ -22,15 +22,15 @@ util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.start<28)), ((28-df.start)*(df.start<=28) + (df.start-28)*(df.start>28)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.start<29)),coef_discretionary_departure_constant_before_5_pm util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.start>33)), ((33-df.start)*(df.start<=33) + (df.start-33)*(df.start>33)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.end<31)), ((31-df.end)*(df.end<=31) + (df.end-31)*(df.end>31)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm @@ -38,7 +38,7 @@ util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Cons util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.end>37)), ((37-df.end)*(df.end<=37) + (df.end-37)*(df.end>37)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.duration==0)),coef_discretionary_duration_constant_0_hour util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.duration==1)),coef_discretionary_duration_constant_30_minutes util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.duration==2)),coef_discretionary_duration_constant_1_hour @@ -46,7 +46,7 @@ util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Con util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.duration==4)),coef_discretionary_duration_constant_2_hours util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.duration==3)),coef_discretionary_calibration_constant_duration_4 util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.duration==4)),coef_discretionary_calibration_constant_duration_5 util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.start==29)),coef_discretionary_calibration_constant_departure_29 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv new file mode 100644 index 000000000..a75d7b3e1 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv @@ -0,0 +1,54 @@ +coefficient_name,value,constrain +coef_discretionary_person_less_than_18_years_old_duration_less_than_reference_linear,0.098366285148487,F +coef_discretionary_person_less_than_18_years_old_duration_greater_than_reference_linear,0.0,F +coef_discretionary_non_working_senior_retiree_duration_less_than_reference_linear,-0.1705916260525488,F +coef_discretionary_retiree_non_working_senior_only_HH_duration_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_discretionary_zero_auto_households_duration_greater_than_reference_linear,0.05222497126898865,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_discretionary_auto_distance_duration_less_than_reference_linear,-0.03606825659562718,F +coef_discretionary_auto_distance_duration_greater_than_reference_linear,0.004596600975929468,F +coef_discretionary_time_pressure_duration_less_reference,0.6728960123740134,F +coef_discretionary_time_pressure_duration_greater_reference,0.25402774161065456,F +coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_reference,0.08104208628580123,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.6104747153010481,F +coef_discretionary_departure_constant_before_7_30_am,-0.3390521412898814,F +coef_discretionary_departure_constant_7_30_am_to_8_am,0.0,F +coef_discretionary_departure_constant_8_am_to_8_30_am,0.0,F +coef_discretionary_departure_constant_8_30_am_to_9_am,0.0,F +coef_discretionary_departure_constant_9_am_to_9_30_am,0.33705457332633537,F +coef_discretionary_departure_constant_9_30_am_to_10_am,0.14050652594559154,F +coef_discretionary_departure_constant_10_am_to_10_30_am,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,2.055051892448251,F +coef_discretionary_departure_constant_before_5_pm,10.078028457555423,F +coef_discretionary_departure_constant_5_pm_to_5_30_pm,8.809104255087856,F +coef_discretionary_departure_constant_5_30_pm_to_6_pm,7.066441289657018,F +coef_discretionary_departure_constant_6_pm_to_6_30_pm,4.909660731901318,F +coef_discretionary_departure_constant_6_30_pm_to_7_pm,2.825818687356497,T +coef_discretionary_departure_constant_after_7_pm,0.0,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-2.407069142986441,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,-1.9913325130310646,F +coef_discretionary_arrival_constant_before_6_30_pm,-8.318569502929208,F +coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-6.434917972534465,F +coef_discretionary_arrival_constant_7_pm_to_7_30_pm,-4.138799434182262,F +coef_discretionary_arrival_constant_7_30_pm_to_8_pm,-2.1058225272583,F +coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0.0,T +coef_discretionary_arrival_constant_8_30_pm_to_9_pm,2.1493158879532435,F +coef_discretionary_arrival_constant_after_9_pm,3.8558666359863616,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,1.6858826820414816,F +coef_discretionary_duration_constant_0_hour,0.0,F +coef_discretionary_duration_constant_30_minutes,0.0,F +coef_discretionary_duration_constant_1_hour,0.0,F +coef_discretionary_duration_constant_1_hr_30_minutes,0.0,F +coef_discretionary_duration_constant_2_hours,0.0,F +coef_discretionary_duration_constant_2_hr_30_minutes,-2.9894794551717427,T +coef_discretionary_duration_constant_3_hours_or_more,-6.039866672397804,F +coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-2.9211224892215153,F +coef_discretionary_calibration_constant_duration_4,0.0,F +coef_discretionary_calibration_constant_duration_5,0.0,F +coef_discretionary_calibration_constant_departure_29,0.0,F +coef_discretionary_calibration_constant_departure_30,0.0,F +coef_discretionary_calibration_constant_departure_31,0.0,F +coef_discretionary_calibration_constant_departure_32,0.0,F +coef_mode_choice_logsum,0.06338187336512106,F diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv new file mode 100644 index 000000000..8a44f3dce --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv @@ -0,0 +1,54 @@ +Label,Description,Expression,Coefficient +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_maintenance_driving_age_student_duration_greater_than_reference,MAINTENANCE - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_driving_age_student_duration_greater_than_reference +util_maintenance_full_time_worker_duration_greater_than_reference,MAINTENANCE - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_full_time_worker_duration_greater_than_reference +util_maintenance_non_driving_student_duration_greater_than_reference,MAINTENANCE - Non-driving Student: Duration > Reference,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_non_driving_student_duration_greater_than_reference +util_maintenance_pre_school_child_duration_less_than_reference,MAINTENANCE - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_pre_school_child_duration_less_than_reference +util_maintenance_part_time_worker_duration_less_than_reference,MAINTENANCE - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_less_than_reference +util_maintenance_part_time_worker_duration_greater_than_reference,MAINTENANCE - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_part_time_worker_duration_greater_than_reference +util_maintenance_retired_duration_less_than_reference,MAINTENANCE - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_less_than_reference +util_maintenance_retired_duration_greater_than_reference,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_retired_duration_greater_than_reference +util_maintenance_university_student_duration_greater_than_reference,MAINTENANCE - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_university_student_duration_greater_than_reference +util_maintenance_female_duration_less_than_reference,MAINTENANCE - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_less_than_reference +util_maintenance_female_duration_greater_than_reference,MAINTENANCE - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_female_duration_greater_than_reference +util_maintenance_low_income_duration_greater_than_reference,"MAINTENANCE - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_low_income_duration_greater_than_reference +util_maintenance_medium_income_duration_less_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_less_than_reference +util_maintenance_medium_income_duration_greater_than_reference,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_income_duration_greater_than_reference +util_maintenance_medium_high_income_duration_greater_than_reference,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_medium_high_income_duration_greater_than_reference +util_maintenance_distance_duration_less_than_reference,MAINTENANCE - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_reference +util_maintenance_distance_duration_greater_than_reference,MAINTENANCE - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_reference +util_maintenance_time_pressure_duration_greater_than_reference,Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where((df.duration<2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where((df.duration>2), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where((df.start<10), ((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where((df.start<10), (((10-df.start)*(df.start<=10) + (df.start-10)*(df.start>10))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where((df.start>17), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), ((15-df.end)*(df.end<=15) + (df.end-15)*(df.end>15)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), ((28-df.end)*(df.end<=28) + (df.end-28)*(df.end>28)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.duration>2)), (((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv new file mode 100644 index 000000000..056150eef --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv @@ -0,0 +1,53 @@ +coefficient_name,value,constrain +coef_maintenance_driving_age_student_duration_greater_than_reference,0,F +coef_maintenance_full_time_worker_duration_greater_than_reference,0.134548182,F +coef_maintenance_non_driving_student_duration_greater_than_reference,0,F +coef_maintenance_pre_school_child_duration_less_than_reference,-2.158781419,F +coef_maintenance_part_time_worker_duration_less_than_reference,0,F +coef_maintenance_part_time_worker_duration_greater_than_reference,0,F +coef_maintenance_retired_duration_less_than_reference,0,F +coef_maintenance_retired_duration_greater_than_reference,-0.035230993,F +coef_maintenance_university_student_duration_greater_than_reference,0,F +coef_maintenance_female_duration_less_than_reference,-0.33673289,F +coef_maintenance_female_duration_greater_than_reference,0,F +coef_maintenance_low_income_duration_greater_than_reference,0.039981716,F +coef_maintenance_medium_income_duration_less_than_reference,0,F +coef_maintenance_medium_income_duration_greater_than_reference,0,T +coef_maintenance_medium_high_income_duration_greater_than_reference,0,F +coef_maintenance_distance_duration_less_than_reference,-0.285294941,F +coef_maintenance_distance_duration_greater_than_reference,0.004262551,F +coef_maintenance_time_pressure_duration_greater_than_reference,0.215748222,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.038995943,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.894749684,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.949877711,F +coef_maintenance_departure_constant_before_8_am,-0.551863376,F +coef_maintenance_departure_constant_8_am_to_8_30_am,-0.144009892,F +coef_maintenance_departure_constant_8_30_am_to_9_00_am,0,F +coef_maintenance_departure_constant_9_am_to_9_30_am,0,F +coef_maintenance_departure_constant_9_30_am_to_10_am,0,F +coef_maintenance_departure_constant_10_am_to_10_30_am,0,T +coef_maintenance_departure_constant_10_30_am_to_11_am,-0.175380868,F +coef_maintenance_departure_constant_after_11_am,-0.388816014,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.022970491,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.236900146,F +coef_maintenance_arrival_constant_before_10_30_am,0,F +coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T +coef_maintenance_arrival_constant_11_am_to_11_30_am,0,F +coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0,F +coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,0,F +coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0,F +coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0,F +coef_maintenance_arrival_constant_after_4_30_pm,0,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.303621746,F +coef_maintenance_duration_constant_0_hr,6.023099557,F +coef_maintenance_duration_constant_30_minutes,6.292254209,T +coef_maintenance_duration_constant_longer_than_30_minutes,4.91445025,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.738952132,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,-0.129507431,F +coef_maintenance_calibration_constant_duration_1,0,F +coef_maintenance_calibration_constant_duration_2,0,F +coef_maintenance_calibration_constant_duration_3,0,F +coef_maintenance_calibration_constant_duration_4,0.253036722,F +coef_maintenance_calibration_constant_duration_5,0.215281783,F +coef_mode_choice_logsum,0.241330739,F diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping.csv new file mode 100644 index 000000000..0966c8336 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping.csv @@ -0,0 +1,54 @@ +Label,Description,Expression,Coefficient +#SHOPPING,,,#SHOPPING +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_shoppping_driving_age_student_duration_greater_than_reference,SHOPPING - Driving age student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 6)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_driving_age_student_duration_greater_than_reference +util_shoppping_full_time_worker_duration_greater_than_reference,SHOPPING - Full-time worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 1)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_full_time_worker_duration_greater_than_reference +util_shoppping_non_driving_student_duration_greater_than_reference,SHOPPING - Non-driving Student: Duration > Reference,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_non_driving_student_duration_greater_than_reference +util_shoppping_pre_school_child_duration_less_than_reference,SHOPPING - Pre-school Child: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 8)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_pre_school_child_duration_less_than_reference +util_shoppping_part_time_worker_duration_less_than_reference,SHOPPING - Part Time Worker: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shoppping_part_time_worker_duration_less_than_reference +util_shopping_part_time_worker_duration_greater_than_reference,SHOPPING - Part Time Worker: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_part_time_worker_duration_greater_than_reference +util_shopping_retired_duration_less_than_reference,SHOPPING - Retired: Duration < Reference,"@np.where(((df.duration<2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_less_than_reference +util_shopping_retired_duration_greater_than_reference,SHOPPING - Retired: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 5)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_retired_duration_greater_than_reference +util_shopping_university_student_duration_greater_than_reference,SHOPPING - University Student: Duration > Reference,"@np.where(((df.duration>2) & (df.ptype == 3)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_university_student_duration_greater_than_reference +util_shopping_female_duration_less_than_reference,SHOPPING - Female: Duration < Reference,"@np.where(((df.duration<2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_less_than_reference +util_shopping_female_duration_greater_than_reference,SHOPPING - Female: Duration > Reference,"@np.where(((df.duration>2) & (df.female)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_female_duration_greater_than_reference +util_shopping_low_income_duration_greater_than_reference,"SHOPPING - Low Income (<=$25,000): Duration > Reference","@np.where(((df.is_income_less25K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_low_income_duration_greater_than_reference +util_shopping_medium_income_duration_less_than_reference,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < Reference","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_income_duration_less_than_reference +util_shopping_medium_high_income_duration_greater_than_reference,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > Reference","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)), 0)",coef_shopping_medium_high_income_duration_greater_than_reference +util_shopping_distance_duration_less_than_reference,SHOPPING - Distance: Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_reference +util_shopping_distance_duration_greater_than_reference,SHOPPING - Distance: Duration > Reference,"@np.where(((df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_reference +util_shopping_time_pressure_duration_greater_than_reference,SHOPPING - Time Pressure - Duration > Reference,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration < Reference,"@np.where(((df.duration<2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,SHOPPING - Number of additional individual shop and maint. tours - Duration > Reference,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)), ((2-df.duration)*(df.duration<=2) + (df.duration-2)*(df.duration>2)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), ((17-df.start)*(df.start<=17) + (df.start-17)*(df.start>17)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), ((19-df.end)*(df.end<=19) + (df.end-19)*(df.end>19)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), ((38-df.end)*(df.end<=38) + (df.end-38)*(df.end>38)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), (((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv new file mode 100644 index 000000000..b6f2d3b7b --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv @@ -0,0 +1,53 @@ +coefficient_name,value,constrain +coef_shoppping_driving_age_student_duration_greater_than_reference,0,F +coef_shoppping_full_time_worker_duration_greater_than_reference,0.052013304,F +coef_shoppping_non_driving_student_duration_greater_than_reference,0,F +coef_shoppping_pre_school_child_duration_less_than_reference,0,F +coef_shoppping_part_time_worker_duration_less_than_reference,0,F +coef_shopping_part_time_worker_duration_greater_than_reference,0,F +coef_shopping_retired_duration_less_than_reference,-0.264488371,F +coef_shopping_retired_duration_greater_than_reference,0,F +coef_shopping_university_student_duration_greater_than_reference,0,F +coef_shopping_female_duration_less_than_reference,-0.706253687,F +coef_shopping_female_duration_greater_than_reference,0.061494712,F +coef_shopping_low_income_duration_greater_than_reference,0.078324543,F +coef_shopping_medium_income_duration_less_than_reference,0,F +coef_shopping_medium_high_income_duration_greater_than_reference,0,F +coef_shopping_distance_duration_less_than_reference,-0.266345479,F +coef_shopping_distance_duration_greater_than_reference,0.011024114,F +coef_shopping_time_pressure_duration_greater_than_reference,0.186025024,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_reference,0,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_reference,-0.073347514,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-1.271759564,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,0.585672241,F +coef_shopping_departure_constant_before_9_am,-0.829474619,F +coef_shopping_departure_constant_9_am_to_9_30_am,0,F +coef_shopping_departure_constant_9_30_am_to_10_am,0.500429426,F +coef_shopping_departure_constant_10_am_to_10_30_am,0,T +coef_shopping_departure_constant_10_30_am_to_11_00_am,1.470959168,F +coef_shopping_departure_constant_after_11_am,2.157602106,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.435436303,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,0.410256343,F +coef_shopping_arrival_constant_before_12_30_pm,2.191026405,F +coef_shopping_arrival_constant_12_30_pm_to_3_pm,0,F +coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.985581618,F +coef_shopping_arrival_constant_3_30_pm_to_4_pm,-1.403508984,F +coef_shopping_arrival_constant_4_pm_to_4_30_pm,-1.921153952,T +coef_shopping_arrival_constant_4_30_pm_to_5_pm,-2.584015365,F +coef_shopping_arrival_constant_5_pm_to_5_30_pm,-3.02435605,F +coef_shopping_arrival_constant_5_30_pm_to_7_pm,-4.332791088,F +coef_shopping_arrival_constant_7_pm_to_9_30_pm,-6.506695028,F +coef_shopping_arrival_constant_after_9_30_pm,-9.0099778,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.820420778,F +coef_shopping_duration_constant_0_hr,0,F +coef_shopping_duration_constant_30_minutes,1.178015119,F +coef_shopping_duration_constant_1_hr,0,T +coef_shopping_duration_constant_1_hour_30_minutes,0,F +coef_shopping_duration_constant_2_hrs,-0.475594334,F +coef_shopping_duration_constant_longer_than_2_hrs,-1.099004049,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.434855764,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,0,F +coef_shopping_calibration_constant_duration_1,0,F +coef_shopping_calibration_constant_duration_2,0,F +coef_shopping_calibration_constant_duration_3,0,F +coef_mode_choice_logsum,0.2,F diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social.csv new file mode 100644 index 000000000..15ba8fd56 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social.csv @@ -0,0 +1,55 @@ +Label,Description,Expression,Coefficient +#SOCIAL,#SOCIAL,,#SOCIAL +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum +util_social_person_less_than_18_years_old_duration_less_than_reference_linear,SOCIAL - Person< 18 years old: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_reference_linear +util_social_person_less_than_18_years_old_duration_greater_than_reference_linear,SOCIAL - Person< 18 years old: Duration > Reference - Linear,"@np.where(((df.duration>6) & (df.age<18)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear +util_social_non_working_senior_or_retiree_duration_less_than_reference_linear,SOCIAL - Non-working senior/ retiree: Duration < Reference - Linear,"@np.where(((df.duration<6) & (df.ptype == 5)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < Reference - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear +util_social_zero_auto_households_duration_less_than_reference_linear,SOCIAL - Zero auto households: Duration < Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_less_than_reference_linear +util_social_zero_auto_households_duration_greater_than_reference_linear,SOCIAL - Zero auto households: Duration > Reference - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_zero_auto_households_duration_greater_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration < Reference - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,SOCIAL - Number of auto more than number of adults: Duration > Reference - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_reference_linear,SOCIAL - Auto Distance: Duration < Reference - Linear,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_reference_linear +util_social_auto_distance_duration_greater_than_reference_linear,SOCIAL - Auto Distance: Duration > Reference - Linear,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_reference_linear +util_social_time_pressure_duration_less_than_reference,SOCIAL - Time Pressure - Duration < Reference,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6))* (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_less_than_reference +util_social_time_pressure_duration_greater_than_reference,SOCIAL - Time Pressure - Duration > Reference,"@np.where(((df.duration>6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (np.log10 (30 * (tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_social_time_pressure_duration_greater_than_reference +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<6)), ((6-df.duration)*(df.duration<=6) + (df.duration-6)*(df.duration>6)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,@((df.start<12)) * ((12-df.start)*(df.start<=12) + (df.start-12)*(df.start>12)),coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), ((29-df.start)*(df.start<=29) + (df.start-29)*(df.start>29)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), ((34-df.start)*(df.start<=34) + (df.start-34)*(df.start>34)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), ((35-df.end)*(df.end<=35) + (df.end-35)*(df.end>35)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), ((40-df.end)*(df.end<=40) + (df.end-40)*(df.end>40)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), ((5-df.duration)*(df.duration<=5) + (df.duration-5)*(df.duration>5)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), ((8-df.duration)*(df.duration<=8) + (df.duration-8)*(df.duration>8)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv new file mode 100644 index 000000000..8e2f4eb4c --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv @@ -0,0 +1,53 @@ +coefficient_name,value,constrain +coef_social_person_less_than_18_years_old_duration_less_than_reference_linear,0.0,F +coef_social_person_less_than_18_years_old_duration_greater_than_reference_linear,0.1848544664093724,F +coef_social_non_working_senior_or_retiree_duration_less_than_reference_linear,0.0,F +coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_less_than_reference_linear,0.0,F +coef_social_zero_auto_households_duration_greater_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_reference_linear,0.0,F +coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_reference_linear,0.0,F +coef_social_auto_distance_duration_less_than_reference_linear,-0.037191211367614524,F +coef_social_auto_distance_duration_greater_than_reference_linear,0.004103999782121744,F +coef_social_time_pressure_duration_less_than_reference,0.0,F +coef_social_time_pressure_duration_greater_than_reference,0.2472747423164598,F +coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_reference,0.0,F +coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.35038347561648353,F +coef_social_departure_constant_before_9_am,0.0,F +coef_social_departure_constant_9_am_to_9_30_am,0.48320177182957835,F +coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,0.10579475979139634,F +coef_social_departure_constant_before_5_30_pm,2.9248450547864326,F +coef_social_departure_constant_5_30_pm_to_6_pm,3.396566813557204,F +coef_social_departure_constant_6_pm_to_6_30_pm,0.0,T +coef_social_departure_constant_6_30_pm_to_7_pm,3.2332806668467815,F +coef_social_departure_constant_7_pm_to_7_30_pm,2.9564723583835772,F +coef_social_departure_constant_after_7_30_pm,2.5033367748702537,F +coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.31575816708433413,F +coef_social_arrival_constant_3_pm_to_3_30_pm,0.6128608441643291,F +coef_social_arrival_constant_3_30_pm_to_4_pm,0.0,F +coef_social_arrival_constant_4_pm_to_4_30_pm,0.0,F +coef_social_arrival_constant_5_pm_to_6_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,-0.11543939695197131,F +coef_social_arrival_constant_before_8_30_pm,0.0,F +coef_social_arrival_constant_8_30_pm_to_9_pm,0.3725347628206525,F +coef_social_arrival_constant_9_pm_to_9_30_pm,0.34127159737778273,F +coef_social_arrival_constant_9_30_pm_to_10_pm,0.0,T +coef_social_arrival_constant_10_pm_to_10_30_pm,0.349324126243515,F +coef_social_arrival_constant_after_10_30_pm,0.0,F +coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.22982597094534407,F +coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.0,F +coef_social_duration_constant_less_than_3_hours,0.0,F +coef_social_duration_constant_3_hours,0.0,T +coef_social_duration_constant_3_hrs_30_minutes,-0.9879910839979239,F +coef_social_duration_constant_4_hours_or_more,-1.9671125098564861,F +coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-1.081195937845935,F +coef_social_calibration_constant_duration_1,0.0,F +coef_social_calibration_constant_duration_2,0.0,F +coef_social_calibration_constant_duration_3,0.0,F +coef_social_calibration_constant_duration_4,0.0,F +coef_social_calibration_constant_duration_5,0.0,F +coef_social_calibration_constant_duration_6,0.0,F +coef_social_calibration_constant_duration_7,0.0,F +coef_social_calibration_constant_duration_8,0.0,F +coef_social_calibration_constant_duration_9,0.0,F +coef_mode_choice_logsum,0.014255126759576003,F diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_school.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_school.csv new file mode 100644 index 000000000..3492559c8 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_school.csv @@ -0,0 +1,61 @@ +Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef +util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_before_730_am__Linear +util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Low_income_lt25000_Departure_after_800_am_Linear +util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_lt_8hrs +util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Low_income_lt25000_Duration_gt_8hrs +util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear +util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_0_to_5_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_13_to_15_yrs_Departure_After_800_am +util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_Age_16_to_17_yrs_Departure_After_800_am +util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_lt_8hrs +util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_0_to_5_yrs_Duration_gt_8hrs +util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start < df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_lt_8hrs +util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_13_to_15_yrs_Duration_gt_8hrs +util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.start > df.durationRefBin_school)) * df.durationShift_school",coef_Age_16_to_17_yrs_Duration_gt_8hrs +util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start < df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear +util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start > df.departureRefBin_school) *df.departureLinearShift1 * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear +util_Time_SOV_freeflow_to_destination_arrival_before_ref,SCHOOL - Time (SOV freeflow) to destination - Arrival < Reference Bin,"@(df.end < df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_before_ref +util_Time_SOV_freeflow_to_destination_arrival_after_ref,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end > df.arrivalRefBin_school) * df.arrivalLinearShift1_school * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_arrival_after_ref +util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start < df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear +util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start > df.departureRefBin_school)) *df.departureLinearShift1",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear +util_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end < df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref +util_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end > df.arrivalRefBin_school)) * df.arrivalLinearShift1_school",coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.departureRefBin_school))) *df.departureLinearShift1",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start < df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start > df.durationRefBin_school))) * df.durationShift_school",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * (((7-df.duration)*(df.duration<=7)) + ((df.duration-7)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM +util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 +util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 +util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 +util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 +util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start > df.departureRefBin_school),coef_Departure_Constant_After_0800_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * (((11-df.start)*(df.start<11)) + ((df.start-11)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11) * (np.maximum(df.start - 11, 0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root +util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM +util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ +util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ +util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ +util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ +util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ +util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 +util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ +util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * (((31-df.end)*(df.end<31)) + ((df.end-31)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear +util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * (((13-df.duration)*(df.duration<13)) + ((df.duration-13)*(df.duration>13)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear +util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs +util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours +util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours +util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours +util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours +util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * (((19-df.duration)*(df.duration<19)) + ((df.duration-19)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_school_coeffs.csv new file mode 100644 index 000000000..4e5c91754 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_school_coeffs.csv @@ -0,0 +1,59 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.0,F +coef_Low_income_lt25000_Departure_before_730_am__Linear,0.0,F +coef_Low_income_lt25000_Departure_after_800_am_Linear,0.0,F +coef_Low_income_lt25000_Duration_lt_8hrs,0.0,F +coef_Low_income_lt25000_Duration_gt_8hrs,0.0,F +coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.0,F +coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.8148835593502417,F +coef_Age_13_to_15_yrs_Departure_Before_730_am,0.27726279249507474,F +coef_Age_13_to_15_yrs_Departure_After_800_am,-0.7597642883716563,F +coef_Age_16_to_17_yrs_Departure_After_800_am,-0.8824101711801812,F +coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.1741028592807906,F +coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.241662039787307,F +coef_Age_13_to_15_yrs_Duration_lt_8hrs,0.0,F +coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.5963387646724607,F +coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.6956001833839539,F +coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.00454314976439104,F +coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.008509123281468139,F +coef_Time_SOV_freeflow_to_destination_arrival_before_ref,-0.0026124484527274773,F +coef_Time_SOV_freeflow_to_destination_arrival_after_ref,0.00213627544235268,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.0,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.14721190410201404,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_before_ref,-0.16604467456086497,F +coef_All_adults_in_the_household_are_fulltime_workers_arrival_after_ref,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Departure_after_800_am,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.0,F +coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,0.0,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,0.0,F +coef_Departure_Constant_Before_0600_AM,-8.764796175382282,F +coef_Departure_Constant_0600_AM_to_0630_AM_7,-2.707793497506736,F +coef_Departure_Constant_0630_AM_to_0700_AM_8,-0.654253451781764,F +coef_Departure_Constant_0700_AM_to_0730_AM_9,0.0,F +coef_Departure_Constant_0730_AM_to_0800_AM_10,-0.48281221099875055,T +coef_Departure_Constant_After_0800_AM,1.0336602346049464,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.0,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-0.4982940561474212,F +coef_Arrival_Constant_Before_0230_PM,0.0,F +coef_Arrival_Constant_0230_PM_0300_PM_24_,0.0,F +coef_Arrival_Constant_0300_PM_0330_PM_25_,0.22741669683682353,F +coef_Arrival_Constant_0330_PM_0400_PM_26_,0.0,F +coef_Arrival_Constant_0400_PM_0430_PM_27_,0.0,T +coef_Arrival_Constant_0430_PM_0500_PM_28_,-1.0249817399820995,F +coef_Arrival_Constant_0500_PM_0530_PM_29,-1.4403670450638697,F +coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.8460834370368264,F +coef_Arrival_Constant_After_0600_PM,-2.3316983953964314,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.6853763563949641,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.19770146146375506,F +coef_Duration_Constant_Shorter_than_7_hrs,-3.0269316327052733,F +coef_Duration_Constant_7_hours,-1.8715765041190864,F +coef_Duration_Constant_7p5_hours,-0.3821662280789037,F +coef_Duration_Constant_8_hours,0.0,T +coef_Duration_Constant_8p5_hours,-0.2743767294042022,F +coef_Duration_Constant_9_hours,-0.8177669270743325,F +coef_Duration_Constant_Longer_than_9_hrs,-0.5422856490451977,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.21538163084141398,F +zero_coef,0.0,T diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_university.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_university.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_university.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_university.csv diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_university_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_university_coeffs.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_university_coeffs.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_university_coeffs.csv diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_work.csv old mode 100755 new mode 100644 similarity index 50% rename from activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv rename to activitysim/examples/production_semcog/configs/tour_scheduling_work.csv index e180d0c15..0d4b76169 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_work.csv @@ -1,66 +1,69 @@ Label,Description,Expression,Coefficient +# writing out varibles we may need for estimation,,, +round_trip_auto_time_to_work,round_trip_auto_time_to_work,@df.roundtrip_auto_time_to_work,zero_coef +age,age,@df.age,zero_coef util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum -util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start<9)) * df.departureLinearShift1,coef_Female_Departure_before_7_am -util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm -util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am -util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am -util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm -util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start<9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am -util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start>9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am +util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_Departure_before_7_am +util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm +util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am +util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am +util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm +util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am +util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm -util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start<9)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am -util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end>30)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm -util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start<9)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am -util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start<9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am -util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start>9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am -util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start>9)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am -util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start<9)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am -util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start>9)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am -util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm -util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm -util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm -util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm -util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm -util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start<9)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am -util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end>30)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm -util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start<9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am -util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start>9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am -util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm -util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end>30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm -util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start>9)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am -util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end<30)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm -util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end>30)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm -#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start<9)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am -#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start>9)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am -#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end<30)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm -#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start<9)) * df.departureLinearShift1,coef_Service_Departure_before_7_am -#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start>9)) * df.departureLinearShift1,coef_Service_Departure_after_7_am -#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm -#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start<9)) * df.departureLinearShift1,coef_Health_Departure_before_7_am -#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end>30)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm -#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start>9)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am -#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end<30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm -#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end>30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm -util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start<9) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am -util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start>9) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am -util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end<30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm -util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end>30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm -util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am -util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm -util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am -util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am -util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm -util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end>30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm -util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start<9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am -util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start>9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am -util_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours -util_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours -util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm -util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm -util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours -util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours +util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am +util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm +util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am +util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am +util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am +util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am +util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm +util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm +util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm +util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm +util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm +util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am +util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm +util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am +util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am +util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm +util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm +util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am +util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm +util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm +#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am +#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am +#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm +#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_before_7_am +#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Service_Departure_after_7_am +#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm +#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Health_Departure_before_7_am +#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm +#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am +#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm +#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm +util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start < df.departureRefBin) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am +util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start > df.departureRefBin) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am +util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end < df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm +util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end > df.arrivalRefBin) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm +util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am +util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end < df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end > df.arrivalRefBin)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm +util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start < df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am +util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start > df.departureRefBin)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am +util_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * (((22-df.start)*(df.start<22)) + ((df.start-22)*(df.start>22))),coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end < df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.end > df.durationRefBin)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref #,#Departure Constants,,coef_Departure_Constants -util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@(df.start<6) * ((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am +util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,@((df.start<7) * (7-df.start)),coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 @@ -69,9 +72,9 @@ util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM ( util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM -util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@(df.start>13) * (((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root +util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@((df.start>13) * (np.maximum(df.start-13,0) ** 0.5))",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root #,#Arrival Constants,,coef_Arrival_Constants -util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@(df.end<25) * ((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm +util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,@((df.end<25) * (25-df.end)),coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 @@ -83,9 +86,9 @@ util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,e util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM -util_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 6:30 pm - Square root,"@(df.end>35) * (((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root +util_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 830 pm - Square root,"@((df.end>35) * (np.maximum(df.end-35,0) ** 0.5))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root #,#Duration Constants,,coef_Duration_Constants -util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@(df.duration<16) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs +util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,@((df.duration<17) * (17-df.duration)),coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours @@ -96,5 +99,9 @@ util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Du util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs -util_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,Duration Constant: Shift for every 30 minutes more than 10 hrs - Linear,"@(df.duration>25) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,Duration Constant: Shift for every 30 minutes more than 12 hrs - Linear,@((df.duration>24) * (df.duration-24)),coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 +#,,, +#,,, +util_Departure_Constant_EA,Departure Constant: Before 06:30 AM,start<8,0.25 +util_Departure_Constant_Before_AM,Departure Constant: AM period,start>7 & start <13,-0.12 diff --git a/activitysim/examples/production_semcog/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/production_semcog/configs/tour_scheduling_work_coeffs.csv new file mode 100644 index 000000000..e8d9607f9 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/tour_scheduling_work_coeffs.csv @@ -0,0 +1,98 @@ +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.2,T +coef_Female_Departure_before_7_am,-0.21983073617010354,F +coef_Female_Arrival_after_6_pm,-0.028931718675078898,F +coef_Female_with_preschool_child_Departure_before_7_am,0.0,F +coef_Female_with_preschool_child_Departure_after_7_am,0.0,F +coef_Female_with_preschool_child_Arrival_after_6_pm,0.0,F +coef_Low_income_lt_25000_Departure_before_7_am,0.23142273676542863,F +coef_Low_income_lt_25000_Departure_after_7_am,0.06394624853227485,F +coef_Low_income_lt_25000_Arrival_after_6_pm,-0.2101481937242358,F +coef_Med_income_25k_to_60k_Departure_before_7_am,0.20582925253502693,F +coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.030759330025131475,F +coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.12339331900370523,F +coef_Age_16_to_18_yrs_Departure_Before_7_am,0.0,F +coef_Age_16_to_18_yrs_Departure_After_7_am,0.1569218088558017,F +coef_Age_19_to_24_yrs_Departure_After_7_am,0.03280621940285348,F +coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.14071003044101923,F +coef_Age_65_plus_yrs_Departure_After_7_am,0.0,F +coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.03801692388513126,F +coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.028091109013424525,F +coef_Age_56_to_64_yrs_Arrival_after_6_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_before_5_pm,0.0,F +coef_Age_65_plus_yrs_Arrival_after_6_pm,0.0,F +coef_Zero_auto_HH_Departure_before_7_am,0.0,F +coef_Zero_auto_HH_Arrival_after_6_pm,0.054809482440729494,F +coef_Parttime_worker_Departure_before_7_am,-0.465300506980724,F +coef_Parttime_worker_Departure_after_7_am,0.24035601017298427,F +coef_Parttime_worker_Arrival_before_5_pm,0.27263218681666973,F +coef_Parttime_worker_Arrival_after_6_pm,-0.2533496036708649,F +coef_University_student_Departure_after_7_am,0.10708190818422741,F +coef_University_student_Arrival_before_5_pm,0.0,F +coef_University_student_Arrival_after_6_pm,-0.0784130766004373,F +coef_Blue_collar_Departure_before_7_am,0.327242475,F +coef_Blue_collar_Departure_after_7_am,0.047214248,F +coef_Blue_collar_Arrival_before_5_pm,0.04197056,F +coef_Service_Departure_before_7_am,0.117783508,F +coef_Service_Departure_after_7_am,0.081611629,F +coef_Service_Arrival_before_5_pm,0.0,T +coef_Health_Departure_before_7_am,0.135275931,F +coef_Health_Arrival_after_6_pm,0.062010123,F +coef_Retail_and_food_Departure_after_7_am,0.076302969,F +coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F +coef_Retail_and_food_Arrival_after_6_pm,0.0270691939999999,F +coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.006152588569993294,F +coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.002638509520279399,F +coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.003924739241949497,F +coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.0016423390591779922,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.0,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.08289214126546349,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.0,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.04292555821779037,F +coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,-0.15631713590985458,F +coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.037321682863975456,F +coef_First_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.3054062365147087,F +coef_First_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.15794972916369113,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,0.0,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.13397035652802908,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_dur_ref,0.45835848769473103,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_dur_ref,0.5272527491008135,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-0.7231020452773588,F +coef_Departure_Constant_Before_06_AM,-0.1915960040171813,F +coef_Departure_Constant_06_AM_06_AM_7,-0.5420940244335356,F +coef_Departure_Constant_06_AM_07_AM_8,-0.0814829941363625,F +coef_Departure_Constant_07_AM_07_AM_9,0.0,T +coef_Departure_Constant_07_AM_08_AM_10,-0.12487604386728021,F +coef_Departure_Constant_08_AM_08_AM_11,-0.4486786777966309,F +coef_Departure_Constant_08_AM_09_AM_12,-0.9838612097965408,F +coef_Departure_Constant_After_09_AM,-1.5618817419634867,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.5783447729979645,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.20009504631293196,F +coef_Arrival_Constant_Before_03_PM,-0.8629231486548913,F +coef_Arrival_Constant_03_PM_04_PM_26,-0.644880302920333,F +coef_Arrival_Constant_04_PM_04_PM_27,-0.3047263117628279,F +coef_Arrival_Constant_04_PM_05_PM_28,-0.1886361914686483,F +coef_Arrival_Constant_05_PM_05_PM_29,0.0,F +coef_Arrival_Constant_05_PM_06_PM_30,0.2500583541484266,T +coef_Arrival_Constant_06_PM_06_PM_31,0.23990107930944812,F +coef_Arrival_Constant_06_PM_7_PM_32,0.15526238473246975,F +coef_Arrival_Constant_7_PM_7_PM_33,0.0,F +coef_Arrival_Constant_7_PM_8_PM_34,-0.18208024865786762,F +coef_Arrival_Constant_After_08_PM,0.0,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_830_pm_Square_root,0.0,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.10663128725700759,F +coef_Duration_Constant_Shorter_than_8p5_hrs,0.0,F +coef_Duration_Constant_8p5_hours,-0.2403003279562287,F +coef_Duration_Constant_9_hours,0.0,F +coef_Duration_Constant_9p5_hours,0.0,F +coef_Duration_Constant_10_hours,0.0,F +coef_Duration_Constant_10p5_hours,-0.3088419298533547,T +coef_Duration_Constant_11_hours,-0.6583531336885191,F +coef_Duration_Constant_11p5_hours,-0.8734017033609268,F +coef_Duration_Constant_12_hours,-1.3551244638084843,F +coef_Duration_Constant_Longer_than_12_hrs,-1.0959910217157498,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_12_hrs,-0.4918560543397093,F +coef_Calibration_constant_Duration_0,-1.2069106964427703,F +zero_coef,0.0,T diff --git a/activitysim/examples/production_semcog/configs/transit_pass_ownership.csv b/activitysim/examples/production_semcog/configs/transit_pass_ownership.csv new file mode 100644 index 000000000..1136ca1f9 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/transit_pass_ownership.csv @@ -0,0 +1,28 @@ +Label,Description,Expression,no_pass,pass +util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft_pass +util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt_pass +util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un_pass +util_nw,Non-working adult,@df.ptype==PTYPE_NONWORK,0,coef_nw_pass +util_rt,Retired,@df.ptype==PTYPE_RETIRED,0,coef_rt_pass +util_inc1,0-$9k,"@df.income.between(0, 9000)",0,coef_inc1_pass +util_inc2,$10-$24k,"@df.income.between(10000, 24000)",0,coef_inc2_pass +util_inc3,$25-$34k,"@df.income.between(25000,34000)",0,coef_inc3_pass +util_inc4,$35-$49k,"@df.income.between(35000,49000)",0,coef_inc4_pass +util_inc10,$250k+,@df.income >= 250000,0,coef_inc10_pass +util_na20,NAICS 20 (mining/utilities/construct),@df.industry_naics=='20',0,coef_na20_pas +util_na30,NAICS 30 (manufacturing,@df.industry_naics=='30',0,coef_na30_pas +util_na50,NAICS 50 (Info/Fin/insur/real estate/prof/sci/tech/manage/admin),@df.industry_naics=='50',0,coef_na50_pas +util_na70,NAICS 70 (Entertain/accom),@df.industry_naics=='70',0,coef_na70_pas +util_na80,NAICS 80 (Other services),@df.industry_naics=='80',0,coef_na80_pas +util_publ,NAICS 90 (Public admin),@df.industry_naics=='90',0,coef_publ_pas +#approximate measure for the time being,,,, +util_wrkamt,Auto minus transit time (work),@df.work_auto_savings * -1,0,coef_wrkamt_pas +util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pas +utils_pass_asc,Constant,1,0,coef_pass_asc +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@(df.ptype == 4) | (df.ptype==5),0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 6,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 7,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 8,0,coef_ps_asc diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.yaml b/activitysim/examples/production_semcog/configs/transit_pass_ownership.yaml similarity index 100% rename from activitysim/examples/prototype_semcog/configs/transit_pass_ownership.yaml rename to activitysim/examples/production_semcog/configs/transit_pass_ownership.yaml diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_ownership_coeffs.csv b/activitysim/examples/production_semcog/configs/transit_pass_ownership_coeffs.csv similarity index 65% rename from activitysim/examples/prototype_semcog/configs/transit_pass_ownership_coeffs.csv rename to activitysim/examples/production_semcog/configs/transit_pass_ownership_coeffs.csv index aa756b4c0..7b76a0762 100644 --- a/activitysim/examples/prototype_semcog/configs/transit_pass_ownership_coeffs.csv +++ b/activitysim/examples/production_semcog/configs/transit_pass_ownership_coeffs.csv @@ -1,4 +1,4 @@ -coefficient_name,coefficient_name,value,constrain +coefficient_name,coefficient_name.1,value,constrain coef_ft_pass,Full-time worker,2.034739841,F coef_pt_pass,Part-time worker,1.934451611,F coef_un_pass,University/College,2.38777737,F @@ -18,3 +18,10 @@ coef_na80_pas,NAICS 80 (Other services),-0.585251651,F coef_publ_pas,NAICS 90 (Public admin),0.30410587,F coef_subs_pas,Subsidy offered,1.967569275,F coef_pass_asc,Constant,-3.932862618,F +coef_ft_asc,ptype_ft calibration constant,-1.7556306647985378,F +coef_pt_asc,ptype_pt calibration constant,-0.9993175989158146,F +coef_un_asc,ptype_un calibration constant,-0.1879099307931272,F +coef_na_asc,ptype_na calibration constant,-0.8203511274990747,F +coef_da_asc,ptype_da calibration constant,1.0596420838216658,F +coef_nd_asc,ptype_nd calibration constant,-2.0998091820041402,F +coef_ps_asc,ptype_ps calibration constant,-2.0,F diff --git a/activitysim/examples/production_semcog/configs/transit_pass_subsidy.csv b/activitysim/examples/production_semcog/configs/transit_pass_subsidy.csv new file mode 100644 index 000000000..95d957491 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/transit_pass_subsidy.csv @@ -0,0 +1,17 @@ +Label,Description,Expression,no_subsidy,subsidy +util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft +util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt +util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un +util_na70,NAICS 70 (Entertain/accom),@df.industry_naics=='70',0,coef_na70 +util_na80,NAICS 80 (Other services),@df.industry_naics=='80',0,coef_na80 +util_publ,NAICS 90 (Public admin),@df.industry_naics=='90',0,coef_publ +util_tr_hh_emp,Household transit accessibility,@df.trPKHH,0,coef_tr_hh_emp +util_pkcost,Daily parking cost (dollars),"@np.minimum(np.where(df.parking_hourly.isna(), 999, df.parking_hourly*9), df.parking_daily)",0,coef_pkcost +utils_sub_asc,Constant,1,0,coef_sub_asc +util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc +util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc +util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc +util_na_ASC,ptype_na calibration constant,@(df.ptype == 4) | (df.ptype == 5),0,coef_na_asc +util_da_ASC,ptype_da calibration constant,@df.ptype == 6,0,coef_da_asc +util_nd_ASC,ptype_nd calibration constant,@df.ptype == 7,0,coef_nd_asc +util_ps_ASC,ptype_ps calibration constant,@df.ptype == 8,0,coef_ps_asc diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.yaml b/activitysim/examples/production_semcog/configs/transit_pass_subsidy.yaml similarity index 100% rename from activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.yaml rename to activitysim/examples/production_semcog/configs/transit_pass_subsidy.yaml diff --git a/activitysim/examples/production_semcog/configs/transit_pass_subsidy_coeffs.csv b/activitysim/examples/production_semcog/configs/transit_pass_subsidy_coeffs.csv new file mode 100644 index 000000000..0dc71de39 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/transit_pass_subsidy_coeffs.csv @@ -0,0 +1,17 @@ +coefficient_name,coefficient_name.1,value,constrain +coef_ft,Full-time worker,1.8826656640000001,F +coef_pt,Part-time worker,1.360746182,F +coef_un,University/College,2.026985147,F +coef_na70,NAICS 70 (Entertain/accom),-0.659568416,F +coef_na80,NAICS 80 (Other services),-0.35258885799999995,F +coef_publ,NAICS 90 (Public admin),0.8649915709999999,F +coef_tr_hh_emp,Transit accessibility to households,0.049608562,F +coef_pkcost,Daily parking cost (dollars),0.060145818,F +coef_sub_asc,Constant,-5.138015944,F +coef_ft_asc,ptype_ft calibration constant,-1.4480580144656137,F +coef_pt_asc,ptype_pt calibration constant,-0.5618957069861918,F +coef_un_asc,ptype_un calibration constant,0.9180815482670087,F +coef_na_asc,ptype_na calibration constant,1.800286399805092,F +coef_da_asc,ptype_da calibration constant,1.1347405644999717,F +coef_nd_asc,ptype_nd calibration constant,-1.4385176853101924,F +coef_ps_asc,ptype_ps calibration constant,-2.0,F diff --git a/activitysim/examples/production_semcog/configs/trip_destination.csv b/activitysim/examples/production_semcog/configs/trip_destination.csv new file mode 100644 index 000000000..02a99b737 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_destination.csv @@ -0,0 +1,17 @@ +Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +#util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, +util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork +util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork +util_distance_joint,distance (calibration adjustment joint),@df.is_joint * (od_skims['DIST'] + dp_skims['DIST']),,,,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint, +util_prox_home_outbound,stop proximity to home (outbound),@df.outbound * od_skims['DIST'],coef_prox_home_outbound_work,,,,,,,,, +util_prox_home_inbound,stop proximity to home (inbound),@~df.outbound * dp_skims['DIST'],coef_prox_home_inbound_work,,,,,,,,, +util_prox_dest_outbound,stop proximity to main destination (outbound),@df.outbound * dp_skims['DIST'],coef_prox_dest_outbound_work,,,,,,,,, +util_prox_dest_inbound,stop proximity to main destination (inbound),@~df.outbound * od_skims['DIST'],0,,,,,,,,, +#,,,,,,,,,,,, +util_sample_of_alternatives_correction_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_mode_choice_logsum_os,Mode choice logsum from origin to stop,od_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum +util_stop_not_accessible_by_this_tour_mode,Can't access stop zone by this tour mode,(od_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +util_mode_choice_logsum_sd,Mode choice logsum from stop to destination,dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum +util_dest_not_accessible_by_this_tour_mode,Can't access destination zone by this tour mode,(dp_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination.yaml b/activitysim/examples/production_semcog/configs/trip_destination.yaml old mode 100755 new mode 100644 similarity index 66% rename from activitysim/examples/prototype_semcog/configs/trip_destination.yaml rename to activitysim/examples/production_semcog/configs/trip_destination.yaml index e754e188b..6cf3aeb63 --- a/activitysim/examples/prototype_semcog/configs/trip_destination.yaml +++ b/activitysim/examples/production_semcog/configs/trip_destination.yaml @@ -1,8 +1,11 @@ +SAMPLE_SPEC: trip_destination_sample.csv +SPEC: trip_destination.csv +COEFFICIENTS: trip_destination_coefficients.csv + SAMPLE_SIZE: 30 DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv DESTINATION_SPEC: trip_destination.csv -COEFFICIENTS: _dummy_coefficients.csv LOGSUM_SETTINGS: trip_mode_choice.yaml @@ -15,8 +18,12 @@ DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample # model-specific logsum-related settings TRIP_ORIGIN: origin ALT_DEST_COL_NAME: dest_taz +# PRIMARY_DEST: destination +# Fix to make sure the return leg sees the right distances PRIMARY_DEST: tour_leg_dest # must be created in preprocessor +# tour_mode is already in trips table, so we don't need it from tours +# (it is assigned in trip_destination_annotate_trips_preprocessor ) REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: - tour_mode @@ -34,3 +41,6 @@ preprocessor: # drop failed trips and cleanup failed trip leg_mates for consistency # (i.e. adjust trip_count, trip_num, first for missing failed trips) CLEANUP: False + +# this setting is used by testing code to force failed trip_destination +# fail_some_trips_for_testing: False diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/production_semcog/configs/trip_destination_annotate_trips_preprocessor.csv old mode 100755 new mode 100644 similarity index 72% rename from activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/production_semcog/configs/trip_destination_annotate_trips_preprocessor.csv index 9f2d502d3..fa2cc514f --- a/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/production_semcog/configs/trip_destination_annotate_trips_preprocessor.csv @@ -7,7 +7,4 @@ Description,Target,Expression #,,not needed as school is not chosen as an intermediate trip destination #,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" #,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" -,purpose_index_num,"size_terms.get_cols(df.purpose)" -,tour_mode_is_walk,"reindex(tours.tour_mode, df.tour_id)=='WALK'" -,tour_mode_is_bike,"reindex(tours.tour_mode, df.tour_id)=='BIKE'" -,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id)).astype(int)" +,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" diff --git a/activitysim/examples/production_semcog/configs/trip_destination_coefficients.csv b/activitysim/examples/production_semcog/configs/trip_destination_coefficients.csv new file mode 100644 index 000000000..c6fd18589 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_destination_coefficients.csv @@ -0,0 +1,19 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999.0,T +coef_one,1,T +coef_mode_choice_logsum,1.821,F +coef_distance_joint,-0.1238,F +coef_util_distance_work_outbound,-0.04972591574229,F +coef_util_distance_work_inbound,0.147813278663948,F +coef_util_distance_univ,-0.0613,F +coef_util_distance_school,-0.1056,F +coef_util_distance_escort,-0.1491,F +coef_util_distance_shopping,-0.1192,F +coef_util_distance_eatout,-0.1029,F +coef_util_distance_othmaint,-0.0962,F +coef_util_distance_social,-0.1329,F +coef_util_distance_othdiscr,-0.126172224,F +coef_util_distance_atwork,-0.122334597,F +coef_prox_home_outbound_work,-0.3800,F +coef_prox_home_inbound_work,-0.1500,F +coef_prox_dest_outbound_work,-0.26,F diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv b/activitysim/examples/production_semcog/configs/trip_destination_sample.csv old mode 100755 new mode 100644 similarity index 75% rename from activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv rename to activitysim/examples/production_semcog/configs/trip_destination_sample.csv index 4a2f5de66..6a0a53480 --- a/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv +++ b/activitysim/examples/production_semcog/configs/trip_destination_sample.csv @@ -1,13 +1,13 @@ Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork ,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 ,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if walk tour not within walking distance,@(df.tour_mode_is_walk) & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode_is_bike) & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 #stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice.csv new file mode 100644 index 000000000..d3f994cc0 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice.csv @@ -0,0 +1,373 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED +#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_for_joint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,coef_unavailable,,,,,,,,,,,,,,,,, +#util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_In-vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,, +#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,coef_unavailable,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,coef_unavailable,,,,,,,,,,,,,,,, +util_SHARED2_In-vehicle_time,SHARED2 - In-vehicle time,@odt_skims['HOV2_TIME'],,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Terminal_time,SHARED2 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr,,,,,,,,,,,,,,,, +util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr,,,,,,,,,,,,,,,, +util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age1619_da,,,,,,,,,,,,,,,, +#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,, +util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_Unavailable_based_joint_tour_mode,SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_Unavailable_if_tour_mode_is_shared_2,SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_In-vehicle_time,SHARED3 - In-vehicle time,@odt_skims['HOV3_TIME'],,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Terminal_time,SHARED3 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,, +util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,, +util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,, +#Walk,#Walk,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,, +#Bike,#Bike,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_tour_mode_is_not_bike,BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,coef_unavailable,,,,,,,,,,,,, +#util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,coef_unavailable,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,coef_ivt,,,,,,,,,,,,, +#Walk_to_Local,#Walk to Local,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,coef_unavailable,,,,,,,,,,,, +util_WALK_LOC_In-vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_IVT'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_XWAIT'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_NT']).clip(0),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * (df.origin_local_time),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * (df.dest_local_time),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WAUX'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * df.WLKLOC_fare_discount * odt_skims['WLK_LOC_FARE'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn,,,,,,,,,,,, +#Walk_to_Premium,#Walk to Premium,,,,,,,,,,,,,,,,,,, +util_WALK_PRM_Unavailable,WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,coef_unavailable,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time,WALK_PRM - In-vehicle time,@odt_skims['WLK_PRM_IVT'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_PMov,WALK_PRM - In-vehicle time on PMov,@(coef_ivt_pmov_multiplier - 1) * odt_skims['WLK_PRM_IVTT_PMov'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_StCar,WALK_PRM - In-vehicle time on StCar,@(coef_ivt_stcar_multiplier - 1) * odt_skims['WLK_PRM_IVTT_StCar'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_BRT,WALK_PRM - In-vehicle time on BRT,@(coef_ivt_brt_multiplier - 1) * odt_skims['WLK_PRM_IVTT_Brt'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_URB,WALK_PRM - In-vehicle time on URB,@(coef_ivt_urb_multiplier - 1) * odt_skims['WLK_PRM_IVTT_UrbRail'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_COM,WALK_PRM - In-vehicle time on COM,@(coef_ivt_com_multiplier - 1) * odt_skims['WLK_PRM_IVTT_ComRail'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Short_iwait_time,WALK_PRM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Long_iwait_time,WALK_PRM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_PRM_XWAIT'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_number_of_transfers,WALK_PRM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_PRM_NT']).clip(0),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@coef_wacc_multiplier * (df.origin_prm_time),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@coef_wegr_multiplier * (df.dest_prm_time),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_otherLight_rail/Ferry_time,WALK_PRM - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_PRM_WAUX'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.ivot * df.WLKPRM_fare_discount * odt_skims['WLK_PRM_FARE'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Destination_zone_densityIndex,WALK_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Person_is_less_than_10_years_old,WALK_PRM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn,,,,,,,,,,, +#Walk_to_Mix,#Walk to Mix,,,,,,,,,,,,,,,,,,, +util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,coef_unavailable,,,,,,,,,, +util_WALK_MIX_In-vehicle_time,WALK_MIX - In-vehicle time,@odt_skims['WLK_MIX_IVT'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_PMov,WALK_MIX - In-vehicle time on PMov,@(coef_ivt_pmov_multiplier - 1) * odt_skims['WLK_MIX_IVTT_PMov'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_StCar,WALK_MIX - In-vehicle time on StCar,@(coef_ivt_stcar_multiplier - 1) * odt_skims['WLK_MIX_IVTT_StCar'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_BRT,WALK_MIX - In-vehicle time on BRT,@(coef_ivt_brt_multiplier - 1) * odt_skims['WLK_MIX_IVTT_Brt'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_URB,WALK_MIX - In-vehicle time on URB,@(coef_ivt_urb_multiplier - 1) * odt_skims['WLK_MIX_IVTT_UrbRail'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_COM,WALK_MIX - In-vehicle time on COM,@(coef_ivt_com_multiplier - 1) * odt_skims['WLK_MIX_IVTT_ComRail'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Short_iwait_time,WALK_MIX - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Long_iwait_time,WALK_MIX - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_MIX_XWAIT'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_MIX_NT']).clip(0),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@coef_waux_multiplier * odt_skims['WLK_MIX_WAUX'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.ivot * df.WLKMIX_fare_discount * odt_skims['WLK_MIX_FARE'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Destination_zone_densityIndex,WALK_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Person_is_less_than_10_years_old,WALK_MIX - Person is less than 10 years old,@(df.age <= 10),,,,,,,,coef_age010_trn,,,,,,,,,, +#PNR_to_Local,#PNR to Local,,,,,,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable_for_zero_auto_households,PNR_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_Destination_zone_densityIndex,PNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Person_is_less_than_10_years_old,PNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,coef_age010_trn,,,,,,,,, +util_PNR_LOC_outbound_Unavailable,PNR_LOC outbound - Unavailable,outbound & ~pnr_local_available_outbound,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_outbound_In-vehicle_time,PNR_LOC outbound - In-vehicle time,@df.outbound * odt_skims['PNR_LOC_IVT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Short_iwait_time,PNR_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Long_iwait_time,PNR_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_transfer_wait_time,PNR_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_LOC_XWAIT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_number_of_transfers,PNR_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['PNR_LOC_NT']).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Drive_time,PNR_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_LOC_DTIME'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Walk_egress_time,PNR_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_local_time),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Walk_other_time,PNR_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_LOC_WAUX'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Fare_and_operating_cost,PNR_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.PNRLOC_fare_discount * odt_skims['PNR_LOC_FARE'] + costPerMile*odt_skims['PNR_LOC_DDIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Unavailable,PNR_LOC inbound - Unavailable,inbound & ~pnr_local_available_inbound,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_inbound_In-vehicle_time,PNR_LOC inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_LOC_IVT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Short_iwait_time,PNR_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Long_iwait_time,PNR_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_transfer_wait_time,PNR_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_LOC_XWAIT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_number_of_transfers,PNR_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['PNRE_LOC_NT']).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Drive_time,PNR_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_LOC_DTIME'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Walk_access_time,PNR_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_local_time),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Walk_other_time,PNR_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_LOC_WAUX'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Fare_and_operating_cost,PNR_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.PNRELOC_fare_discount * odt_skims['PNRE_LOC_FARE'] + costPerMile*odt_skims['PNRE_LOC_DDIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,coef_ivt,,,,,,,,, +#Drive_to_Premium,#Drive to Premium,,,,,,,,,,,,,,,,,,, +util_PNR_PRM_Unavailable_for_zero_auto_households,PNR_PRM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_Destination_zone_densityIndex,PNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Person_is_less_than_10_years_old,PNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn,,,,,,,, +util_PNR_PRM_outbound_Unavailable,PNR_PRM outbound - Unavailable,outbound & ~pnr_premium_available_outbound,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_outbound_In-vehicle_time,PNR_PRM outbound - In-vehicle time,@df.outbound * odt_skims['PNR_PRM_IVT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNR_PRM_IVTT_PMov'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNR_PRM_IVTT_StCar'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNR_PRM_IVTT_Brt'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNR_PRM_IVTT_UrbRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNR_PRM_IVTT_ComRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Short_iwait_time,PNR_PRM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Long_iwait_time,PNR_PRM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_transfer_wait_time,PNR_PRM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_PRM_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_number_of_transfers,PNR_PRM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['PNR_PRM_NT']).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Drive_time,PNR_PRM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_PRM_DTIME'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Walk_egress_time,PNR_PRM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_prm_time),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Walk_other_time,PNR_PRM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_PRM_WAUX'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Fare_and_operating_cost,PNR_PRM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.PNRPRM_fare_discount * odt_skims['PNR_PRM_FARE'] + costPerMile * odt_skims['PNR_PRM_DDIST']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Unavailable,PNR_PRM inbound - Unavailable,inbound & ~pnr_premium_available_inbound,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_inbound_In-vehicle_time,PNR_PRM inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_PRM_IVT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_PMov'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_StCar'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_Brt'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_UrbRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_ComRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Short_iwait_time,PNR_PRM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Long_iwait_time,PNR_PRM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_transfer_wait_time,PNR_PRM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_PRM_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_number_of_transfers,PNR_PRM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['PNRE_PRM_NT']).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Drive_time,PNR_PRM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_PRM_DTIME'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Walk_access_time,PNR_PRM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_prm_time),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Walk_other_time,PNR_PRM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_PRM_WAUX'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Fare_and_operating_cost,PNR_PRM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.PNREPRM_fare_discount * odt_skims['PNRE_PRM_FARE'] + costPerMile * odt_skims['PNRE_PRM_DDIST']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['PNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,coef_ivt,,,,,,,, +#PNR_to_Mix,#PNR to Mix,,,,,,,,,,,,,,,,,,, +util_PNR_MIX_Unavailable_for_zero_auto_households,PNR_MIX - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_Unavailable_for_persons_less_than_16,PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_Destination_zone_densityIndex,PNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Person_is_less_than_10_years_old,PNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_age010_trn,,,,,,, +util_PNR_MIX_outbound_Unavailable,PNR_MIX outbound - Unavailable,outbound & ~pnr_mix_available_outbound,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_outbound_In-vehicle_time,PNR_MIX outbound - In-vehicle time,@df.outbound * odt_skims['PNR_MIX_IVT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNR_MIX_IVTT_PMov'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNR_MIX_IVTT_StCar'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNR_MIX_IVTT_Brt'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNR_MIX_IVTT_UrbRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNR_MIX_IVTT_ComRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Short_iwait_time,PNR_MIX outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Long_iwait_time,PNR_MIX outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_transfer_wait_time,PNR_MIX outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_MIX_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_number_of_transfers,PNR_MIX outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['PNR_MIX_NT']).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Drive_time,PNR_MIX outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_MIX_DTIME'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Walk_egress_ime,PNR_MIX outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Walk_other_time,PNR_MIX outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_MIX_WAUX'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Fare_and_operating_cost,PNR_MIX outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.PNRMIX_fare_discount * odt_skims['PNR_MIX_FARE'] + costPerMile * odt_skims['PNR_MIX_DDIST']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Unavailable,PNR_MIX inbound - Unavailable,inbound & ~pnr_mix_available_inbound,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_inbound_In-vehicle_time,PNR_MIX inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_MIX_IVT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_PMov'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_StCar'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_Brt'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_ComRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Short_iwait_time,PNR_MIX inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Long_iwait_time,PNR_MIX inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_transfer_wait_time,PNR_MIX inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_MIX_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_number_of_transfers,PNR_MIX inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['PNRE_MIX_NT']).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Drive_time,PNR_MIX inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_MIX_DTIME'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Walk_access_time,PNR_MIX inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Walk_other_time,PNR_MIX inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_MIX_WAUX'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Fare_and_operating_cost,PNR_MIX inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.PNREMIX_fare_discount * odt_skims['PNRE_MIX_FARE'] + costPerMile * odt_skims['PNRE_MIX_DDIST']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['PNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,, +#KNR_to_Local,#KNR to Local,,,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable_for_persons_less_than_16,KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_Destination_zone_densityIndex,KNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Person_is_less_than_10_years_old,KNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_age010_trn,,,,,, +util_KNR_LOC_outbound_Unavailable,KNR_LOC outbound - Unavailable,outbound & ~knr_local_available_outbound,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_outbound_In-vehicle_time,KNR_LOC outbound - In-vehicle time,@df.outbound * odt_skims['KNR_LOC_IVT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Short_iwait_time,KNR_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Long_iwait_time,KNR_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_transfer_wait_time,KNR_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_LOC_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_number_of_transfers,KNR_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['KNR_LOC_NT']).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Drive_time,KNR_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_LOC_DTIME'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Walk_egress_time,KNR_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_local_time),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Walk_other_time,KNR_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_LOC_WAUX'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Fare_and_operating_cost,KNR_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.KNRLOC_fare_discount * odt_skims['KNR_LOC_FARE'] + costPerMile*odt_skims['KNR_LOC_DDIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Unavailable,KNR_LOC inbound - Unavailable,inbound & ~knr_local_available_inbound,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_inbound_In-vehicle_time,KNR_LOC inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_LOC_IVT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Short_iwait_time,KNR_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Long_iwait_time,KNR_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_transfer_wait_time,KNR_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_LOC_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_number_of_transfers,KNR_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['KNRE_LOC_NT']).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Drive_time,KNR_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_LOC_DTIME'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Walk_access_time,KNR_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_local_time),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Walk_other_time,KNR_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_LOC_WAUX'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Fare_and_operating_cost,KNR_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.KNRELOC_fare_discount * odt_skims['KNRE_LOC_FARE'] + costPerMile*odt_skims['KNRE_LOC_DDIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,coef_ivt,,,,,, +#Drive_to_Premium,#Drive to Premium,,,,,,,,,,,,,,,,,,, +util_KNR_PRM_Unavailable_for_persons_less_than_16,KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_Destination_zone_densityIndex,KNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Person_is_less_than_10_years_old,KNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,, +util_KNR_PRM_outbound_Unavailable,KNR_PRM outbound - Unavailable,outbound & ~knr_premium_available_outbound,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_outbound_In-vehicle_time,KNR_PRM outbound - In-vehicle time,@df.outbound * odt_skims['KNR_PRM_IVT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNR_PRM_IVTT_PMov'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNR_PRM_IVTT_StCar'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNR_PRM_IVTT_Brt'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNR_PRM_IVTT_UrbRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNR_PRM_IVTT_ComRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Short_iwait_time,KNR_PRM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Long_iwait_time,KNR_PRM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_transfer_wait_time,KNR_PRM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_PRM_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_number_of_transfers,KNR_PRM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KNR_PRM_NT']).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Drive_time,KNR_PRM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_PRM_DTIME'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Walk_egress_time,KNR_PRM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_prm_time),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Walk_other_time,KNR_PRM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_PRM_WAUX'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Fare_and_operating_cost,KNR_PRM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.KNRPRM_fare_discount * odt_skims['KNR_PRM_FARE'] + costPerMile * odt_skims['KNR_PRM_DDIST']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Unavailable,KNR_PRM inbound - Unavailable,inbound & ~knr_premium_available_inbound,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_inbound_In-vehicle_time,KNR_PRM inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_PRM_IVT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_PMov'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_StCar'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_Brt'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_UrbRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_ComRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Short_iwait_time,KNR_PRM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Long_iwait_time,KNR_PRM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_transfer_wait_time,KNR_PRM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_PRM_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_number_of_transfers,KNR_PRM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['KNRE_PRM_NT']).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Drive_time,KNR_PRM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_PRM_DTIME'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Walk_access_time,KNR_PRM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_prm_time),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Walk_other_time,KNR_PRM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_PRM_WAUX'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Fare_and_operating_cost,KNR_PRM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.KNREPRM_fare_discount * odt_skims['KNRE_PRM_FARE'] + costPerMile * odt_skims['KNRE_PRM_DDIST']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['KNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,, +#KNR_to_Mix,#KNR to Mix,,,,,,,,,,,,,,,,,,, +util_KNR_MIX_Unavailable_for_persons_less_than_16,KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_Destination_zone_densityIndex,KNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Person_is_less_than_10_years_old,KNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,, +util_KNR_MIX_outbound_Unavailable,KNR_MIX outbound - Unavailable,outbound & ~knr_mix_available_outbound,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_outbound_In-vehicle_time,KNR_MIX outbound - In-vehicle time,@df.outbound * odt_skims['KNR_MIX_IVT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNR_MIX_IVTT_PMov'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNR_MIX_IVTT_StCar'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNR_MIX_IVTT_Brt'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNR_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNR_MIX_IVTT_ComRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Short_iwait_time,KNR_MIX outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Long_iwait_time,KNR_MIX outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_transfer_wait_time,KNR_MIX outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_MIX_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_number_of_transfers,KNR_MIX outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KNR_MIX_NT']).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Drive_time,KNR_MIX outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_MIX_DTIME'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Walk_egress_ime,KNR_MIX outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Walk_other_time,KNR_MIX outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_MIX_WAUX'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Fare_and_operating_cost,KNR_MIX outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (df.KNRMIX_fare_discount * odt_skims['KNR_MIX_FARE'] + costPerMile * odt_skims['KNR_MIX_DDIST']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Unavailable,KNR_MIX inbound - Unavailable,inbound & ~knr_mix_available_inbound,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_inbound_In-vehicle_time,KNR_MIX inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_MIX_IVT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_PMov'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_StCar'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_Brt'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_ComRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Short_iwait_time,KNR_MIX inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Long_iwait_time,KNR_MIX inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_transfer_wait_time,KNR_MIX inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_MIX_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_number_of_transfers,KNR_MIX inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['KNRE_MIX_NT']).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Drive_time,KNR_MIX inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_MIX_DTIME'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Walk_access_time,KNR_MIX inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Walk_other_time,KNR_MIX inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_MIX_WAUX'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Fare_and_operating_cost,KNR_MIX inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (df.KNREMIX_fare_discount * odt_skims['KNRE_MIX_FARE'] + costPerMile * odt_skims['KNRE_MIX_DDIST']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['KNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,coef_ivt,,,, +#Taxi,#Taxi,,,,,,,,,,,,,,,,,,, +util_Taxi_In-vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,coef_ivt,, +util_TNC_Single_In-vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Shared_In-vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,coef_ivt +#,#,,,,,,,,,,,,,,,,,,, +util_SOV_tour_mode_availability,SOV tour mode availability,tour_mode_is_SOV,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_sr2_tour_mode_availability,SR2 tour mode availability,tour_mode_is_sr2,,,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_sr3p_tour_mode_availability,SR3+ tour mode availability,tour_mode_is_sr3p,,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Walk_tour_mode_availability,Walk tour mode availability,tour_mode_is_walk,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Bike_tour_mode_availability,Bike tour mode availability,tour_mode_is_bike,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Walk_to_Transit_tour_mode_availability,Walk to Transit tour mode availability,tour_mode_is_walk_transit,coef_unavailable,,,,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,, +util_drive_to_Transit_tour_mode_availability,drive Transit tour mode availability,tour_mode_is_drive_transit,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,,,,,coef_unavailable,,, +#util_PNR_to_Transit_tour_mode_availability,PNR Transit tour mode availability,tour_mode_is_pnr_transit,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,, +#util_KNR_to_Transit_tour_mode_availability,KNR Transit tour mode availability,tour_mode_is_knr_transit,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_School_bus_tour_mode_availability,School bus tour mode availability,tour_mode_is_school_bus,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_Ride_Hail_tour_mode_availability,Ride Hail tour mode availability,tour_mode_is_ride_hail,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,, +#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,sov_ASC_walk,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,sov_ASC_taxi,sov_ASC_tnc_single,sov_ASC_tnc_shared +util_Shared_Ride_2_tour_mode_ASC_drive_alone,Shared Ride 2 tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),sr2_ASC_sov,,,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,sr2_ASC_taxi,sr2_ASC_tnc_single,sr2_ASC_tnc_shared +util_Shared_Ride_3+_tour_mode_ASC_drive_alone,Shared Ride 3+ tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),sr3p_ASC_sov,,,,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,sr3p_ASC_sr2,,,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,sr3p_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,sr3p_ASC_taxi,sr3p_ASC_tnc_single,sr3p_ASC_tnc_shared +#util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,walk_ASC_taxi,walk_ASC_tnc_single,walk_ASC_tnc_shared +util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,bike_ASC_walk,,,,,,,,,,,,,, +#util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,bike_ASC_taxi,bike_ASC_tnc_single,bike_ASC_tnc_shared +util_Walk-transit_tour_mode_ASC_shared_ride_2,Walk-transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,walk_transit_ASC_sr2,,,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_shared_ride_3+,Walk-transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_walk,Walk-transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,walk_transit_ASC_walk,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_ride_hail,Walk-transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,walk_transit_ASC_taxi,walk_transit_ASC_tnc_single,walk_transit_ASC_tnc_shared +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_pnr_transit),,,,,,,,,,,,,,,,pnr_ASC_taxi,pnr_ASC_tnc_single,pnr_ASC_tnc_shared +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_knr_transit),,,,,,,,,,,,,,,,knr_ASC_taxi,knr_ASC_tnc_single,knr_ASC_tnc_shared +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- shared 2,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,ride_hail_ASC_sr2,,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- shared 3+,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,ride_hail_ASC_walk,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- walk-transit,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,ride_hail_ASC_taxi,,ride_hail_ASC_tnc_shared +util_Shared_Ride_2_tour_mode_ASC_walk,joint - sr2 tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,,,joint_sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_walk,joint - sr3p tour mode ASC,@(df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,joint_sr3p_ASC_sr2,,joint_sr3p_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_shared_ride_2,joint - Walk-transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_shared_ride_3+,joint - Walk-transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_walk,joint - Walk-transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,joint_walk_transit_ASC_taxi,joint_walk_transit_ASC_tnc_single,joint_walk_transit_ASC_tnc_shared +util_joint_PNR_to_Transit_tour_mode_ASC_ride_hail,joint - PNR to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_pnr_transit),,,,,,,,,,,,,,,,joint_pnr_ASC_taxi,joint_pnr_ASC_tnc_single,joint_pnr_ASC_tnc_shared +util_joint_KNR_to_Transit_tour_mode_ASC_ride_hail,joint - KNR to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_knr_transit),,,,,,,,,,,,,,,,joint_knr_ASC_taxi,joint_knr_ASC_tnc_single,joint_knr_ASC_tnc_shared +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- shared 2,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- shared 3+,@ (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- taxi,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,,joint_ride_hail_ASC_tnc_shared +util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,coef_unavailable,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,coef_unavailable,,,,,,,,,,,,, +#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,, +util_School_Bus_Unavailable_if_primary_purpose_NOT_school,School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_Unavailable_Tour_Mode_=_SOV,School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_Unavailable_Tour_Mode_=_Transit,School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_In-vehicle_time_(20_miles_per_hour),School Bus - In-vehicle time (20 miles per hour),@odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_Walk_Time,School Bus - Walk Time,@coef_wacc_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_Wait_Time,School Bus - Wait Time,@coef_short_iwait_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_tour_mode_ASC_shared_ride_2,School Bus tour mode ASC -- shared ride 2,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,schoolbus_ASC_sr2,,,,,,,,,,,,,,,, +util_School_Bus_tour_mode_ASC_shared_ride_3+,School Bus tour mode ASC -- shared ride 3+,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,schoolbus_ASC_sr3p,,,,,,,,,,,,,,, +util_School_Bus_tour_mode_ASC_walk,School Bus tour mode ASC -- walk,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,schoolbus_ASC_walk,,,,,,,,,,,,,, +util_Origin_density_index,Origin density index,@(origin_density_applied*df.origin_density_index).clip(origin_density_index_max) if origin_density_applied else 0,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,coef_ivt,coef_ivt +util_TNC_shared_adjustment,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,coef_ivt +util_umt_unavailable_wlk,UMT unavailable for non affiliates WALK,@((odt_skims['WLK_LOC_IVTT_UMT'] + dot_skims['WLK_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,coef_unavailable,,,,,,,,,,,, +util_umt_unavailable_pnr,UMT unavailable for non affiliates PNR,@((odt_skims['PNR_LOC_IVTT_UMT'] + dot_skims['PNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,,,,coef_unavailable,,,,,,,,, +util_umt_unavailable_knr,UMT unavailable for non affiliates KNR,@((odt_skims['KNR_LOC_IVTT_UMT'] + dot_skims['KNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,,,,,,,coef_unavailable,,,,,, +#,,,,,,,,,,,,,,,,,,,, +util_walk_DPM_asc,,PMOV_transit,,,,,,,0.55,,,0.55,,,0.55,,,,, +#util_walk_DPMwTransfer_asc,,PMOV_transit_wTransfer,,,,,,,,0.05,,,0.075,,,0.05,,,, +util_walk_StCar_asc,,StCar_transit,,,,,,,0.45,,,0.45,,,0.45,,,,, +#util_walk_StcarwTransfer_asc,,StCar_transit_wTransfer,,,,,,,,0.05,,,0.05,,,0.05,,,, +util_AATA_transit,,AATA_transit,,,,,,-0.2,,,-0.2,,,-0.2,,,,,, +util_district1_15_cte,districts15_cte,districts15_cte,,,,,,coef_transit_district15,,,coef_transit_district15,,,coef_transit_district15,,,,,, +util_districts1_2_cte,,districts1_2_cte,,,,,,1,,,1,,,1,,,,,, diff --git a/activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml b/activitysim/examples/production_semcog/configs/trip_mode_choice.yaml old mode 100755 new mode 100644 similarity index 80% rename from activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml rename to activitysim/examples/production_semcog/configs/trip_mode_choice.yaml index c5f224994..c25a88c43 --- a/activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice.yaml @@ -1,6 +1,8 @@ +# trip_mode_choice.yaml + SPEC: trip_mode_choice.csv -#COEFFICIENTS: trip_mode_choice_coeffs.csv -LEGACY_COEFFICIENTS: trip_mode_choice_coeffs.csv +COEFFICIENTS: trip_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: trip_mode_choice_coefficients_template.csv LOGIT_TYPE: NL @@ -54,16 +56,16 @@ NESTS: CONSTANTS: orig_col_name: origin dest_col_name: destination - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.00 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 + # costPerMile: 18.29 + # costShareSr2: 1.75 + # costShareSr3: 2.50 + # waitThresh: 10.00 + # walkThresh: 1.00 + # shortWalk: 0.333 + # longWalk: 0.667 + # walkSpeed: 3.00 + # bikeThresh: 6.00 + # bikeSpeed: 12.00 # maxCbdAreaTypeThresh: 2 # indivTour: 1.00000 # upperEA: 5 @@ -152,6 +154,19 @@ CONSTANTS: 5: 0 min_waitTime: 0 max_waitTime: 50 + ivt_cost_multiplier: 0.6 + ridehail_wait_time_multiplier: 1.5 + walktimelong_multiplier: 5 + biketimelong_multiplier: 5 + xfers_wlk_multiplier: 5 + xfers_drv_multiplier: 15 + dacc_ratio_multiplier: 0 + density_index_multiplier: -5 + origin_density_index_multiplier: -15 + origin_density_index_max: -15 + ivt_com_multiplier: 0.80 + ivt_exp_multiplier: -0.0175 + ivt_hvy_multiplier: 0.80 # so far, we can use the same spec as for non-joint tours preprocessor: @@ -160,6 +175,7 @@ preprocessor: TABLES: - land_use - tours + - persons # - SPEC: trip_mode_choice_annotate_trips_preprocessor2 # DF: df @@ -179,6 +195,10 @@ TOURS_MERGED_CHOOSER_COLUMNS: - value_of_time - tour_type - free_parking_at_work + - ptype + - transit_pass_ownership + - transit_pass_subsidy + MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice_.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_.csv new file mode 100644 index 000000000..601ac7d2d --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_.csv @@ -0,0 +1,379 @@ +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED +#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_for_joint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,coef_unavailable,,,,,,,,,,,,,,,,, +#util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,coef_unavailable,,,,,,,,,,,,,,,,, +util_DRIVEALONE_In-vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,, +#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,coef_unavailable,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,coef_unavailable,,,,,,,,,,,,,,,, +util_SHARED2_In-vehicle_time,SHARED2 - In-vehicle time,@odt_skims['HOV2_TIME'],,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Terminal_time,SHARED2 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr,,,,,,,,,,,,,,,, +util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr,,,,,,,,,,,,,,,, +util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age1619_da,,,,,,,,,,,,,,,, +#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,, +util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_Unavailable_based_joint_tour_mode,SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_Unavailable_if_tour_mode_is_shared_2,SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,coef_unavailable,,,,,,,,,,,,,,, +util_SHARED3_In-vehicle_time,SHARED3 - In-vehicle time,@odt_skims['HOV3_TIME'],,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Terminal_time,SHARED3 - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,, +util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,, +util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,, +#Walk,#Walk,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,coef_ivt,,,,,,,,,,,,,, +#Bike,#Bike,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_tour_mode_is_not_bike,BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,coef_unavailable,,,,,,,,,,,,, +#util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,coef_unavailable,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,coef_ivt,,,,,,,,,,,,, +#Walk_to_Local,#Walk to Local,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,coef_unavailable,,,,,,,,,,,, +util_WALK_LOC_In-vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_IVT'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_XWAIT'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_NT']).clip(0),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * (df.origin_local_time),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * (df.dest_local_time),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WAUX'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LOC_FARE'],,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn,,,,,,,,,,,, +#Walk_to_Premium,#Walk to Premium,,,,,,,,,,,,,,,,,,, +util_WALK_PRM_Unavailable,WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,coef_unavailable,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time,WALK_PRM - In-vehicle time,@odt_skims['WLK_PRM_IVT'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_PMov,WALK_PRM - In-vehicle time on PMov,@(coef_ivt_pmov_multiplier - 1) * odt_skims['WLK_PRM_IVTT_PMov'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_StCar,WALK_PRM - In-vehicle time on StCar,@(coef_ivt_stcar_multiplier - 1) * odt_skims['WLK_PRM_IVTT_StCar'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_BRT,WALK_PRM - In-vehicle time on BRT,@(coef_ivt_brt_multiplier - 1) * odt_skims['WLK_PRM_IVTT_Brt'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_URB,WALK_PRM - In-vehicle time on URB,@(coef_ivt_urb_multiplier - 1) * odt_skims['WLK_PRM_IVTT_UrbRail'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In-vehicle_time_on_COM,WALK_PRM - In-vehicle time on COM,@(coef_ivt_com_multiplier - 1) * odt_skims['WLK_PRM_IVTT_ComRail'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Short_iwait_time,WALK_PRM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Long_iwait_time,WALK_PRM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_PRM_XWAIT'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_number_of_transfers,WALK_PRM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_PRM_NT']).clip(0),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@coef_wacc_multiplier * (df.origin_prm_time),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@coef_wegr_multiplier * (df.dest_prm_time),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_otherLight_rail/Ferry_time,WALK_PRM - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_PRM_WAUX'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_PRM_FARE'],,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Destination_zone_densityIndex,WALK_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Topology,WALK_PRM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Person_is_less_than_10_years_old,WALK_PRM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn,,,,,,,,,,, +#Walk_to_Mix,#Walk to Mix,,,,,,,,,,,,,,,,,,, +util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,coef_unavailable,,,,,,,,,, +util_WALK_MIX_In-vehicle_time,WALK_MIX - In-vehicle time,@odt_skims['WLK_MIX_IVT'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_Bus,WALK_MIX - In-vehicle time on Bus,@odt_skims['WLK_MIX_IVTT_Bus'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_PMov,WALK_MIX - In-vehicle time on PMov,@(coef_ivt_pmov_multiplier - 1) * odt_skims['WLK_MIX_IVTT_PMov'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_StCar,WALK_MIX - In-vehicle time on StCar,@(coef_ivt_stcar_multiplier - 1) * odt_skims['WLK_MIX_IVTT_StCar'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_BRT,WALK_MIX - In-vehicle time on BRT,@(coef_ivt_brt_multiplier - 1) * odt_skims['WLK_MIX_IVTT_Brt'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_URB,WALK_MIX - In-vehicle time on URB,@(coef_ivt_urb_multiplier - 1) * odt_skims['WLK_MIX_IVTT_UrbRail'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In-vehicle_time_on_COM,WALK_MIX - In-vehicle time on COM,@(coef_ivt_com_multiplier - 1) * odt_skims['WLK_MIX_IVTT_ComRail'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Short_iwait_time,WALK_MIX - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Long_iwait_time,WALK_MIX - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_MIX_XWAIT'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_MIX_NT']).clip(0),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@coef_waux_multiplier * odt_skims['WLK_MIX_WAUX'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_MIX_FARE'],,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Destination_zone_densityIndex,WALK_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Topology,WALK_MIX - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Person_is_less_than_10_years_old,WALK_MIX - Person is less than 10 years old,@(df.age <= 10),,,,,,,,coef_age010_trn,,,,,,,,,, +#PNR_to_Local,#PNR to Local,,,,,,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable_for_zero_auto_households,PNR_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_Destination_zone_densityIndex,PNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Topology,PNR_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Person_is_less_than_10_years_old,PNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,coef_age010_trn,,,,,,,,, +util_PNR_LOC_outbound_Unavailable,PNR_LOC outbound - Unavailable,outbound & ~pnr_local_available_outbound,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_outbound_In-vehicle_time,PNR_LOC outbound - In-vehicle time,@df.outbound * odt_skims['PNR_LOC_IVT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Short_iwait_time,PNR_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Long_iwait_time,PNR_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_transfer_wait_time,PNR_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_LOC_XWAIT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_number_of_transfers,PNR_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['PNR_LOC_NT']).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Drive_time,PNR_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_LOC_DTIME'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Walk_egress_time,PNR_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_local_time),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Walk_other_time,PNR_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_LOC_WAUX'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Fare_and_operating_cost,PNR_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNR_LOC_FARE'] + costPerMile*odt_skims['PNR_LOC_DDIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Unavailable,PNR_LOC inbound - Unavailable,inbound & ~pnr_local_available_inbound,,,,,,,,,coef_unavailable,,,,,,,,, +util_PNR_LOC_inbound_In-vehicle_time,PNR_LOC inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_LOC_IVT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Short_iwait_time,PNR_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Long_iwait_time,PNR_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_transfer_wait_time,PNR_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_LOC_XWAIT'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_number_of_transfers,PNR_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['PNRE_LOC_NT']).clip(0),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Drive_time,PNR_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_LOC_DTIME'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Walk_access_time,PNR_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_local_time),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Walk_other_time,PNR_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_LOC_WAUX'],,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Fare_and_operating_cost,PNR_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNRE_LOC_FARE'] + costPerMile*odt_skims['PNRE_LOC_DDIST']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,coef_ivt,,,,,,,,, +#Drive_to_Premium,#Drive to Premium,,,,,,,,,,,,,,,,,,, +util_PNR_PRM_Unavailable_for_zero_auto_households,PNR_PRM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_Destination_zone_densityIndex,PNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Topology,PNR_PRM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Person_is_less_than_10_years_old,PNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn,,,,,,,, +util_PNR_PRM_outbound_Unavailable,PNR_PRM outbound - Unavailable,outbound & ~pnr_premium_available_outbound,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_outbound_In-vehicle_time,PNR_PRM outbound - In-vehicle time,@df.outbound * odt_skims['PNR_PRM_IVT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNR_PRM_IVTT_PMov'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNR_PRM_IVTT_StCar'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNR_PRM_IVTT_Brt'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNR_PRM_IVTT_UrbRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNR_PRM_IVTT_ComRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Short_iwait_time,PNR_PRM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Long_iwait_time,PNR_PRM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_transfer_wait_time,PNR_PRM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_PRM_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_number_of_transfers,PNR_PRM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['PNR_PRM_NT']).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Drive_time,PNR_PRM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_PRM_DTIME'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Walk_egress_time,PNR_PRM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_prm_time),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Walk_other_time,PNR_PRM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_PRM_WAUX'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Fare_and_operating_cost,PNR_PRM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNR_PRM_FARE'] + costPerMile * odt_skims['PNR_PRM_DDIST']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Unavailable,PNR_PRM inbound - Unavailable,inbound & ~pnr_premium_available_inbound,,,,,,,,,,coef_unavailable,,,,,,,, +util_PNR_PRM_inbound_In-vehicle_time,PNR_PRM inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_PRM_IVT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_PMov'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_StCar'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_Brt'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_UrbRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In-vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNRE_PRM_IVTT_ComRail'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Short_iwait_time,PNR_PRM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Long_iwait_time,PNR_PRM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_transfer_wait_time,PNR_PRM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_PRM_XWAIT'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_number_of_transfers,PNR_PRM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['PNRE_PRM_NT']).clip(0),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Drive_time,PNR_PRM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_PRM_DTIME'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Walk_access_time,PNR_PRM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_prm_time),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Walk_other_time,PNR_PRM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_PRM_WAUX'],,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Fare_and_operating_cost,PNR_PRM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNRE_PRM_FARE'] + costPerMile * odt_skims['PNRE_PRM_DDIST']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['PNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,coef_ivt,,,,,,,, +#PNR_to_Mix,#PNR to Mix,,,,,,,,,,,,,,,,,,, +util_PNR_MIX_Unavailable_for_zero_auto_households,PNR_MIX - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_Unavailable_for_persons_less_than_16,PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_Destination_zone_densityIndex,PNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Topology,PNR_MIX - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Person_is_less_than_10_years_old,PNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_age010_trn,,,,,,, +util_PNR_MIX_outbound_Unavailable,PNR_MIX outbound - Unavailable,outbound & ~pnr_mix_available_outbound,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_outbound_In-vehicle_time,PNR_MIX outbound - In-vehicle time,@df.outbound * odt_skims['PNR_MIX_IVT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_Bus,PNR_MIX - In-vehicle time on Bus,@df.outbound * odt_skims['PNR_MIX_IVTT_Bus'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNR_MIX_IVTT_PMov'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNR_MIX_IVTT_StCar'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNR_MIX_IVTT_Brt'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNR_MIX_IVTT_UrbRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNR_MIX_IVTT_ComRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Short_iwait_time,PNR_MIX outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Long_iwait_time,PNR_MIX outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_transfer_wait_time,PNR_MIX outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['PNR_MIX_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_number_of_transfers,PNR_MIX outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['PNR_MIX_NT']).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Drive_time,PNR_MIX outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['PNR_MIX_DTIME'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Walk_egress_ime,PNR_MIX outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Walk_other_time,PNR_MIX outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['PNR_MIX_WAUX'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Fare_and_operating_cost,PNR_MIX outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNR_MIX_FARE'] + costPerMile * odt_skims['PNR_MIX_DDIST']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_outbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['PNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Unavailable,PNR_MIX inbound - Unavailable,inbound & ~pnr_mix_available_inbound,,,,,,,,,,,coef_unavailable,,,,,,, +util_PNR_MIX_inbound_In-vehicle_time,PNR_MIX inbound - In-vehicle time,@df.inbound * odt_skims['PNRE_MIX_IVT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_Bus,PNR_MIX - In-vehicle time on Bus,@df.inbound * odt_skims['PNRE_MIX_IVTT_Bus'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_PMov'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_StCar'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_Brt'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In-vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['PNRE_MIX_IVTT_ComRail'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Short_iwait_time,PNR_MIX inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Long_iwait_time,PNR_MIX inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_transfer_wait_time,PNR_MIX inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['PNRE_MIX_XWAIT'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_number_of_transfers,PNR_MIX inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['PNRE_MIX_NT']).clip(0),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Drive_time,PNR_MIX inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['PNRE_MIX_DTIME'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Walk_access_time,PNR_MIX inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Walk_other_time,PNR_MIX inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['PNRE_MIX_WAUX'],,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Fare_and_operating_cost,PNR_MIX inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['PNRE_MIX_FARE'] + costPerMile * odt_skims['PNRE_MIX_DDIST']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_inbound_Ratio_of_drive_access_distance_to_OD_distance,PNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['PNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,coef_ivt,,,,,,, +#KNR_to_Local,#KNR to Local,,,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable_for_persons_less_than_16,KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_Destination_zone_densityIndex,KNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Topology,KNR_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Person_is_less_than_10_years_old,KNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_age010_trn,,,,,, +util_KNR_LOC_outbound_Unavailable,KNR_LOC outbound - Unavailable,outbound & ~knr_local_available_outbound,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_outbound_In-vehicle_time,KNR_LOC outbound - In-vehicle time,@df.outbound * odt_skims['KNR_LOC_IVT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Short_iwait_time,KNR_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Long_iwait_time,KNR_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_transfer_wait_time,KNR_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_LOC_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_number_of_transfers,KNR_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['KNR_LOC_NT']).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Drive_time,KNR_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_LOC_DTIME'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Walk_egress_time,KNR_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_local_time),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Walk_other_time,KNR_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_LOC_WAUX'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Fare_and_operating_cost,KNR_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNR_LOC_FARE'] + costPerMile*odt_skims['KNR_LOC_DDIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Unavailable,KNR_LOC inbound - Unavailable,inbound & ~knr_local_available_inbound,,,,,,,,,,,,coef_unavailable,,,,,, +util_KNR_LOC_inbound_In-vehicle_time,KNR_LOC inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_LOC_IVT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Short_iwait_time,KNR_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Long_iwait_time,KNR_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_transfer_wait_time,KNR_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_LOC_XWAIT'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_number_of_transfers,KNR_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['KNRE_LOC_NT']).clip(0),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Drive_time,KNR_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_LOC_DTIME'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Walk_access_time,KNR_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_local_time),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Walk_other_time,KNR_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_LOC_WAUX'],,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Fare_and_operating_cost,KNR_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNRE_LOC_FARE'] + costPerMile*odt_skims['KNRE_LOC_DDIST']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,coef_ivt,,,,,, +#Drive_to_Premium,#Drive to Premium,,,,,,,,,,,,,,,,,,, +util_KNR_PRM_Unavailable_for_persons_less_than_16,KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_Destination_zone_densityIndex,KNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Topology,KNR_PRM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Person_is_less_than_10_years_old,KNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,, +util_KNR_PRM_outbound_Unavailable,KNR_PRM outbound - Unavailable,outbound & ~knr_premium_available_outbound,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_outbound_In-vehicle_time,KNR_PRM outbound - In-vehicle time,@df.outbound * odt_skims['KNR_PRM_IVT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNR_PRM_IVTT_PMov'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNR_PRM_IVTT_StCar'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNR_PRM_IVTT_Brt'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNR_PRM_IVTT_UrbRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNR_PRM_IVTT_ComRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Short_iwait_time,KNR_PRM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Long_iwait_time,KNR_PRM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_transfer_wait_time,KNR_PRM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_PRM_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_number_of_transfers,KNR_PRM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KNR_PRM_NT']).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Drive_time,KNR_PRM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_PRM_DTIME'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Walk_egress_time,KNR_PRM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * (df.dest_prm_time),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Walk_other_time,KNR_PRM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_PRM_WAUX'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Fare_and_operating_cost,KNR_PRM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNR_PRM_FARE'] + costPerMile * odt_skims['KNR_PRM_DDIST']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Unavailable,KNR_PRM inbound - Unavailable,inbound & ~knr_premium_available_inbound,,,,,,,,,,,,,coef_unavailable,,,,, +util_KNR_PRM_inbound_In-vehicle_time,KNR_PRM inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_PRM_IVT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_PMov'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_StCar'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_Brt'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_UrbRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In-vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNRE_PRM_IVTT_ComRail'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Short_iwait_time,KNR_PRM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Long_iwait_time,KNR_PRM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_transfer_wait_time,KNR_PRM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_PRM_XWAIT'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_number_of_transfers,KNR_PRM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['KNRE_PRM_NT']).clip(0),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Drive_time,KNR_PRM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_PRM_DTIME'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Walk_access_time,KNR_PRM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_prm_time),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Walk_other_time,KNR_PRM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_PRM_WAUX'],,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Fare_and_operating_cost,KNR_PRM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNRE_PRM_FARE'] + costPerMile * odt_skims['KNRE_PRM_DDIST']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['KNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,coef_ivt,,,,, +#KNR_to_Mix,#KNR to Mix,,,,,,,,,,,,,,,,,,, +util_KNR_MIX_Unavailable_for_persons_less_than_16,KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_Destination_zone_densityIndex,KNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Topology,KNR_MIX - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Person_is_less_than_10_years_old,KNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,, +util_KNR_MIX_outbound_Unavailable,KNR_MIX outbound - Unavailable,outbound & ~knr_mix_available_outbound,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_outbound_In-vehicle_time,KNR_MIX outbound - In-vehicle time,@df.outbound * odt_skims['KNR_MIX_IVT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_Bus,KNR_MIX - In-vehicle time on Bus,@df.outbound * odt_skims['KNR_MIX_IVTT_Bus'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@df.outbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNR_MIX_IVTT_PMov'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@df.outbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNR_MIX_IVTT_StCar'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@df.outbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNR_MIX_IVTT_Brt'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@df.outbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNR_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@df.outbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNR_MIX_IVTT_ComRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Short_iwait_time,KNR_MIX outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Long_iwait_time,KNR_MIX outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_transfer_wait_time,KNR_MIX outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['KNR_MIX_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_number_of_transfers,KNR_MIX outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['KNR_MIX_NT']).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Drive_time,KNR_MIX outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['KNR_MIX_DTIME'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Walk_egress_ime,KNR_MIX outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * (df.dest_mix_time),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Walk_other_time,KNR_MIX outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['KNR_MIX_WAUX'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Fare_and_operating_cost,KNR_MIX outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNR_MIX_FARE'] + costPerMile * odt_skims['KNR_MIX_DDIST']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_outbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['KNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Unavailable,KNR_MIX inbound - Unavailable,inbound & ~knr_mix_available_inbound,,,,,,,,,,,,,,coef_unavailable,,,, +util_KNR_MIX_inbound_In-vehicle_time,KNR_MIX inbound - In-vehicle time,@df.inbound * odt_skims['KNRE_MIX_IVT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_Bus,KNR_MIX - In-vehicle time on Bus,@df.inbound * odt_skims['KNRE_MIX_IVTT_Bus'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@df.inbound * (coef_ivt_pmov_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_PMov'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@df.inbound * (coef_ivt_stcar_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_StCar'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@df.inbound * (coef_ivt_brt_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_Brt'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@df.inbound * (coef_ivt_urb_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In-vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@df.inbound * (coef_ivt_com_multiplier - 1) * odt_skims['KNRE_MIX_IVTT_ComRail'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Short_iwait_time,KNR_MIX inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Long_iwait_time,KNR_MIX inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_transfer_wait_time,KNR_MIX inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['KNRE_MIX_XWAIT'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_number_of_transfers,KNR_MIX inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['KNRE_MIX_NT']).clip(0),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Drive_time,KNR_MIX inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['KNRE_MIX_DTIME'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Walk_access_time,KNR_MIX inbound - Walk access time,@df.inbound * coef_wacc_multiplier * (df.origin_mix_time),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Walk_other_time,KNR_MIX inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['KNRE_MIX_WAUX'],,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Fare_and_operating_cost,KNR_MIX inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['KNRE_MIX_FARE'] + costPerMile * odt_skims['KNRE_MIX_DDIST']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_inbound_Ratio_of_drive_access_distance_to_OD_distance,KNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['KNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,coef_ivt,,,, +#Taxi,#Taxi,,,,,,,,,,,,,,,,,,, +util_Taxi_In-vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,coef_ivt,, +util_TNC_Single_In-vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Shared_In-vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,coef_ivt +#,#,,,,,,,,,,,,,,,,,,, +util_SOV_tour_mode_availability,SOV tour mode availability,tour_mode_is_SOV,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_sr2_tour_mode_availability,SR2 tour mode availability,tour_mode_is_sr2,,,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_sr3p_tour_mode_availability,SR3+ tour mode availability,tour_mode_is_sr3p,,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Walk_tour_mode_availability,Walk tour mode availability,tour_mode_is_walk,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Bike_tour_mode_availability,Bike tour mode availability,tour_mode_is_bike,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_Walk_to_Transit_tour_mode_availability,Walk to Transit tour mode availability,tour_mode_is_walk_transit,coef_unavailable,,,,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,, +util_Drive_to_Transit_tour_mode_availability,Drive to Transit tour mode availability,tour_mode_is_drive_transit,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,,,,,,coef_unavailable,,, +util_School_bus_tour_mode_availability,School bus tour mode availability,tour_mode_is_school_bus,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +util_Ride_Hail_tour_mode_availability,Ride Hail tour mode availability,tour_mode_is_ride_hail,coef_unavailable,,,,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,,, +#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,sov_ASC_walk,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,sov_ASC_taxi,sov_ASC_tnc_single,sov_ASC_tnc_shared +util_Shared_Ride_2_tour_mode_ASC_drive_alone,Shared Ride 2 tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),sr2_ASC_sov,,,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,sr2_ASC_taxi,sr2_ASC_tnc_single,sr2_ASC_tnc_shared +util_Shared_Ride_3+_tour_mode_ASC_drive_alone,Shared Ride 3+ tour mode ASC -- drive alone,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),sr3p_ASC_sov,,,,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,sr3p_ASC_sr2,,,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,sr3p_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,sr3p_ASC_taxi,sr3p_ASC_tnc_single,sr3p_ASC_tnc_shared +#util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,walk_ASC_taxi,walk_ASC_tnc_single,walk_ASC_tnc_shared +util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,bike_ASC_walk,,,,,,,,,,,,,, +#util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,bike_ASC_taxi,bike_ASC_tnc_single,bike_ASC_tnc_shared +util_Walk-transit_tour_mode_ASC_shared_ride_2,Walk-transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,walk_transit_ASC_sr2,,,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_shared_ride_3+,Walk-transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_walk,Walk-transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,walk_transit_ASC_walk,,,,,,,,,,,,,, +util_Walk-transit_tour_mode_ASC_ride_hail,Walk-transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,walk_transit_ASC_taxi,walk_transit_ASC_tnc_single,walk_transit_ASC_tnc_shared +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_pnr_transit),,,,,,,,,,,,,,,,pnr_ASC_taxi,pnr_ASC_tnc_single,pnr_ASC_tnc_shared +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_knr_transit),,,,,,,,,,,,,,,,knr_ASC_taxi,knr_ASC_tnc_single,knr_ASC_tnc_shared +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- shared 2,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,ride_hail_ASC_sr2,,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- shared 3+,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,ride_hail_ASC_walk,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- walk-transit,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,ride_hail_ASC_taxi,,ride_hail_ASC_tnc_shared +util_Shared_Ride_2_tour_mode_ASC_walk,joint - sr2 tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,,,joint_sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3+_tour_mode_ASC_walk,joint - sr3p tour mode ASC,@(df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,joint_sr3p_ASC_sr2,,joint_sr3p_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_shared_ride_2,joint - Walk-transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_shared_ride_3+,joint - Walk-transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Walk-transit_tour_mode_ASC_walk,joint - Walk-transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,joint_walk_transit_ASC_taxi,joint_walk_transit_ASC_tnc_single,joint_walk_transit_ASC_tnc_shared +util_joint_PNR_to_Transit_tour_mode_ASC_ride_hail,joint - PNR to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_pnr_transit),,,,,,,,,,,,,,,,joint_pnr_ASC_taxi,joint_pnr_ASC_tnc_single,joint_pnr_ASC_tnc_shared +util_joint_KNR_to_Transit_tour_mode_ASC_ride_hail,joint - KNR to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_knr_transit),,,,,,,,,,,,,,,,joint_knr_ASC_taxi,joint_knr_ASC_tnc_single,joint_knr_ASC_tnc_shared +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- shared 2,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- shared 3+,@ (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail,joint - Ride Hail tour mode ASC -- taxi,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,,joint_ride_hail_ASC_tnc_shared +util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,coef_unavailable,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,coef_unavailable,,,,,,,,,,,,, +#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,, +util_School_Bus_Unavailable_if_primary_purpose_NOT_school,School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_Unavailable_Tour_Mode_=_SOV,School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_Unavailable_Tour_Mode_=_Transit,School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,coef_unavailable,,, +util_School_Bus_In-vehicle_time_(20_miles_per_hour),School Bus - In-vehicle time (20 miles per hour),@odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_Walk_Time,School Bus - Walk Time,@coef_wacc_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_Wait_Time,School Bus - Wait Time,@coef_short_iwait_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +util_School_Bus_tour_mode_ASC_shared_ride_2,School Bus tour mode ASC -- shared ride 2,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,schoolbus_ASC_sr2,,,,,,,,,,,,,,,, +util_School_Bus_tour_mode_ASC_shared_ride_3+,School Bus tour mode ASC -- shared ride 3+,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,schoolbus_ASC_sr3p,,,,,,,,,,,,,,, +util_School_Bus_tour_mode_ASC_walk,School Bus tour mode ASC -- walk,@df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,schoolbus_ASC_walk,,,,,,,,,,,,,, +util_Origin_density_index,Origin density index,@(origin_density_applied*df.origin_density_index).clip(origin_density_index_max) if origin_density_applied else 0,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,coef_ivt,coef_ivt +util_TNC_shared_adjustment,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,coef_ivt +util_umt_unavailable_wlk,UMT unavailable for non affiliates WALK,@((odt_skims['WLK_LOC_IVTT_UMT'] + dot_skims['WLK_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,coef_unavailable,,,,,,,,,,,, +util_umt_unavailable_pnr,UMT unavailable for non affiliates PNR,@((odt_skims['PNR_LOC_IVTT_UMT'] + dot_skims['PNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,,,,coef_unavailable,,,,,,,,, +util_umt_unavailable_knr,UMT unavailable for non affiliates KNR,@((odt_skims['KNR_LOC_IVTT_UMT'] + dot_skims['KNRE_LOC_IVTT_UMT'])>0) & (df.umich_affiliate_trip==False),,,,,,,,,,,,coef_unavailable,,,,,, diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..cdf1a56b5 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,230 @@ +Description,Target,Expression +,is_joint,(df.number_of_participants > 1) +,is_indiv,(df.number_of_participants == 1) +,is_atwork_subtour,~df.parent_tour_id.isnull() +,is_school,"(df.primary_purpose=='school') & (df.purpose.isin(['school', 'Home']))" +,c_cost,(0.60 * coef_ivt) / df.value_of_time +,ivot,1.0/ df.value_of_time +#,, +#atwork subtours,, +#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,, +,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')" +,work_tour_is_SOV,parent_tour_mode.isin(['DRIVEALONE']) +,work_tour_is_bike,parent_tour_mode=='BIKE' +#,, +,i_tour_mode,df.tour_mode.map(I_MODE_MAP) +,tour_mode_is_SOV,"i_tour_mode.isin(I_SOV_MODES) & (df.get('parked_at_university', default = False)==False)" +,tour_mode_is_sr2,"i_tour_mode.isin(I_SR2_MODES) & (df.get('parked_at_university', default = False)==False)" +,tour_mode_is_sr3p,"i_tour_mode.isin(I_SR3P_MODES) & (df.get('parked_at_university', default = False)==False)" +,tour_mode_is_walk,i_tour_mode.isin([I_WALK_MODE]) +,tour_mode_is_bike,i_tour_mode.isin([I_BIKE_MODE]) +,tour_mode_is_walk_transit,"i_tour_mode.isin(I_WALK_TRANSIT_MODES) | (df.get('parked_at_university', default = False)==True)" +#,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES) +,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES) +,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES) +,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES) +,tour_mode_not_drive_transit,~tour_mode_is_drive_transit +,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES) +,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES) +,tour_mode_is_school_bus,i_tour_mode.isin(I_SCHOOLBUS_MODE) +,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES) +#,, +,inbound,~df.outbound +,first_trip,df.trip_num == 1 +,last_trip,df.trip_num == df.trip_count +origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))" +dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))" +,total_terminal_time,_origin_terminal_time + _dest_terminal_time +#,, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work +,tour_duration,"reindex(tours.duration, df.tour_id)" +,_dest_daily_parking_cost,"reindex(land_use.parking_daily, df[dest_col_name])" +,_dest_hourly_parking_cost_temp,"reindex(land_use.parking_hourly, df[dest_col_name])" +,_dest_hourly_parking_cost,"np.where(_dest_hourly_parking_cost_temp.isna(), 999, _dest_hourly_parking_cost_temp)" +,tour_parking_cost,"np.where(df.tour_type == 'work', ~free_parking_available * np.minimum(_dest_hourly_parking_cost * tour_duration/2*100, _dest_daily_parking_cost*100), np.where(_dest_hourly_parking_cost!=999, _dest_hourly_parking_cost * tour_duration/2 * 100,0))" +trip parking cost is tour's divided by two,total_parking_cost,tour_parking_cost/2 +#,, +#,dest_hourly_parking_cost,"reindex(land_use.parking_hourly, df[DESTINATION])" +#,origin_hourly_parking_cost,"reindex(land_use.parking_hourly, df[ORIGIN])" +"#np.where(first_trip, np.where(inbound,df.duration/2 * ~free_parking_available,0), 1)",origin_duration,"np.where(first_trip, np.where(inbound,df.duration/2,0), 1)" +"#np.where(last_trip, np.where(inbound, df.duration/2 * ~free_parking_available, 0), 1)",dest_duration,"np.where(last_trip, np.where(inbound, df.duration/2, 0), 1)" +#,origin_parking_cost,origin_duration*origin_hourly_parking_cost +#,dest_parking_cost,dest_duration*dest_hourly_parking_cost +#,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0 +,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))" +,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))" +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination & (tour_mode_not_drive_transit) +,pnr_transit_available,tour_mode_is_pnr_transit +,knr_transit_available,tour_mode_is_knr_transit +,origin_walk_time,shortWalk*60/walkSpeed +,destination_walk_time,shortWalk*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +#,, +,sov_available,(odt_skims['SOV_TIME']>0) & tour_mode_not_drive_transit +,hov2_available,(odt_skims['HOV2_TIME']>0) & tour_mode_not_drive_transit +,hov3_available,(odt_skims['HOV3_TIME']>0) & tour_mode_not_drive_transit +#,od_dist_walk,od_skims['DISTWALK'] +#,do_dist_walk,od_skims.reverse('DISTWALK') +#,max_dist_walk,od_skims.max('DISTWALK') +#,dist_bike,od_skims['DISTBIKE'] +#,dist_only,od_skims['DIST'] +,umich_affiliate_trip,"reindex(persons.umich_worker, df.person_id) | reindex(persons.umich_student, df.person_id)" +#,, +,origin,df.origin if 'origin' in df.columns else df.home_zone_id +,destination,df.destination if 'destination' in df.columns else df.alt_dest +#access egress distances,, +,origin_local_dist,"reindex(land_use.AE_LOCAL, origin)" +,origin_prm_dist,"reindex(land_use.AE_PRM, origin)" +,origin_mix_dist,"np.minimum(origin_local_dist, origin_prm_dist)" +,dest_local_dist,"reindex(land_use.AE_LOCAL, destination)" +,dest_prm_dist,"reindex(land_use.AE_PRM, destination)" +,dest_mix_dist,"np.minimum(dest_local_dist, dest_prm_dist)" +#transit availability,, +,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0) & (origin_local_dist < max_local_walk_dist) & (dest_local_dist < max_local_walk_dist) +,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0) & (origin_prm_dist < max_prm_walk_dist) & (dest_prm_dist < max_prm_walk_dist) +,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0) & (origin_mix_dist < max_mix_walk_dist) & (dest_mix_dist < max_mix_walk_dist) +,pnr_local_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_LOC_IVT']>0) & (dest_local_dist < max_local_walk_dist) +,pnr_local_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_LOC_IVT']>0) & (origin_local_dist < max_local_walk_dist) +,pnr_premium_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_PRM_IVT']>0) & (dest_prm_dist < max_prm_walk_dist) +,pnr_premium_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_PRM_IVT']>0) & (origin_prm_dist < max_prm_walk_dist) +,pnr_mix_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_MIX_IVT']>0) & (dest_mix_dist < max_mix_walk_dist) +,pnr_mix_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_MIX_IVT']>0) & (origin_mix_dist < max_mix_walk_dist) +,knr_local_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_LOC_IVT']>0) & (dest_local_dist < max_local_walk_dist) +,knr_local_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_LOC_IVT']>0) & (origin_local_dist < max_local_walk_dist) +,knr_premium_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_PRM_IVT']>0) & (dest_prm_dist < max_prm_walk_dist) +,knr_premium_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_PRM_IVT']>0) & (origin_prm_dist < max_prm_walk_dist) +,knr_mix_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_MIX_IVT']>0) & (dest_mix_dist < max_mix_walk_dist) +,knr_mix_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_MIX_IVT']>0) & (origin_mix_dist < max_mix_walk_dist) +#access egress times,, +,origin_local_time,origin_local_dist * 60/walk_speed +,origin_prm_time,origin_prm_dist * 60/walk_speed +,origin_mix_time,origin_mix_dist * 60/walk_speed +,dest_local_time,dest_local_dist * 60/walk_speed +,dest_prm_time,dest_prm_dist * 60/walk_speed +,dest_mix_time,dest_mix_dist * 60/walk_speed +#,transit fare discount calculations, +,ptype,df.ptype +,transit_pass_ownership,df.transit_pass_ownership +,transit_pass_subsidy,df.transit_pass_subsidy +cash discounts for seniors and students and preKs,transit_cash_discount_factor,"np.where((df.age>64) | (ptype>5), cash_discount_factor, 1)" +100% subsidy if own subsidy,transit_subsidy_factor,"np.where(transit_pass_subsidy, 1, 0)" +,pass_discount_factor_ddot,"ptype.map({k: v for k, v in transit_pass_discount_factor_ddot.items()})" +,pass_discount_factor_smart,"ptype.map({k: v for k, v in transit_pass_discount_factor_smart.items()})" +,pass_discount_factor_aata,"ptype.map({k: v for k, v in transit_pass_discount_factor_aata.items()})" +,pass_discount_factor_um,"ptype.map({k: v for k, v in transit_pass_discount_factor_um.items()})" +,pass_discount_factor_other,"ptype.map({k: v for k, v in transit_pass_discount_factor_other.items()})" +#,pass discount differs by ptype and agency, +,WLKLOC_pass_discount_DDOT,"np.where((odt_skims['WLK_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNRLOC_pass_discount_DDOT,"np.where((odt_skims['PNR_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNRELOC_pass_discount_DDOT,"np.where((odt_skims['PNRE_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNRLOC_pass_discount_DDOT,"np.where((odt_skims['KNR_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNRELOC_pass_discount_DDOT,"np.where((odt_skims['KNRE_LOC_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,WLKLOC_pass_discount_SMART,"np.where((odt_skims['WLK_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNRLOC_pass_discount_SMART,"np.where((odt_skims['PNR_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNRELOC_pass_discount_SMART,"np.where((odt_skims['PNRE_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNRLOC_pass_discount_SMART,"np.where((odt_skims['KNR_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNRELOC_pass_discount_SMART,"np.where((odt_skims['KNRE_LOC_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,WLKLOC_pass_discount_AATA,"np.where((odt_skims['WLK_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNRLOC_pass_discount_AATA,"np.where((odt_skims['PNR_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNRELOC_pass_discount_AATA,"np.where((odt_skims['PNRE_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNRLOC_pass_discount_AATA,"np.where((odt_skims['KNR_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNRELOC_pass_discount_AATA,"np.where((odt_skims['KNRE_LOC_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,WLKLOC_pass_discount_UM,"np.where((odt_skims['WLK_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNRLOC_pass_discount_UM,"np.where((odt_skims['PNR_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNRELOC_pass_discount_UM,"np.where((odt_skims['PNRE_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNRLOC_pass_discount_UM,"np.where((odt_skims['KNR_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNRELOC_pass_discount_UM,"np.where((odt_skims['KNRE_LOC_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,WLKLOC_pass_discount_OTHER,"np.where((odt_skims['WLK_LOC_IVTT_BWAT']>0) | (odt_skims['WLK_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,PNRLOC_pass_discount_OTHER,"np.where((odt_skims['PNR_LOC_IVTT_BWAT']>0) | (odt_skims['PNR_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,PNRELOC_pass_discount_OTHER,"np.where((odt_skims['PNRE_LOC_IVTT_BWAT']>0) | (odt_skims['PNRE_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,KNRLOC_pass_discount_OTHER,"np.where((odt_skims['KNR_LOC_IVTT_BWAT']>0) | (odt_skims['KNR_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,KNRELOC_pass_discount_OTHER,"np.where((odt_skims['KNRE_LOC_IVTT_BWAT']>0) | (odt_skims['KNRE_LOC_IVTT_LET']>0), pass_discount_factor_other, 1)" +,WLKPRM_pass_discount_OTHER,"np.where((odt_skims['WLK_PRM_IVTT_PMov']>0) | (odt_skims['WLK_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,PNRPRM_pass_discount_OTHER,"np.where((odt_skims['PNR_PRM_IVTT_PMov']>0) | (odt_skims['PNR_PRM_IVTT_LET']>0), pass_discount_factor_other, 1)" +,PNREPRM_pass_discount_OTHER,"np.where((odt_skims['PNRE_PRM_IVTT_PMov']>0) | (odt_skims['PNRE_PRM_IVTT_LET']>0), pass_discount_factor_other, 1)" +,KNRPRM_pass_discount_OTHER,"np.where((odt_skims['KNR_PRM_IVTT_PMov']>0) | (odt_skims['KNR_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,KNREPRM_pass_discount_OTHER,"np.where((odt_skims['KNRE_PRM_IVTT_PMov']>0) | (odt_skims['KNRE_PRM_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,WLKMIX_pass_discount_DDOT,"np.where((odt_skims['WLK_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNRMIX_pass_discount_DDOT,"np.where((odt_skims['PNR_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,PNREMIX_pass_discount_DDOT,"np.where((odt_skims['PNRE_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNRMIX_pass_discount_DDOT,"np.where((odt_skims['KNR_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,KNREMIX_pass_discount_DDOT,"np.where((odt_skims['KNRE_MIX_IVTT_DDOT']>0), pass_discount_factor_ddot, 1)" +,WLKMIX_pass_discount_SMART,"np.where((odt_skims['WLK_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNRMIX_pass_discount_SMART,"np.where((odt_skims['PNR_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,PNREMIX_pass_discount_SMART,"np.where((odt_skims['PNRE_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNRMIX_pass_discount_SMART,"np.where((odt_skims['KNR_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,KNREMIX_pass_discount_SMART,"np.where((odt_skims['KNRE_MIX_IVTT_SMART']>0), pass_discount_factor_smart, 1)" +,WLKMIX_pass_discount_AATA,"np.where((odt_skims['WLK_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNRMIX_pass_discount_AATA,"np.where((odt_skims['PNR_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,PNREMIX_pass_discount_AATA,"np.where((odt_skims['PNRE_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNRMIX_pass_discount_AATA,"np.where((odt_skims['KNR_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,KNREMIX_pass_discount_AATA,"np.where((odt_skims['KNRE_MIX_IVTT_AAATA']>0), pass_discount_factor_aata, 1)" +,WLKMIX_pass_discount_UM,"np.where((odt_skims['WLK_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNRMIX_pass_discount_UM,"np.where((odt_skims['PNR_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,PNREMIX_pass_discount_UM,"np.where((odt_skims['PNRE_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNRMIX_pass_discount_UM,"np.where((odt_skims['KNR_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,KNREMIX_pass_discount_UM,"np.where((odt_skims['KNRE_MIX_IVTT_UMT']>0), pass_discount_factor_um, 1)" +,WLKMIX_pass_discount_OTHER,"np.where((odt_skims['WLK_MIX_IVTT_BWAT']>0) | (odt_skims['WLK_MIX_IVTT_LET']>0) | (odt_skims['WLK_MIX_IVTT_PMov']>0) | (odt_skims['WLK_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,PNRMIX_pass_discount_OTHER,"np.where((odt_skims['PNR_MIX_IVTT_BWAT']>0) | (odt_skims['PNR_MIX_IVTT_LET']>0) | (odt_skims['WLK_MIX_IVTT_PMov']>0) | (odt_skims['WLK_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,PNREMIX_pass_discount_OTHER,"np.where((odt_skims['PNRE_MIX_IVTT_BWAT']>0) | (odt_skims['PNRE_MIX_IVTT_LET']>0) | (odt_skims['WLK_MIX_IVTT_PMov']>0) | (odt_skims['WLK_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,KNRMIX_pass_discount_OTHER,"np.where((odt_skims['KNR_MIX_IVTT_BWAT']>0) | (odt_skims['KNR_MIX_IVTT_LET']>0) | (odt_skims['WLK_MIX_IVTT_PMov']>0) | (odt_skims['WLK_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +,KNREMIX_pass_discount_OTHER,"np.where((odt_skims['KNRE_MIX_IVTT_BWAT']>0) | (odt_skims['KNRE_MIX_IVTT_LET']>0) | (odt_skims['WLK_MIX_IVTT_PMov']>0) | (odt_skims['WLK_MIX_IVTT_StCar']>0), pass_discount_factor_other, 1)" +#,Use the defined hierarchy to pick the pass discount for each transit alternative, +,WLKLOC_pass_discount,"np.where(WLKLOC_pass_discount_DDOT<1, WLKLOC_pass_discount_DDOT, np.where(WLKLOC_pass_discount_SMART<1, WLKLOC_pass_discount_SMART, np.where(WLKLOC_pass_discount_AATA<1, WLKLOC_pass_discount_AATA, WLKLOC_pass_discount_OTHER)))" +,WLKPRM_pass_discount,WLKPRM_pass_discount_OTHER +,WLKMIX_pass_discount,"np.where(WLKMIX_pass_discount_DDOT<1, WLKMIX_pass_discount_DDOT, np.where(WLKMIX_pass_discount_SMART<1, WLKMIX_pass_discount_SMART, np.where(WLKMIX_pass_discount_AATA<1, WLKMIX_pass_discount_AATA, WLKMIX_pass_discount_OTHER)))" +,PNRLOC_pass_discount,"np.where(PNRLOC_pass_discount_DDOT<1, PNRLOC_pass_discount_DDOT, np.where(PNRLOC_pass_discount_SMART<1, PNRLOC_pass_discount_SMART, np.where(PNRLOC_pass_discount_AATA<1, PNRLOC_pass_discount_AATA, PNRLOC_pass_discount_OTHER)))" +,PNRPRM_pass_discount,PNRPRM_pass_discount_OTHER +,PNRMIX_pass_discount,"np.where(PNRMIX_pass_discount_DDOT<1, PNRMIX_pass_discount_DDOT, np.where(PNRMIX_pass_discount_SMART<1, PNRMIX_pass_discount_SMART, np.where(PNRMIX_pass_discount_AATA<1, PNRMIX_pass_discount_AATA, PNRMIX_pass_discount_OTHER)))" +,PNRELOC_pass_discount,"np.where(PNRELOC_pass_discount_DDOT<1, PNRELOC_pass_discount_DDOT, np.where(PNRELOC_pass_discount_SMART<1, PNRELOC_pass_discount_SMART, np.where(PNRELOC_pass_discount_AATA<1, PNRELOC_pass_discount_AATA, PNRELOC_pass_discount_OTHER)))" +,PNREPRM_pass_discount,PNREPRM_pass_discount_OTHER +,PNREMIX_pass_discount,"np.where(PNREMIX_pass_discount_DDOT<1, PNREMIX_pass_discount_DDOT, np.where(PNREMIX_pass_discount_SMART<1, PNREMIX_pass_discount_SMART, np.where(PNREMIX_pass_discount_AATA<1, PNREMIX_pass_discount_AATA, PNREMIX_pass_discount_OTHER)))" +,KNRLOC_pass_discount,"np.where(KNRLOC_pass_discount_DDOT<1, KNRLOC_pass_discount_DDOT, np.where(KNRLOC_pass_discount_SMART<1, KNRLOC_pass_discount_SMART, np.where(KNRLOC_pass_discount_AATA<1, KNRLOC_pass_discount_AATA, KNRLOC_pass_discount_OTHER)))" +,KNRPRM_pass_discount,KNRPRM_pass_discount_OTHER +,KNRMIX_pass_discount,"np.where(KNRMIX_pass_discount_DDOT<1, KNRMIX_pass_discount_DDOT, np.where(KNRMIX_pass_discount_SMART<1, KNRMIX_pass_discount_SMART, np.where(KNRMIX_pass_discount_AATA<1, KNRMIX_pass_discount_AATA, KNRMIX_pass_discount_OTHER)))" +,KNRELOC_pass_discount,"np.where(KNRELOC_pass_discount_DDOT<1, KNRELOC_pass_discount_DDOT, np.where(KNRELOC_pass_discount_SMART<1, KNRELOC_pass_discount_SMART, np.where(KNRELOC_pass_discount_AATA<1, KNRELOC_pass_discount_AATA, KNRELOC_pass_discount_OTHER)))" +,KNREPRM_pass_discount,KNREPRM_pass_discount_OTHER +,KNREMIX_pass_discount,"np.where(KNREMIX_pass_discount_DDOT<1, KNREMIX_pass_discount_DDOT, np.where(KNREMIX_pass_discount_SMART<1, KNREMIX_pass_discount_SMART, np.where(KNREMIX_pass_discount_AATA<1, KNREMIX_pass_discount_AATA, KNREMIX_pass_discount_OTHER)))" +#,"Now compute the fare discounts using cash, subsidy, and pass discounts", +,WLKLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,WLKPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,WLKMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), WLKMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), WLKMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNRELOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNRELOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNRELOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNREPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNREPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNREPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,PNREMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), PNREMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), PNREMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRLOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRLOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRLOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNRELOC_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNRELOC_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNRELOC_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNREPRM_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNREPRM_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNREPRM_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +,KNREMIX_fare_discount,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), transit_cash_discount_factor, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), KNREMIX_pass_discount, np.where((transit_pass_ownership == True) & (transit_pass_subsidy == True), KNREMIX_pass_discount * (1-transit_subsidy_factor), np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), transit_cash_discount_factor * (1-transit_subsidy_factor), 1))))" +#,, +,PMOV_transit,"np.where(((odt_skims['WLK_PRM_IVTT_PMov']>0)) | ((odt_skims['PNR_PRM_IVTT_PMov']>0)) | ((odt_skims['KNR_PRM_IVTT_PMov']>0)) & ((odt_skims['WLK_PRM_NT']==0)&(odt_skims['PNR_PRM_NT']==0)&(odt_skims['KNR_PRM_NT']==0)&(odt_skims['PNRE_PRM_NT']==0)&(odt_skims['KNRE_PRM_NT']==0)),1,0)" +,PMOV_transit_wTransfer,"np.where(((odt_skims['WLK_PRM_IVTT_PMov']>0)) | ((odt_skims['PNR_PRM_IVTT_PMov']>0)) | ((odt_skims['KNR_PRM_IVTT_PMov']>0)) & ((odt_skims['WLK_PRM_NT']>0)|(odt_skims['PNR_PRM_NT']>0)|(odt_skims['KNR_PRM_NT']>0)|(odt_skims['PNRE_PRM_NT']>0)|(odt_skims['KNRE_PRM_NT']>0)),1,0)" +,StCar_transit,"np.where((((odt_skims['WLK_PRM_IVTT_StCar']>0)) | ((odt_skims['PNR_PRM_IVTT_StCar']>0)) | ((odt_skims['KNR_PRM_IVTT_StCar']>0))) & ((odt_skims['WLK_PRM_NT']==0)&(odt_skims['PNR_PRM_NT']==0)&(odt_skims['KNR_PRM_NT']==0)&(odt_skims['PNRE_PRM_NT']==0)&(odt_skims['KNRE_PRM_NT']==0)),1,0)" +,StCar_transit_wTransfer,"np.where((((odt_skims['WLK_PRM_IVTT_StCar']>0)) | ((odt_skims['PNR_PRM_IVTT_StCar']>0)) | ((odt_skims['KNR_PRM_IVTT_StCar']>0))) & ((odt_skims['WLK_PRM_NT']>0)|(odt_skims['PNR_PRM_NT']>0)|(odt_skims['KNR_PRM_NT']>0)|(odt_skims['PNRE_PRM_NT']>0)|(odt_skims['KNRE_PRM_NT']>0)),1,0)" +,AATA_transit,"np.where(((odt_skims['WLK_LOC_IVTT_AAATA']>0)) | ((odt_skims['PNR_LOC_IVTT_AAATA']>0)) | ((odt_skims['KNR_LOC_IVTT_AAATA']>0)),1,0)" +#,, +,orig_district,"reindex(land_use.DISTRICT20, df[orig_col_name])" +,dest_district,"reindex(land_use.DISTRICT20, df[dest_col_name])" +,districts1_2_cte,"np.where((orig_district.isin([1,2])) & (dest_district.isin([1,2])), -0.2,0)" +,districts8_13_cte,"np.where((orig_district.isin([8,9,10,13])) & (dest_district.isin([8,9,10,13])), -2,0)" +,districts15_cte,"np.where((orig_district==15) & (dest_district==15), 2,0)" diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients.csv new file mode 100644 index 000000000..ae9acaa7e --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients.csv @@ -0,0 +1,447 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_one,1,T +coef_nest_root,1,T +coef_nest_AUTO,0.72,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_WALKACCESS,0.5,T +coef_nest_PNRACCESS,0.5,T +coef_nest_KNRACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_othmaint_social,-0.02,F +coef_ivt_work,-0.02,F +coef_ivt_univ_school,-0.03,F +coef_ivt_escort_shopping_eatout_othdiscr_atwork,-0.03,F +coef_age1619_da,0,F +coef_age010_trn,0,F +coef_age16p_sr,0,F +coef_hhsize1_sr,-0.73,F +coef_hhsize2_sr,0,F +coef_walktimeshort_atwork,1.35,F +coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_biketimeshort_atwork,2.7,F +coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,4,T +coef_dtim_atwork,1.35,F +coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_ivt_pmov_multiplier,1,T +coef_ivt_stcar_multiplier,0.9,T +coef_ivt_brt_multiplier,0.9,T +coef_ivt_urb_multiplier,0.8,T +coef_ivt_com_multiplier,0.7,T +coef_long_iwait_atwork,0.67,F +coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,1,T +coef_short_iwait_atwork,1.35,F +coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_wacc_atwork,1.35,F +coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_waux_atwork,1.35,F +coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_wegr_atwork,1.35,F +coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_xwait_atwork,1.35,F +coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2,T +coef_bike_ASC_walk_atwork,0,F +coef_bike_ASC_walk_eatout,0,F +coef_bike_ASC_walk_escort,0,F +coef_bike_ASC_walk_othdiscr,0,F +coef_bike_ASC_walk_othmaint,0,F +coef_bike_ASC_walk_school,0,F +coef_bike_ASC_walk_shopping,0,F +coef_bike_ASC_walk_social,0,F +coef_bike_ASC_walk_univ,0,F +coef_bike_ASC_walk_work,0,F +coef_joint_sr2_ASC_walk,-2.471792438,F +coef_joint_sr3p_ASC_sr2,9.839224393,F +coef_joint_sr3p_ASC_walk,-1.071636758,F +coef_joint_walk_transit_ASC_sr2,-0.852339913,F +coef_joint_walk_transit_ASC_sr3p,1.144241245,F +coef_joint_walk_transit_ASC_walk,6.075155959,F +coef_joint_walk_transit_ASC_taxi,-6,F +coef_joint_walk_transit_ASC_tnc_single,-6,F +coef_joint_walk_transit_ASC_tnc_shared,-6,F +coef_joint_pnr_ASC_taxi,-6,F +coef_joint_pnr_ASC_tnc_single,-6,F +coef_joint_pnr_ASC_tnc_shared,-6,F +coef_joint_knr_ASC_taxi,-2,F +coef_joint_knr_ASC_tnc_single,-2,F +coef_joint_knr_ASC_tnc_shared,-2,F +coef_schoolbus_ASC_sr2_atwork,0,F +coef_schoolbus_ASC_sr2_eatout,0,F +coef_schoolbus_ASC_sr2_escort,0,F +coef_schoolbus_ASC_sr2_othdiscr,0,F +coef_schoolbus_ASC_sr2_othmaint,0,F +coef_schoolbus_ASC_sr2_school,-10.06373283,F +coef_schoolbus_ASC_sr2_shopping,0,F +coef_schoolbus_ASC_sr2_social,0,F +coef_schoolbus_ASC_sr2_univ,0,F +coef_schoolbus_ASC_sr2_work,0,F +coef_schoolbus_ASC_sr3p_atwork,0,F +coef_schoolbus_ASC_sr3p_eatout,0,F +coef_schoolbus_ASC_sr3p_escort,0,F +coef_schoolbus_ASC_sr3p_othdiscr,0,F +coef_schoolbus_ASC_sr3p_othmaint,0,F +coef_schoolbus_ASC_sr3p_school,-9.988929352,F +coef_schoolbus_ASC_sr3p_shopping,0,F +coef_schoolbus_ASC_sr3p_social,0,F +coef_schoolbus_ASC_sr3p_univ,0,F +coef_schoolbus_ASC_sr3p_work,0,F +coef_schoolbus_ASC_walk_atwork,0,F +coef_schoolbus_ASC_walk_eatout,0,F +coef_schoolbus_ASC_walk_escort,0,F +coef_schoolbus_ASC_walk_othdiscr,0,F +coef_schoolbus_ASC_walk_othmaint,0,F +coef_schoolbus_ASC_walk_school,-9.259293862,F +coef_schoolbus_ASC_walk_shopping,0,F +coef_schoolbus_ASC_walk_social,0,F +coef_schoolbus_ASC_walk_univ,0,F +coef_schoolbus_ASC_walk_work,0,F +coef_sov_ASC_walk_atwork,-2.46867203,F +coef_sov_ASC_walk_eatout,-0.938373445,F +coef_sov_ASC_walk_escort,-0.297751799,F +coef_sov_ASC_walk_othdiscr,-1.148373445,F +coef_sov_ASC_walk_othmaint,-1.597751799,F +coef_sov_ASC_walk_school,-9,F +coef_sov_ASC_walk_shopping,-1.767751799,F +coef_sov_ASC_walk_social,-1.398373445,F +coef_sov_ASC_walk_univ,-1.011502455,F +coef_sov_ASC_walk_work,-1.802520239,F +coef_sov_ASC_taxi_atwork,-7,F +coef_sov_ASC_taxi_eatout,0,F +coef_sov_ASC_taxi_escort,0,F +coef_sov_ASC_taxi_othdiscr,0,F +coef_sov_ASC_taxi_othmaint,0,F +coef_sov_ASC_taxi_school,-5.65,F +coef_sov_ASC_taxi_shopping,0,F +coef_sov_ASC_taxi_social,0,F +coef_sov_ASC_taxi_univ,-6.65,F +coef_sov_ASC_taxi_work,-7,F +coef_sov_ASC_tnc_single_atwork,-7,F +coef_sov_ASC_tnc_single_eatout,0,F +coef_sov_ASC_tnc_single_escort,0,F +coef_sov_ASC_tnc_single_othdiscr,0,F +coef_sov_ASC_tnc_single_othmaint,0,F +coef_sov_ASC_tnc_single_school,-5.65,F +coef_sov_ASC_tnc_single_shopping,0,F +coef_sov_ASC_tnc_single_social,0,F +coef_sov_ASC_tnc_single_univ,-6.65,F +coef_sov_ASC_tnc_single_work,-7,F +coef_sov_ASC_tnc_shared_atwork,-7,F +coef_sov_ASC_tnc_shared_eatout,0,F +coef_sov_ASC_tnc_shared_escort,0,F +coef_sov_ASC_tnc_shared_othdiscr,0,F +coef_sov_ASC_tnc_shared_othmaint,0,F +coef_sov_ASC_tnc_shared_school,-5.65,F +coef_sov_ASC_tnc_shared_shopping,0,F +coef_sov_ASC_tnc_shared_social,0,F +coef_sov_ASC_tnc_shared_univ,-6.65,F +coef_sov_ASC_tnc_shared_work,-7,F +coef_sr2_ASC_sov_atwork,-1.620744866,F +coef_sr2_ASC_sov_eatout,-0.791850445,F +coef_sr2_ASC_sov_escort,-0.5220431,F +coef_sr2_ASC_sov_othdiscr,-0.791850445,F +coef_sr2_ASC_sov_othmaint,-0.5220431,F +coef_sr2_ASC_sov_school,0.072153131,F +coef_sr2_ASC_sov_shopping,-0.5220431,F +coef_sr2_ASC_sov_social,-0.791850445,F +coef_sr2_ASC_sov_univ,-1.031443997,F +coef_sr2_ASC_sov_work,-0.508671077,F +coef_sr2_ASC_walk_atwork,-3.825642366,F +coef_sr2_ASC_walk_eatout,-0.433293836,F +coef_sr2_ASC_walk_escort,-3.403290336,F +coef_sr2_ASC_walk_othdiscr,-0.383293836,F +coef_sr2_ASC_walk_othmaint,-1.943290336,F +coef_sr2_ASC_walk_school,-0.354193147,F +coef_sr2_ASC_walk_shopping,-1.463290336,F +coef_sr2_ASC_walk_social,-1.153293836,F +coef_sr2_ASC_walk_univ,-0.380972958,F +coef_sr2_ASC_walk_work,-0.736462014,F +coef_sr2_ASC_taxi_atwork,0,F +coef_sr2_ASC_taxi_eatout,0,F +coef_sr2_ASC_taxi_escort,0,F +coef_sr2_ASC_taxi_othdiscr,0,F +coef_sr2_ASC_taxi_othmaint,0,F +coef_sr2_ASC_taxi_school,0,F +coef_sr2_ASC_taxi_shopping,0,F +coef_sr2_ASC_taxi_social,0,F +coef_sr2_ASC_taxi_univ,0,F +coef_sr2_ASC_taxi_work,0,F +coef_sr2_ASC_tnc_single_atwork,0,F +coef_sr2_ASC_tnc_single_eatout,0,F +coef_sr2_ASC_tnc_single_escort,0,F +coef_sr2_ASC_tnc_single_othdiscr,0,F +coef_sr2_ASC_tnc_single_othmaint,0,F +coef_sr2_ASC_tnc_single_school,0,F +coef_sr2_ASC_tnc_single_shopping,0,F +coef_sr2_ASC_tnc_single_social,0,F +coef_sr2_ASC_tnc_single_univ,0,F +coef_sr2_ASC_tnc_single_work,0,F +coef_sr2_ASC_tnc_shared_atwork,0,F +coef_sr2_ASC_tnc_shared_eatout,0,F +coef_sr2_ASC_tnc_shared_escort,0,F +coef_sr2_ASC_tnc_shared_othdiscr,0,F +coef_sr2_ASC_tnc_shared_othmaint,0,F +coef_sr2_ASC_tnc_shared_school,0,F +coef_sr2_ASC_tnc_shared_shopping,0,F +coef_sr2_ASC_tnc_shared_social,0,F +coef_sr2_ASC_tnc_shared_univ,0,F +coef_sr2_ASC_tnc_shared_work,0,F +coef_sr3p_ASC_sov_atwork,-1.744080912,F +coef_sr3p_ASC_sov_eatout,-1.468882814,F +coef_sr3p_ASC_sov_escort,-1.10860006,F +coef_sr3p_ASC_sov_othdiscr,-1.468882814,F +coef_sr3p_ASC_sov_othmaint,-1.10860006,F +coef_sr3p_ASC_sov_school,-1.825714468,F +coef_sr3p_ASC_sov_shopping,-1.10860006,F +coef_sr3p_ASC_sov_social,-1.468882814,F +coef_sr3p_ASC_sov_univ,-1.142813652,F +coef_sr3p_ASC_sov_work,-0.256157751,F +coef_sr3p_ASC_sr2_atwork,-0.64725158,F +coef_sr3p_ASC_sr2_eatout,-0.724454156,F +coef_sr3p_ASC_sr2_escort,-0.65216434,F +coef_sr3p_ASC_sr2_othdiscr,-1.014454156,F +coef_sr3p_ASC_sr2_othmaint,-0.67216434,F +coef_sr3p_ASC_sr2_school,-1.093790496,F +coef_sr3p_ASC_sr2_shopping,-0.46216434,F +coef_sr3p_ASC_sr2_social,-0.564454156,F +coef_sr3p_ASC_sr2_univ,-0.811403038,F +coef_sr3p_ASC_sr2_work,-0.343730774,F +coef_sr3p_ASC_walk_atwork,-3.158756084,F +coef_sr3p_ASC_walk_eatout,0.051857124,F +coef_sr3p_ASC_walk_escort,-2.142113481,F +coef_sr3p_ASC_walk_othdiscr,-0.428142876,F +coef_sr3p_ASC_walk_othmaint,-1.092113481,F +coef_sr3p_ASC_walk_school,-0.828569173,F +coef_sr3p_ASC_walk_shopping,-1.642113481,F +coef_sr3p_ASC_walk_social,-0.218142876,F +coef_sr3p_ASC_walk_univ,-0.631705608,F +coef_sr3p_ASC_walk_work,-1.048894984,F +coef_sr3p_ASC_taxi_atwork,0,F +coef_sr3p_ASC_taxi_eatout,0,F +coef_sr3p_ASC_taxi_escort,0,F +coef_sr3p_ASC_taxi_othdiscr,0,F +coef_sr3p_ASC_taxi_othmaint,0,F +coef_sr3p_ASC_taxi_school,0,F +coef_sr3p_ASC_taxi_shopping,0,F +coef_sr3p_ASC_taxi_social,0,F +coef_sr3p_ASC_taxi_univ,0,F +coef_sr3p_ASC_taxi_work,0,F +coef_sr3p_ASC_tnc_single_atwork,0,F +coef_sr3p_ASC_tnc_single_eatout,0,F +coef_sr3p_ASC_tnc_single_escort,0,F +coef_sr3p_ASC_tnc_single_othdiscr,0,F +coef_sr3p_ASC_tnc_single_othmaint,0,F +coef_sr3p_ASC_tnc_single_school,0,F +coef_sr3p_ASC_tnc_single_shopping,0,F +coef_sr3p_ASC_tnc_single_social,0,F +coef_sr3p_ASC_tnc_single_univ,0,F +coef_sr3p_ASC_tnc_single_work,0,F +coef_sr3p_ASC_tnc_shared_atwork,0,F +coef_sr3p_ASC_tnc_shared_eatout,0,F +coef_sr3p_ASC_tnc_shared_escort,0,F +coef_sr3p_ASC_tnc_shared_othdiscr,0,F +coef_sr3p_ASC_tnc_shared_othmaint,0,F +coef_sr3p_ASC_tnc_shared_school,0,F +coef_sr3p_ASC_tnc_shared_shopping,0,F +coef_sr3p_ASC_tnc_shared_social,0,F +coef_sr3p_ASC_tnc_shared_univ,0,F +coef_sr3p_ASC_tnc_shared_work,0,F +coef_walk_transit_ASC_sr2_atwork,-52,F +coef_walk_transit_ASC_sr2_eatout,-3.277280992,F +coef_walk_transit_ASC_sr2_escort,-2.816907177,F +coef_walk_transit_ASC_sr2_othdiscr,-3.277280992,F +coef_walk_transit_ASC_sr2_othmaint,-2.816907177,F +coef_walk_transit_ASC_sr2_school,-1.067460714,F +coef_walk_transit_ASC_sr2_shopping,-2.816907177,F +coef_walk_transit_ASC_sr2_social,-3.277280992,F +coef_walk_transit_ASC_sr2_univ,-1.692048933,F +coef_walk_transit_ASC_sr2_work,-2.263503048,F +coef_walk_transit_ASC_sr3p_atwork,6.5,F +coef_walk_transit_ASC_sr3p_eatout,-3.71975504,F +coef_walk_transit_ASC_sr3p_escort,-4.011357479,F +coef_walk_transit_ASC_sr3p_othdiscr,-3.71975504,F +coef_walk_transit_ASC_sr3p_othmaint,-4.011357479,F +coef_walk_transit_ASC_sr3p_school,-2.036881943,F +coef_walk_transit_ASC_sr3p_shopping,-4.011357479,F +coef_walk_transit_ASC_sr3p_social,-3.71975504,F +coef_walk_transit_ASC_sr3p_univ,-3.155976407,F +coef_walk_transit_ASC_sr3p_work,-2.311033195,F +coef_walk_transit_ASC_walk_atwork,10,F +coef_walk_transit_ASC_walk_eatout,5.487490973,F +coef_walk_transit_ASC_walk_escort,4.450183717,F +coef_walk_transit_ASC_walk_othdiscr,6.5,F +coef_walk_transit_ASC_walk_othmaint,5.5,F +coef_walk_transit_ASC_walk_school,1.206168225,F +coef_walk_transit_ASC_walk_shopping,4.250183717,F +coef_walk_transit_ASC_walk_social,5.856148939,F +coef_walk_transit_ASC_walk_univ,3,F +coef_walk_transit_ASC_walk_work,3.8,F +coef_walk_transit_ASC_taxi_atwork,-36,F +coef_walk_transit_ASC_taxi_eatout,-6,F +coef_walk_transit_ASC_taxi_escort,-8,F +coef_walk_transit_ASC_taxi_othdiscr,-6,F +coef_walk_transit_ASC_taxi_othmaint,-8,F +coef_walk_transit_ASC_taxi_school,-6,F +coef_walk_transit_ASC_taxi_shopping,-8,F +coef_walk_transit_ASC_taxi_social,-6,F +coef_walk_transit_ASC_taxi_univ,-8,F +coef_walk_transit_ASC_taxi_work,-6,F +coef_walk_transit_ASC_tnc_single_atwork,-35.55708184,F +coef_walk_transit_ASC_tnc_single_eatout,-3.539109366,F +coef_walk_transit_ASC_tnc_single_escort,-3.728652264,F +coef_walk_transit_ASC_tnc_single_othdiscr,-3.539109366,F +coef_walk_transit_ASC_tnc_single_othmaint,-3.728652264,F +coef_walk_transit_ASC_tnc_single_school,-6,F +coef_walk_transit_ASC_tnc_single_shopping,-3.728652264,F +coef_walk_transit_ASC_tnc_single_social,-3.539109366,F +coef_walk_transit_ASC_tnc_single_univ,-4.77105815,F +coef_walk_transit_ASC_tnc_single_work,-1.89748304,F +coef_walk_transit_ASC_tnc_shared_atwork,-36,F +coef_walk_transit_ASC_tnc_shared_eatout,-6,F +coef_walk_transit_ASC_tnc_shared_escort,-3.308554217,F +coef_walk_transit_ASC_tnc_shared_othdiscr,-6,F +coef_walk_transit_ASC_tnc_shared_othmaint,-3.308554217,F +coef_walk_transit_ASC_tnc_shared_school,-6,F +coef_walk_transit_ASC_tnc_shared_shopping,-3.308554217,F +coef_walk_transit_ASC_tnc_shared_social,-6,F +coef_walk_transit_ASC_tnc_shared_univ,-8,F +coef_walk_transit_ASC_tnc_shared_work,-1.242981701,F +coef_pnr_ASC_taxi_atwork,0,F +coef_pnr_ASC_taxi_eatout,-4,F +coef_pnr_ASC_taxi_escort,-4,F +coef_pnr_ASC_taxi_othdiscr,-4,F +coef_pnr_ASC_taxi_othmaint,-4,F +coef_pnr_ASC_taxi_school,-4,F +coef_pnr_ASC_taxi_shopping,-4,F +coef_pnr_ASC_taxi_social,-4,F +coef_pnr_ASC_taxi_univ,-4,F +coef_pnr_ASC_taxi_work,-38,F +coef_pnr_ASC_tnc_single_atwork,0,F +coef_pnr_ASC_tnc_single_eatout,-6,F +coef_pnr_ASC_tnc_single_escort,-4,F +coef_pnr_ASC_tnc_single_othdiscr,-6,F +coef_pnr_ASC_tnc_single_othmaint,-4,F +coef_pnr_ASC_tnc_single_school,-12,F +coef_pnr_ASC_tnc_single_shopping,-4,F +coef_pnr_ASC_tnc_single_social,-6,F +coef_pnr_ASC_tnc_single_univ,-0.8,F +coef_pnr_ASC_tnc_single_work,-38,F +coef_pnr_ASC_tnc_shared_atwork,0,F +coef_pnr_ASC_tnc_shared_eatout,-6,F +coef_pnr_ASC_tnc_shared_escort,-1.903945917,F +coef_pnr_ASC_tnc_shared_othdiscr,-6,F +coef_pnr_ASC_tnc_shared_othmaint,-1.903945917,F +coef_pnr_ASC_tnc_shared_school,-14,F +coef_pnr_ASC_tnc_shared_shopping,-1.903945917,F +coef_pnr_ASC_tnc_shared_social,-6,F +coef_pnr_ASC_tnc_shared_univ,0.1,F +coef_pnr_ASC_tnc_shared_work,-40,F +coef_knr_ASC_taxi_atwork,-4,F +coef_knr_ASC_taxi_eatout,-2,F +coef_knr_ASC_taxi_escort,-4,F +coef_knr_ASC_taxi_othdiscr,-2,F +coef_knr_ASC_taxi_othmaint,-4,F +coef_knr_ASC_taxi_school,-2,F +coef_knr_ASC_taxi_shopping,-4,F +coef_knr_ASC_taxi_social,-2,F +coef_knr_ASC_taxi_univ,-4,F +coef_knr_ASC_taxi_work,-6,F +coef_knr_ASC_tnc_single_atwork,-6,F +coef_knr_ASC_tnc_single_eatout,-0.685991857,F +coef_knr_ASC_tnc_single_escort,-4,F +coef_knr_ASC_tnc_single_othdiscr,-0.685991857,F +coef_knr_ASC_tnc_single_othmaint,-4,F +coef_knr_ASC_tnc_single_school,-4,F +coef_knr_ASC_tnc_single_shopping,-4,F +coef_knr_ASC_tnc_single_social,-0.685991857,F +coef_knr_ASC_tnc_single_univ,-4,F +coef_knr_ASC_tnc_single_work,-6,F +coef_knr_ASC_tnc_shared_atwork,-6,F +coef_knr_ASC_tnc_shared_eatout,-2,F +coef_knr_ASC_tnc_shared_escort,-6,F +coef_knr_ASC_tnc_shared_othdiscr,-2,F +coef_knr_ASC_tnc_shared_othmaint,-6,F +coef_knr_ASC_tnc_shared_school,-4,F +coef_knr_ASC_tnc_shared_shopping,-6,F +coef_knr_ASC_tnc_shared_social,-2,F +coef_knr_ASC_tnc_shared_univ,-4,F +coef_knr_ASC_tnc_shared_work,-4,F +coef_ride_hail_ASC_sr2_atwork,-5.087151438,F +coef_ride_hail_ASC_sr2_eatout,-3.008963528,F +coef_ride_hail_ASC_sr2_escort,-6.158806545,F +coef_ride_hail_ASC_sr2_othdiscr,-3.008963528,F +coef_ride_hail_ASC_sr2_othmaint,-6.158806545,F +coef_ride_hail_ASC_sr2_school,-2,F +coef_ride_hail_ASC_sr2_shopping,-6.158806545,F +coef_ride_hail_ASC_sr2_social,-3.008963528,F +coef_ride_hail_ASC_sr2_univ,-8,F +coef_ride_hail_ASC_sr2_work,-2.501911301,F +coef_ride_hail_ASC_sr3p_atwork,-3.170206298,F +coef_ride_hail_ASC_sr3p_eatout,-2.44111868,F +coef_ride_hail_ASC_sr3p_escort,-8.08074379,F +coef_ride_hail_ASC_sr3p_othdiscr,-2.44111868,F +coef_ride_hail_ASC_sr3p_othmaint,-8.08074379,F +coef_ride_hail_ASC_sr3p_school,-2,F +coef_ride_hail_ASC_sr3p_shopping,-8.08074379,F +coef_ride_hail_ASC_sr3p_social,-2.44111868,F +coef_ride_hail_ASC_sr3p_univ,-8,F +coef_ride_hail_ASC_sr3p_work,-4.179706499,F +coef_ride_hail_ASC_walk_atwork,-2.651229293,F +coef_ride_hail_ASC_walk_eatout,1.713164329,F +coef_ride_hail_ASC_walk_escort,-1.500683557,F +coef_ride_hail_ASC_walk_othdiscr,1.713164329,F +coef_ride_hail_ASC_walk_othmaint,-1.500683557,F +coef_ride_hail_ASC_walk_school,0,F +coef_ride_hail_ASC_walk_shopping,-1.500683557,F +coef_ride_hail_ASC_walk_social,1.713164329,F +coef_ride_hail_ASC_walk_univ,1.951866303,F +coef_ride_hail_ASC_walk_work,1.870011335,F +coef_ride_hail_ASC_walk_transit_atwork,0,F +coef_ride_hail_ASC_walk_transit_eatout,0,F +coef_ride_hail_ASC_walk_transit_escort,0,F +coef_ride_hail_ASC_walk_transit_othdiscr,0,F +coef_ride_hail_ASC_walk_transit_othmaint,0,F +coef_ride_hail_ASC_walk_transit_school,0,F +coef_ride_hail_ASC_walk_transit_shopping,0,F +coef_ride_hail_ASC_walk_transit_social,0,F +coef_ride_hail_ASC_walk_transit_univ,0,F +coef_ride_hail_ASC_walk_transit_work,0,F +coef_ride_hail_ASC_taxi_atwork,-1.966237689,F +coef_ride_hail_ASC_taxi_eatout,-1.523627128,F +coef_ride_hail_ASC_taxi_escort,-2.351382979,F +coef_ride_hail_ASC_taxi_othdiscr,-1.523627128,F +coef_ride_hail_ASC_taxi_othmaint,-2.351382979,F +coef_ride_hail_ASC_taxi_school,-2,F +coef_ride_hail_ASC_taxi_shopping,-2.351382979,F +coef_ride_hail_ASC_taxi_social,-1.523627128,F +coef_ride_hail_ASC_taxi_univ,-2,F +coef_ride_hail_ASC_taxi_work,-1.058587162,F +coef_ride_hail_ASC_tnc_shared_atwork,-0.311353966,F +coef_ride_hail_ASC_tnc_shared_eatout,0.914610974,F +coef_ride_hail_ASC_tnc_shared_escort,-0.578043138,F +coef_ride_hail_ASC_tnc_shared_othdiscr,0.914610974,F +coef_ride_hail_ASC_tnc_shared_othmaint,-0.578043138,F +coef_ride_hail_ASC_tnc_shared_school,0,F +coef_ride_hail_ASC_tnc_shared_shopping,-0.578043138,F +coef_ride_hail_ASC_tnc_shared_social,0.914610974,F +coef_ride_hail_ASC_tnc_shared_univ,0.411988198,F +coef_ride_hail_ASC_tnc_shared_work,-0.709248249,F +coef_joint_ride_hail_ASC_sr2,9.986518294,F +coef_joint_ride_hail_ASC_sr3p,-2.332036095,F +coef_joint_ride_hail_ASC_walk,3.000998785,F +coef_joint_ride_hail_ASC_taxi,-4,F +coef_joint_ride_hail_ASC_tnc_shared,-2.05508206,F +walk_express_penalty,10,T +adjust_tnc_shared,30,T +coef_origin_density_applied_work_univ_school,0,T +coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,1,T +coef_transit_district15_univ,1,T +coef_transit_district15_work,0,T +coef_transit_district15_school,0,T +coef_transit_district15_escort,0,T +coef_transit_district15_shopping,0,T +coef_transit_district15_eatout,0,T +coef_transit_district15_othmaint,0,T +coef_transit_district15_social,0,T +coef_transit_district15_othdiscr,0,T +coef_transit_district15_atwork,0,T diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_.csv new file mode 100644 index 000000000..2220b02a8 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_.csv @@ -0,0 +1,437 @@ +coefficient_name,value,constrain +coef_unavailable,-999.0,T +coef_one,1.0,T +coef_nest_root,1.0,T +coef_nest_AUTO,0.72,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_WALKACCESS,0.5,T +coef_nest_PNRACCESS,0.5,T +coef_nest_KNRACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_othmaint_social,-0.02,F +coef_ivt_work,-0.02,F +coef_ivt_univ_school,-0.03,F +coef_ivt_escort_shopping_eatout_othdiscr_atwork,-0.03,F +coef_age1619_da,0.0,F +coef_age010_trn,0.0,F +coef_age16p_sr,0.0,F +coef_hhsize1_sr,-0.73,F +coef_hhsize2_sr,0.0,F +coef_walktimeshort_atwork,1.35,F +coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_biketimeshort_atwork,2.7,F +coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,4.0,T +coef_dtim_atwork,1.35,F +coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_ivt_pmov_multiplier,1.0,T +coef_ivt_stcar_multiplier,0.9,T +coef_ivt_brt_multiplier,0.9,T +coef_ivt_urb_multiplier,0.8,T +coef_ivt_com_multiplier,0.7,T +coef_long_iwait_atwork,0.67,F +coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,1.0,T +coef_short_iwait_atwork,1.35,F +coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_wacc_atwork,1.35,F +coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_waux_atwork,1.35,F +coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_wegr_atwork,1.35,F +coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_xwait_atwork,1.35,F +coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,2.0,T +coef_bike_ASC_walk_atwork,0.0,F +coef_bike_ASC_walk_eatout,0.0,F +coef_bike_ASC_walk_escort,0.0,F +coef_bike_ASC_walk_othdiscr,0.0,F +coef_bike_ASC_walk_othmaint,0.0,F +coef_bike_ASC_walk_school,0.0,F +coef_bike_ASC_walk_shopping,0.0,F +coef_bike_ASC_walk_social,0.0,F +coef_bike_ASC_walk_univ,0.0,F +coef_bike_ASC_walk_work,0.0,F +coef_joint_sr2_ASC_walk,-2.4718066098180653,F +coef_joint_sr3p_ASC_sr2,6.071516809140375,F +coef_joint_sr3p_ASC_walk,-1.146278445,F +coef_joint_walk_transit_ASC_sr2,-0.4809089948207574,F +coef_joint_walk_transit_ASC_sr3p,1.5970226263331693,F +coef_joint_walk_transit_ASC_walk,6.680994689701683,F +coef_joint_walk_transit_ASC_taxi,-6.0,F +coef_joint_walk_transit_ASC_tnc_single,-6.0,F +coef_joint_walk_transit_ASC_tnc_shared,-6.0,F +coef_joint_pnr_ASC_taxi,-6.0,F +coef_joint_pnr_ASC_tnc_single,-6.0,F +coef_joint_pnr_ASC_tnc_shared,-6.0,F +coef_joint_knr_ASC_taxi,-2.0,F +coef_joint_knr_ASC_tnc_single,-2.0,F +coef_joint_knr_ASC_tnc_shared,-2.0,F +coef_schoolbus_ASC_sr2_atwork,0.0,F +coef_schoolbus_ASC_sr2_eatout,0.0,F +coef_schoolbus_ASC_sr2_escort,0.0,F +coef_schoolbus_ASC_sr2_othdiscr,0.0,F +coef_schoolbus_ASC_sr2_othmaint,0.0,F +coef_schoolbus_ASC_sr2_school,-8.870848980688523,F +coef_schoolbus_ASC_sr2_shopping,0.0,F +coef_schoolbus_ASC_sr2_social,0.0,F +coef_schoolbus_ASC_sr2_univ,0.0,F +coef_schoolbus_ASC_sr2_work,0.0,F +coef_schoolbus_ASC_sr3p_atwork,0.0,F +coef_schoolbus_ASC_sr3p_eatout,0.0,F +coef_schoolbus_ASC_sr3p_escort,0.0,F +coef_schoolbus_ASC_sr3p_othdiscr,0.0,F +coef_schoolbus_ASC_sr3p_othmaint,0.0,F +coef_schoolbus_ASC_sr3p_school,-8.793453560431455,F +coef_schoolbus_ASC_sr3p_shopping,0.0,F +coef_schoolbus_ASC_sr3p_social,0.0,F +coef_schoolbus_ASC_sr3p_univ,0.0,F +coef_schoolbus_ASC_sr3p_work,0.0,F +coef_schoolbus_ASC_walk_atwork,0.0,F +coef_schoolbus_ASC_walk_eatout,0.0,F +coef_schoolbus_ASC_walk_escort,0.0,F +coef_schoolbus_ASC_walk_othdiscr,0.0,F +coef_schoolbus_ASC_walk_othmaint,0.0,F +coef_schoolbus_ASC_walk_school,-7.98773432802811,F +coef_schoolbus_ASC_walk_shopping,0.0,F +coef_schoolbus_ASC_walk_social,0.0,F +coef_schoolbus_ASC_walk_univ,0.0,F +coef_schoolbus_ASC_walk_work,0.0,F +coef_sov_ASC_walk_atwork,-2.4561938411017716,F +coef_sov_ASC_walk_eatout,-0.943603919,F +coef_sov_ASC_walk_escort,-0.307249092,F +coef_sov_ASC_walk_othdiscr,-1.153603919,F 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+coef_ride_hail_ASC_walk_othmaint,-1.805366437835127,F +coef_ride_hail_ASC_walk_school,0.0,F +coef_ride_hail_ASC_walk_shopping,-1.805366437835127,F +coef_ride_hail_ASC_walk_social,2.043672436692793,F +coef_ride_hail_ASC_walk_univ,-0.42711895617043627,F +coef_ride_hail_ASC_walk_work,1.9169188200900251,F +coef_ride_hail_ASC_walk_transit_atwork,0.0,F +coef_ride_hail_ASC_walk_transit_eatout,0.0,F +coef_ride_hail_ASC_walk_transit_escort,0.0,F +coef_ride_hail_ASC_walk_transit_othdiscr,0.0,F +coef_ride_hail_ASC_walk_transit_othmaint,0.0,F +coef_ride_hail_ASC_walk_transit_school,0.0,F +coef_ride_hail_ASC_walk_transit_shopping,0.0,F +coef_ride_hail_ASC_walk_transit_social,0.0,F +coef_ride_hail_ASC_walk_transit_univ,0.0,F +coef_ride_hail_ASC_walk_transit_work,0.0,F +coef_ride_hail_ASC_taxi_atwork,-2.822046100103776,F +coef_ride_hail_ASC_taxi_eatout,-0.52939679648215,F +coef_ride_hail_ASC_taxi_escort,-2.440694838009208,F +coef_ride_hail_ASC_taxi_othdiscr,-0.52939679648215,F +coef_ride_hail_ASC_taxi_othmaint,-2.440694838009208,F +coef_ride_hail_ASC_taxi_school,-2.0,F +coef_ride_hail_ASC_taxi_shopping,-2.440694838009208,F +coef_ride_hail_ASC_taxi_social,-0.52939679648215,F +coef_ride_hail_ASC_taxi_univ,-2.0,F +coef_ride_hail_ASC_taxi_work,-1.5897539437496788,F +coef_ride_hail_ASC_tnc_shared_atwork,-0.273876953345801,F +coef_ride_hail_ASC_tnc_shared_eatout,0.5581894414791493,F +coef_ride_hail_ASC_tnc_shared_escort,-0.8150333821508897,F +coef_ride_hail_ASC_tnc_shared_othdiscr,0.5581894414791493,F +coef_ride_hail_ASC_tnc_shared_othmaint,-0.8150333821508897,F +coef_ride_hail_ASC_tnc_shared_school,0.0,F +coef_ride_hail_ASC_tnc_shared_shopping,-0.8150333821508897,F +coef_ride_hail_ASC_tnc_shared_social,0.5581894414791493,F +coef_ride_hail_ASC_tnc_shared_univ,0.5873113718080742,F +coef_ride_hail_ASC_tnc_shared_work,-0.8398801540853604,F +coef_joint_ride_hail_ASC_sr2,8.9718118272908,F +coef_joint_ride_hail_ASC_sr3p,-2.357988990538013,F +coef_joint_ride_hail_ASC_walk,3.3078622620547504,F +coef_joint_ride_hail_ASC_taxi,-4.0,F +coef_joint_ride_hail_ASC_tnc_shared,-1.615763246732392,F +walk_express_penalty,10.0,T +adjust_tnc_shared,30.0,T +coef_origin_density_applied_work_univ_school,0.0,T +coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,1.0,T diff --git a/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_template.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_template.csv new file mode 100644 index 000000000..04576537d --- /dev/null +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_template.csv @@ -0,0 +1,99 @@ +coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +#same for all segments,,,,,,,,,, +coef_unavailable,,,,,,,,,, +coef_one,,,,,,,,,, +coef_nest_root,,,,,,,,,, +coef_nest_AUTO,,,,,,,,,, +coef_nest_NONMOTORIZED,,,,,,,,,, +coef_nest_TRANSIT,,,,,,,,,, +coef_nest_WALKACCESS,,,,,,,,,, +coef_nest_PNRACCESS,,,,,,,,,, +coef_nest_KNRACCESS,,,,,,,,,, +coef_nest_RIDEHAIL,,,,,,,,,, +#,,,,,,,,,, +coef_ivt,coef_ivt_work,coef_ivt_univ_school,coef_ivt_univ_school,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_othmaint_social,coef_ivt_othmaint_social,coef_ivt_escort_shopping_eatout_othdiscr_atwork,coef_ivt_escort_shopping_eatout_othdiscr_atwork +coef_age1619_da,,,,,,,,,, +coef_age010_trn,,,,,,,,,, +coef_age16p_sr,,,,,,,,,, +coef_hhsize1_sr,,,,,,,,,, +coef_hhsize2_sr,,,,,,,,,, +#multipliers that differ by purpose and were apparently estimated only for atwork,,,,,,,,,, +coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier,coef_ivt_pmov_multiplier +coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier,coef_ivt_stcar_multiplier +coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier,coef_ivt_brt_multiplier +coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier,coef_ivt_urb_multiplier +coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier,coef_ivt_com_multiplier +coef_short_iwait_multiplier,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_short_iwait_atwork +coef_long_iwait_multiplier,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_long_iwait_atwork +coef_wacc_multiplier,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wacc_atwork +coef_wegr_multiplier,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_wegr_atwork +coef_waux_multiplier,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_waux_atwork +coef_dtim_multiplier,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_dtim_atwork +coef_xwait_multiplier,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_xwait_atwork +coef_walktimeshort_multiplier,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_walktimeshort_atwork +coef_biketimeshort_multiplier,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_work_univ_school_escort_shopping_eatout_othmaint_social_othdiscr,coef_biketimeshort_atwork +#ASCs,,,,,,,,,, +sov_ASC_walk,coef_sov_ASC_walk_work,coef_sov_ASC_walk_univ,coef_sov_ASC_walk_school,coef_sov_ASC_walk_escort,coef_sov_ASC_walk_shopping,coef_sov_ASC_walk_eatout,coef_sov_ASC_walk_othmaint,coef_sov_ASC_walk_social,coef_sov_ASC_walk_othdiscr,coef_sov_ASC_walk_atwork +sov_ASC_taxi,coef_sov_ASC_taxi_work,coef_sov_ASC_taxi_univ,coef_sov_ASC_taxi_school,coef_sov_ASC_taxi_escort,coef_sov_ASC_taxi_shopping,coef_sov_ASC_taxi_eatout,coef_sov_ASC_taxi_othmaint,coef_sov_ASC_taxi_social,coef_sov_ASC_taxi_othdiscr,coef_sov_ASC_taxi_atwork +sov_ASC_tnc_single,coef_sov_ASC_tnc_single_work,coef_sov_ASC_tnc_single_univ,coef_sov_ASC_tnc_single_school,coef_sov_ASC_tnc_single_escort,coef_sov_ASC_tnc_single_shopping,coef_sov_ASC_tnc_single_eatout,coef_sov_ASC_tnc_single_othmaint,coef_sov_ASC_tnc_single_social,coef_sov_ASC_tnc_single_othdiscr,coef_sov_ASC_tnc_single_atwork +sov_ASC_tnc_shared,coef_sov_ASC_tnc_shared_work,coef_sov_ASC_tnc_shared_univ,coef_sov_ASC_tnc_shared_school,coef_sov_ASC_tnc_shared_escort,coef_sov_ASC_tnc_shared_shopping,coef_sov_ASC_tnc_shared_eatout,coef_sov_ASC_tnc_shared_othmaint,coef_sov_ASC_tnc_shared_social,coef_sov_ASC_tnc_shared_othdiscr,coef_sov_ASC_tnc_shared_atwork +sr2_ASC_sov,coef_sr2_ASC_sov_work,coef_sr2_ASC_sov_univ,coef_sr2_ASC_sov_school,coef_sr2_ASC_sov_escort,coef_sr2_ASC_sov_shopping,coef_sr2_ASC_sov_eatout,coef_sr2_ASC_sov_othmaint,coef_sr2_ASC_sov_social,coef_sr2_ASC_sov_othdiscr,coef_sr2_ASC_sov_atwork +sr2_ASC_walk,coef_sr2_ASC_walk_work,coef_sr2_ASC_walk_univ,coef_sr2_ASC_walk_school,coef_sr2_ASC_walk_escort,coef_sr2_ASC_walk_shopping,coef_sr2_ASC_walk_eatout,coef_sr2_ASC_walk_othmaint,coef_sr2_ASC_walk_social,coef_sr2_ASC_walk_othdiscr,coef_sr2_ASC_walk_atwork +sr2_ASC_taxi,coef_sr2_ASC_taxi_work,coef_sr2_ASC_taxi_univ,coef_sr2_ASC_taxi_school,coef_sr2_ASC_taxi_escort,coef_sr2_ASC_taxi_shopping,coef_sr2_ASC_taxi_eatout,coef_sr2_ASC_taxi_othmaint,coef_sr2_ASC_taxi_social,coef_sr2_ASC_taxi_othdiscr,coef_sr2_ASC_taxi_atwork +sr2_ASC_tnc_single,coef_sr2_ASC_tnc_single_work,coef_sr2_ASC_tnc_single_univ,coef_sr2_ASC_tnc_single_school,coef_sr2_ASC_tnc_single_escort,coef_sr2_ASC_tnc_single_shopping,coef_sr2_ASC_tnc_single_eatout,coef_sr2_ASC_tnc_single_othmaint,coef_sr2_ASC_tnc_single_social,coef_sr2_ASC_tnc_single_othdiscr,coef_sr2_ASC_tnc_single_atwork +sr2_ASC_tnc_shared,coef_sr2_ASC_tnc_shared_work,coef_sr2_ASC_tnc_shared_univ,coef_sr2_ASC_tnc_shared_school,coef_sr2_ASC_tnc_shared_escort,coef_sr2_ASC_tnc_shared_shopping,coef_sr2_ASC_tnc_shared_eatout,coef_sr2_ASC_tnc_shared_othmaint,coef_sr2_ASC_tnc_shared_social,coef_sr2_ASC_tnc_shared_othdiscr,coef_sov_ASC_tnc_shared_atwork +sr3p_ASC_sov,coef_sr3p_ASC_sov_work,coef_sr3p_ASC_sov_univ,coef_sr3p_ASC_sov_school,coef_sr3p_ASC_sov_escort,coef_sr3p_ASC_sov_shopping,coef_sr3p_ASC_sov_eatout,coef_sr3p_ASC_sov_othmaint,coef_sr3p_ASC_sov_social,coef_sr3p_ASC_sov_othdiscr,coef_sr3p_ASC_sov_atwork +sr3p_ASC_sr2,coef_sr3p_ASC_sr2_work,coef_sr3p_ASC_sr2_univ,coef_sr3p_ASC_sr2_school,coef_sr3p_ASC_sr2_escort,coef_sr3p_ASC_sr2_shopping,coef_sr3p_ASC_sr2_eatout,coef_sr3p_ASC_sr2_othmaint,coef_sr3p_ASC_sr2_social,coef_sr3p_ASC_sr2_othdiscr,coef_sr3p_ASC_sr2_atwork +sr3p_ASC_walk,coef_sr3p_ASC_walk_work,coef_sr3p_ASC_walk_univ,coef_sr3p_ASC_walk_school,coef_sr3p_ASC_walk_escort,coef_sr3p_ASC_walk_shopping,coef_sr3p_ASC_walk_eatout,coef_sr3p_ASC_walk_othmaint,coef_sr3p_ASC_walk_social,coef_sr3p_ASC_walk_othdiscr,coef_sr3p_ASC_walk_atwork +sr3p_ASC_taxi,coef_sr3p_ASC_taxi_work,coef_sr3p_ASC_taxi_univ,coef_sr3p_ASC_taxi_school,coef_sr3p_ASC_taxi_escort,coef_sr3p_ASC_taxi_shopping,coef_sr3p_ASC_taxi_eatout,coef_sr3p_ASC_taxi_othmaint,coef_sr3p_ASC_taxi_social,coef_sr3p_ASC_taxi_othdiscr,coef_sr3p_ASC_taxi_atwork +sr3p_ASC_tnc_single,coef_sr3p_ASC_tnc_single_work,coef_sr3p_ASC_tnc_single_univ,coef_sr3p_ASC_tnc_single_school,coef_sr3p_ASC_tnc_single_escort,coef_sr3p_ASC_tnc_single_shopping,coef_sr3p_ASC_tnc_single_eatout,coef_sr3p_ASC_tnc_single_othmaint,coef_sr3p_ASC_tnc_single_social,coef_sr3p_ASC_tnc_single_othdiscr,coef_sr3p_ASC_tnc_single_atwork +sr3p_ASC_tnc_shared,coef_sr3p_ASC_tnc_shared_work,coef_sr3p_ASC_tnc_shared_univ,coef_sr3p_ASC_tnc_shared_school,coef_sr3p_ASC_tnc_shared_escort,coef_sr3p_ASC_tnc_shared_shopping,coef_sr3p_ASC_tnc_shared_eatout,coef_sr3p_ASC_tnc_shared_othmaint,coef_sr3p_ASC_tnc_shared_social,coef_sr3p_ASC_tnc_shared_othdiscr,coef_sov_ASC_tnc_shared_atwork +#walk_ASC_rh,coef_walk_ASC_rh_work,coef_walk_ASC_rh_univ,coef_walk_ASC_rh_school,coef_walk_ASC_rh_escort,coef_walk_ASC_rh_shopping,coef_walk_ASC_rh_eatout,coef_walk_ASC_rh_othmaint,coef_walk_ASC_rh_social,coef_walk_ASC_rh_othdiscr,coef_walk_ASC_rh_atwork +bike_ASC_walk,coef_bike_ASC_walk_work,coef_bike_ASC_walk_univ,coef_bike_ASC_walk_school,coef_bike_ASC_walk_escort,coef_bike_ASC_walk_shopping,coef_bike_ASC_walk_eatout,coef_bike_ASC_walk_othmaint,coef_bike_ASC_walk_social,coef_bike_ASC_walk_othdiscr,coef_bike_ASC_walk_atwork +#bike_ASC_rh,coef_bike_ASC_rh_work,coef_bike_ASC_rh_univ,coef_bike_ASC_rh_school,coef_bike_ASC_rh_escort,coef_bike_ASC_rh_shopping,coef_bike_ASC_rh_eatout,coef_bike_ASC_rh_othmaint,coef_bike_ASC_rh_social,coef_bike_ASC_rh_othdiscr,coef_bike_ASC_rh_atwork +walk_transit_ASC_sr2,coef_walk_transit_ASC_sr2_work,coef_walk_transit_ASC_sr2_univ,coef_walk_transit_ASC_sr2_school,coef_walk_transit_ASC_sr2_escort,coef_walk_transit_ASC_sr2_shopping,coef_walk_transit_ASC_sr2_eatout,coef_walk_transit_ASC_sr2_othmaint,coef_walk_transit_ASC_sr2_social,coef_walk_transit_ASC_sr2_othdiscr,coef_walk_transit_ASC_sr2_atwork +walk_transit_ASC_sr3p,coef_walk_transit_ASC_sr3p_work,coef_walk_transit_ASC_sr3p_univ,coef_walk_transit_ASC_sr3p_school,coef_walk_transit_ASC_sr3p_escort,coef_walk_transit_ASC_sr3p_shopping,coef_walk_transit_ASC_sr3p_eatout,coef_walk_transit_ASC_sr3p_othmaint,coef_walk_transit_ASC_sr3p_social,coef_walk_transit_ASC_sr3p_othdiscr,coef_walk_transit_ASC_sr3p_atwork +walk_transit_ASC_walk,coef_walk_transit_ASC_walk_work,coef_walk_transit_ASC_walk_univ,coef_walk_transit_ASC_walk_school,coef_walk_transit_ASC_walk_escort,coef_walk_transit_ASC_walk_shopping,coef_walk_transit_ASC_walk_eatout,coef_walk_transit_ASC_walk_othmaint,coef_walk_transit_ASC_walk_social,coef_walk_transit_ASC_walk_othdiscr,coef_walk_transit_ASC_walk_atwork +walk_transit_ASC_taxi,coef_walk_transit_ASC_taxi_work,coef_walk_transit_ASC_taxi_univ,coef_walk_transit_ASC_taxi_school,coef_walk_transit_ASC_taxi_escort,coef_walk_transit_ASC_taxi_shopping,coef_walk_transit_ASC_taxi_eatout,coef_walk_transit_ASC_taxi_othmaint,coef_walk_transit_ASC_taxi_social,coef_walk_transit_ASC_taxi_othdiscr,coef_walk_transit_ASC_taxi_atwork +walk_transit_ASC_tnc_single,coef_walk_transit_ASC_tnc_single_work,coef_walk_transit_ASC_tnc_single_univ,coef_walk_transit_ASC_tnc_single_school,coef_walk_transit_ASC_tnc_single_escort,coef_walk_transit_ASC_tnc_single_shopping,coef_walk_transit_ASC_tnc_single_eatout,coef_walk_transit_ASC_tnc_single_othmaint,coef_walk_transit_ASC_tnc_single_social,coef_walk_transit_ASC_tnc_single_othdiscr,coef_walk_transit_ASC_tnc_single_atwork +walk_transit_ASC_tnc_shared,coef_walk_transit_ASC_tnc_shared_work,coef_walk_transit_ASC_tnc_shared_univ,coef_walk_transit_ASC_tnc_shared_school,coef_walk_transit_ASC_tnc_shared_escort,coef_walk_transit_ASC_tnc_shared_shopping,coef_walk_transit_ASC_tnc_shared_eatout,coef_walk_transit_ASC_tnc_shared_othmaint,coef_walk_transit_ASC_tnc_shared_social,coef_walk_transit_ASC_tnc_shared_othdiscr,coef_walk_transit_ASC_tnc_shared_atwork +pnr_ASC_taxi,coef_pnr_ASC_taxi_work,coef_pnr_ASC_taxi_univ,coef_pnr_ASC_taxi_school,coef_pnr_ASC_taxi_escort,coef_pnr_ASC_taxi_shopping,coef_pnr_ASC_taxi_eatout,coef_pnr_ASC_taxi_othmaint,coef_pnr_ASC_taxi_social,coef_pnr_ASC_taxi_othdiscr,coef_pnr_ASC_taxi_atwork +pnr_ASC_tnc_single,coef_pnr_ASC_tnc_single_work,coef_pnr_ASC_tnc_single_univ,coef_pnr_ASC_tnc_single_school,coef_pnr_ASC_tnc_single_escort,coef_pnr_ASC_tnc_single_shopping,coef_pnr_ASC_tnc_single_eatout,coef_pnr_ASC_tnc_single_othmaint,coef_pnr_ASC_tnc_single_social,coef_pnr_ASC_tnc_single_othdiscr,coef_pnr_ASC_tnc_single_atwork +pnr_ASC_tnc_shared,coef_pnr_ASC_tnc_shared_work,coef_pnr_ASC_tnc_shared_univ,coef_pnr_ASC_tnc_shared_school,coef_pnr_ASC_tnc_shared_escort,coef_pnr_ASC_tnc_shared_shopping,coef_pnr_ASC_tnc_shared_eatout,coef_pnr_ASC_tnc_shared_othmaint,coef_pnr_ASC_tnc_shared_social,coef_pnr_ASC_tnc_shared_othdiscr,coef_pnr_ASC_tnc_shared_atwork +knr_ASC_taxi,coef_knr_ASC_taxi_work,coef_knr_ASC_taxi_univ,coef_knr_ASC_taxi_school,coef_knr_ASC_taxi_escort,coef_knr_ASC_taxi_shopping,coef_knr_ASC_taxi_eatout,coef_knr_ASC_taxi_othmaint,coef_knr_ASC_taxi_social,coef_knr_ASC_taxi_othdiscr,coef_knr_ASC_taxi_atwork +knr_ASC_tnc_single,coef_knr_ASC_tnc_single_work,coef_knr_ASC_tnc_single_univ,coef_knr_ASC_tnc_single_school,coef_knr_ASC_tnc_single_escort,coef_knr_ASC_tnc_single_shopping,coef_knr_ASC_tnc_single_eatout,coef_knr_ASC_tnc_single_othmaint,coef_knr_ASC_tnc_single_social,coef_knr_ASC_tnc_single_othdiscr,coef_knr_ASC_tnc_single_atwork +knr_ASC_tnc_shared,coef_knr_ASC_tnc_shared_work,coef_knr_ASC_tnc_shared_univ,coef_knr_ASC_tnc_shared_school,coef_knr_ASC_tnc_shared_escort,coef_knr_ASC_tnc_shared_shopping,coef_knr_ASC_tnc_shared_eatout,coef_knr_ASC_tnc_shared_othmaint,coef_knr_ASC_tnc_shared_social,coef_knr_ASC_tnc_shared_othdiscr,coef_knr_ASC_tnc_shared_atwork +schoolbus_ASC_sr2,coef_schoolbus_ASC_sr2_work,coef_schoolbus_ASC_sr2_univ,coef_schoolbus_ASC_sr2_school,coef_schoolbus_ASC_sr2_escort,coef_schoolbus_ASC_sr2_shopping,coef_schoolbus_ASC_sr2_eatout,coef_schoolbus_ASC_sr2_othmaint,coef_schoolbus_ASC_sr2_social,coef_schoolbus_ASC_sr2_othdiscr,coef_schoolbus_ASC_sr2_atwork +schoolbus_ASC_sr3p,coef_schoolbus_ASC_sr3p_work,coef_schoolbus_ASC_sr3p_univ,coef_schoolbus_ASC_sr3p_school,coef_schoolbus_ASC_sr3p_escort,coef_schoolbus_ASC_sr3p_shopping,coef_schoolbus_ASC_sr3p_eatout,coef_schoolbus_ASC_sr3p_othmaint,coef_schoolbus_ASC_sr3p_social,coef_schoolbus_ASC_sr3p_othdiscr,coef_schoolbus_ASC_sr3p_atwork +schoolbus_ASC_walk,coef_schoolbus_ASC_walk_work,coef_schoolbus_ASC_walk_univ,coef_schoolbus_ASC_walk_school,coef_schoolbus_ASC_walk_escort,coef_schoolbus_ASC_walk_shopping,coef_schoolbus_ASC_walk_eatout,coef_schoolbus_ASC_walk_othmaint,coef_schoolbus_ASC_walk_social,coef_schoolbus_ASC_walk_othdiscr,coef_schoolbus_ASC_walk_atwork +#,,,,,,,,,, +ride_hail_ASC_sr2,coef_ride_hail_ASC_sr2_work,coef_ride_hail_ASC_sr2_univ,coef_ride_hail_ASC_sr2_school,coef_ride_hail_ASC_sr2_escort,coef_ride_hail_ASC_sr2_shopping,coef_ride_hail_ASC_sr2_eatout,coef_ride_hail_ASC_sr2_othmaint,coef_ride_hail_ASC_sr2_social,coef_ride_hail_ASC_sr2_othdiscr,coef_ride_hail_ASC_sr2_atwork +ride_hail_ASC_sr3p,coef_ride_hail_ASC_sr3p_work,coef_ride_hail_ASC_sr3p_univ,coef_ride_hail_ASC_sr3p_school,coef_ride_hail_ASC_sr3p_escort,coef_ride_hail_ASC_sr3p_shopping,coef_ride_hail_ASC_sr3p_eatout,coef_ride_hail_ASC_sr3p_othmaint,coef_ride_hail_ASC_sr3p_social,coef_ride_hail_ASC_sr3p_othdiscr,coef_ride_hail_ASC_sr3p_atwork +ride_hail_ASC_walk,coef_ride_hail_ASC_walk_work,coef_ride_hail_ASC_walk_univ,coef_ride_hail_ASC_walk_school,coef_ride_hail_ASC_walk_escort,coef_ride_hail_ASC_walk_shopping,coef_ride_hail_ASC_walk_eatout,coef_ride_hail_ASC_walk_othmaint,coef_ride_hail_ASC_walk_social,coef_ride_hail_ASC_walk_othdiscr,coef_ride_hail_ASC_walk_atwork +ride_hail_ASC_walk_transit,coef_ride_hail_ASC_walk_transit_work,coef_ride_hail_ASC_walk_transit_univ,coef_ride_hail_ASC_walk_transit_school,coef_ride_hail_ASC_walk_transit_escort,coef_ride_hail_ASC_walk_transit_shopping,coef_ride_hail_ASC_walk_transit_eatout,coef_ride_hail_ASC_walk_transit_othmaint,coef_ride_hail_ASC_walk_transit_social,coef_ride_hail_ASC_walk_transit_othdiscr,coef_ride_hail_ASC_walk_transit_atwork +ride_hail_ASC_taxi,coef_ride_hail_ASC_taxi_work,coef_ride_hail_ASC_taxi_univ,coef_ride_hail_ASC_taxi_school,coef_ride_hail_ASC_taxi_escort,coef_ride_hail_ASC_taxi_shopping,coef_ride_hail_ASC_taxi_eatout,coef_ride_hail_ASC_taxi_othmaint,coef_ride_hail_ASC_taxi_social,coef_ride_hail_ASC_taxi_othdiscr,coef_ride_hail_ASC_taxi_atwork +ride_hail_ASC_tnc_shared,coef_ride_hail_ASC_tnc_shared_work,coef_ride_hail_ASC_tnc_shared_univ,coef_ride_hail_ASC_tnc_shared_school,coef_ride_hail_ASC_tnc_shared_escort,coef_ride_hail_ASC_tnc_shared_shopping,coef_ride_hail_ASC_tnc_shared_eatout,coef_ride_hail_ASC_tnc_shared_othmaint,coef_ride_hail_ASC_tnc_shared_social,coef_ride_hail_ASC_tnc_shared_othdiscr,coef_ride_hail_ASC_tnc_shared_atwork +joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2,coef_joint_sr3p_ASC_sr2 +joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk,coef_joint_sr3p_ASC_walk +joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk,coef_joint_sr2_ASC_walk +joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2,coef_joint_walk_transit_ASC_sr2 +joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p,coef_joint_walk_transit_ASC_sr3p +joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk,coef_joint_walk_transit_ASC_walk +joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi,coef_joint_walk_transit_ASC_taxi +joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single,coef_joint_walk_transit_ASC_tnc_single +joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared,coef_joint_walk_transit_ASC_tnc_shared +joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi,coef_joint_pnr_ASC_taxi +joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single,coef_joint_pnr_ASC_tnc_single +joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared,coef_joint_pnr_ASC_tnc_shared +joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi,coef_joint_knr_ASC_taxi +joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single,coef_joint_knr_ASC_tnc_single +joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared,coef_joint_knr_ASC_tnc_shared +joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2,coef_joint_ride_hail_ASC_sr2 +joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p,coef_joint_ride_hail_ASC_sr3p +joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk,coef_joint_ride_hail_ASC_walk +joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi,coef_joint_ride_hail_ASC_taxi +joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared,coef_joint_ride_hail_ASC_tnc_shared +walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty,walk_express_penalty +adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared,adjust_tnc_shared +origin_density_applied,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_work_univ_school,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork,coef_origin_density_applied_escort_shopping_eatout_othmaint_social_othdiscr_atwork +coef_transit_district15,coef_transit_district15_work,coef_transit_district15_univ,coef_transit_district15_school,coef_transit_district15_escort,coef_transit_district15_shopping,coef_transit_district15_eatout,coef_transit_district15_othmaint,coef_transit_district15_social,coef_transit_district15_othdiscr,coef_transit_district15_atwork diff --git a/activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv b/activitysim/examples/production_semcog/configs/trip_mode_choice_coeffs.csv old mode 100755 new mode 100644 similarity index 54% rename from activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv rename to activitysim/examples/production_semcog/configs/trip_mode_choice_coeffs.csv index 441bd0e4f..dc506f6ee --- a/activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv +++ b/activitysim/examples/production_semcog/configs/trip_mode_choice_coeffs.csv @@ -1,4 +1,4 @@ -coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork c_ivt,-0.022,-0.0271,-0.0271,-0.0279,-0.0279,-0.0279,-0.0175,-0.0175,-0.0279,-0.0279 c_ivt_pmov,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt c_ivt_stcar,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt @@ -35,71 +35,41 @@ c_hhsize1_sr,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0. c_hhsize2_sr,0,0,0,0,0,0,0,0,0,0 #free_parking_allowed,1,0,0,0,0,0,0,0,0,0 #indiv tour ASCs,,,,,,,,,, -sov_ASC_sr2,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 -sov_ASC_sr3p,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 -sov_ASC_walk,-1.579278297,-0.329078195,-3,-0.407754765,-1.884354765,-1.129357279,-1.707054765,-1.592757279,-1.335757279,-3.034080783 -sov_ASC_rh,-7,-6.649,-5.6549,0,0,0,0,0,0,-7 +sov_ASC_sr2,-999.0,-999.0,-999.0,0.0,-999.0,-999.0,-999.0,-999.0,-999.0,-999.0 +sov_ASC_sr3p,-999.0,-999.0,-999.0,0.0,-999.0,-999.0,-999.0,-999.0,-999.0,-999.0 +sov_ASC_walk,-1.5792782968324877,-0.3290781949740797,-3.0,-0.40775476464362187,-1.8843547646436225,-1.1293572790428554,-1.7070547646436218,-1.5927572790428557,-1.3357572790428551,-3.0340807834553853 #sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 -sr2_ASC_sov,-0.51299105,-1.058942553,-0.293588173,-0.536498222,-0.536498222,-0.849808648,-0.536498222,-0.849808648,-0.849808648,-0.601604001 -sr2_ASC_walk,-0.407385039,0.166984338,-0.173411375,-3.466936859,-1.525836859,-0.739819085,-2.013036859,-1.460119085,-0.693719085,-4.398412986 -sr2_ASC_rh,-7,-6.6897,-7,-7,-7,-7,-7,-7,-7,-7 -sr3p_ASC_sov,-0.229516694,-1.142035901,-1.860536359,-1.111545636,-1.111545636,-1.592880167,-1.111545636,-1.592880167,-1.592880167,-0.915383849 -sr3p_ASC_sr2,-0.333410245,-0.795945688,-1.095643656,-0.656228794,-0.466128794,-0.748226556,-0.683828794,-0.586026556,-1.044626556,-0.75108718 +sr2_ASC_sov,-0.512991050041268,-1.0589425529617411,-0.29358817312793983,-0.5364982216218682,-0.5364982216218682,-0.8498086476948487,-0.5364982216218682,-0.8498086476948487,-0.8498086476948487,-0.6016040006708439 +sr2_ASC_walk,-0.4073850393790288,0.16698433841804375,-0.17341137513290372,-3.4669368585615485,-1.5258368585615474,-0.7398190846487671,-2.013036858561549,-1.4601190846487668,-0.6937190846487672,-4.398412985525359 +sr3p_ASC_sov,-0.2295166944873816,-1.1420359009435297,-1.8605363586423205,-1.1115456358815738,-1.1115456358815738,-1.5928801670390054,-1.1115456358815738,-1.5928801670390054,-1.5928801670390054,-0.9153838488093408 +sr3p_ASC_sr2,-0.3334102448344269,-0.7959456883669828,-1.09564365603906,-0.6562287939398239,-0.4661287939398242,-0.7482265559118922,-0.6838287939398241,-0.5860265559118925,-1.0446265559118924,-0.7510871801661652 #sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 -sr3p_ASC_walk,-0.72844504,0.021931966,-0.70641187,-2.362025557,-1.862425557,-0.146994878,-1.312325557,-0.416994878,-0.630794878,-3.892581674 -sr3p_ASC_rh,-71.9177,-7,-6.6714,-7,-7,-7,-7,-7,-7,-6.026 -walk_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 +sr3p_ASC_walk,-0.7284450401939693,0.02193196588591275,-0.7064118703304251,-2.3620255572649405,-1.86242555726494,-0.1469948782710122,-1.31232555726494,-0.41699487827101217,-0.6307948782710123,-3.8925816735328977 bike_ASC_walk,-2.8333,-2.49405068,-3.238666604,-15.5203,-3.0245,-3.699,-2.5706,-14.2384,-2.3577,-5.0532 -bike_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 #walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 -walktransit_ASC_sr2,-3.705971533,-4.132166342,-3.186743446,-3.139673944,-3.139673944,-3.696770924,-3.139673944,-3.696770924,-3.696770924,-4 -walktransit_ASC_sr3p,-3.578423306,-5.496253927,-3.595736858,-6.198039835,-6.198039835,-3.455537128,-6.198039835,-3.455537128,-3.455537128,-1.156825048 -walktransit_ASC_walk,2.289791885,0.742780398,-0.458321333,1.976505237,1.976505237,2.544557044,1.976505237,2.544557044,2.544557044,1.038327796 -walk_transit_ASC_rh,-3.7408,-4.2691,-7,-3.1136,-3.1136,-4.7508,-3.1136,-4.7508,-4.7508,-3.8506 -drive_transit_ASC_rh,0.6674,-0.6328,-0.1272,0.687,0.687,3.5701,0.687,3.5701,3.5701,-6.952 -ride_hail_ASC_sr2,-3.9085,-2.5785,-3.0613,-3.3353,-3.3353,-3.301,-3.3353,-3.301,-3.301,-4.2636 -ride_hail_ASC_sr3p,-10.8661,-3.1888,-4.0886,-7,-7,-4.0649,-7,-4.0649,-4.0649,-7 -ride_hail_ASC_walk,1.497,-10.8471,-1.2386,-7,-7,-1.3318,-7,-1.3318,-1.3318,-1.6755 -ride_hail_ASC_walk_transit,-10.8661,-7,-7,-7,-7,-7,-7,-7,-7,-7 -drive_transit_ASC_rh,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25 -ride_hail_ASC_sr2,-6.108,-4.3372,-1.5869,-5.6483,-5.6483,-5.9692,-5.6483,-5.9692,-5.9692,-5.2763 -ride_hail_ASC_sr3p,-7,-4.922,-2.5362,-7,-7,-6.7199,-7,-6.7199,-6.7199,-7 -ride_hail_ASC_walk,0.2858,-7,0.245,-7,-7,-3.3603,-7,-3.3603,-3.3603,-2.6103 -ride_hail_ASC_walk_transit,0,0,0,0,0,0,0,0,0,0 -ride_hail_ASC_taxi,-2.385,-1.5994,-0.5869,-3.6629,-3.6629,-2.5543,-3.6629,-2.5543,-2.5543,-1.7764 -ride_hail_ASC_tnc_single,0.735,0.1081,0.3201,0.7879,0.7879,0.8274,0.7879,0.8274,0.8274,1.2143 -ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 +walktransit_ASC_sr2,-3.7059715334404735,-4.132166342200151,-3.1867434459698383,-3.1396739442261423,-3.1396739442261423,-3.6967709236305577,-3.1396739442261423,-3.6967709236305577,-3.6967709236305577,-4.0 +walktransit_ASC_sr3p,-3.578423306067476,-5.496253926895809,-3.595736857701258,-6.1980398352928665,-6.1980398352928665,-3.4555371281684337,-6.1980398352928665,-3.4555371281684337,-3.4555371281684337,-1.1568250478408622 +walktransit_ASC_walk,2.2897918854586368,0.7427803982258763,-0.4583213330652848,1.976505237003828,1.976505237003828,2.544557043538197,1.976505237003828,2.544557043538197,2.544557043538197,1.0383277958420796 #pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 #knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 #joint tour ASCs,,,,,,,,,, -joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_walk_ASC_rh,0,0,0,0,-3.0362,-3.0362,-3.0362,-3.0362,-3.0362,0 #joint_sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_sr2_ASC_walk,0,0,0,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,0 +joint_sr2_ASC_walk,0.0,0.0,0.0,-2.620466384339975,-2.620466384339975,-2.620466384339975,-2.620466384339975,-2.620466384339975,-2.620466384339975,0.0 #joint_sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_sr3p_ASC_sr2,0,0,0,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,0 -joint_sr3p_ASC_walk,0,0,0,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,0 -joint_bike_ASC_walk,0,0,0,-3,-3,-3,-3,-3,-3,0 -joint_bike_ASC_rh,0,0,0,0,-12.3057,-12.3057,-12.3057,-12.3057,-12.3057,0 +joint_sr3p_ASC_sr2,0.0,0.0,0.0,1.5763174440858891,1.5763174440858891,1.5763174440858891,1.5763174440858891,1.5763174440858891,1.5763174440858891,0.0 +joint_sr3p_ASC_walk,0.0,0.0,0.0,-1.238904557023363,-1.238904557023363,-1.238904557023363,-1.238904557023363,-1.238904557023363,-1.238904557023363,0.0 +joint_bike_ASC_walk,0.0,0.0,0.0,-3.0,-3.0,-3.0,-3.0,-3.0,-3.0,0.0 #joint_walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_walktransit_ASC_sr2,0,0,0,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,0 -joint_walktransit_ASC_sr3p,0,0,0,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,0 -joint_walktransit_ASC_walk,0,0,0,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,0 -joint_walk_transit_ASC_rh,0,0,0,0,1.2266,1.2266,1.2266,1.2266,1.2266,0 -joint_drive_transit_ASC_rh,0,0,0,0,4.6138,4.6138,4.6138,4.6138,4.6138,0 -joint_ride_hail_ASC_taxi,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_ride_hail_ASC_tnc_single,0,0,0,0,-4.7339,-4.7339,-4.7339,-4.7339,-4.7339,0 -joint_ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 +joint_walktransit_ASC_sr2,0.0,0.0,0.0,-2.3129029415955698,-2.3129029415955698,-2.3129029415955698,-2.3129029415955698,-2.3129029415955698,-2.3129029415955698,0.0 +joint_walktransit_ASC_sr3p,0.0,0.0,0.0,-3.8777685193067013,-3.8777685193067013,-3.8777685193067013,-3.8777685193067013,-3.8777685193067013,-3.8777685193067013,0.0 +joint_walktransit_ASC_walk,0.0,0.0,0.0,1.5768234235430478,1.5768234235430478,1.5768234235430478,1.5768234235430478,1.5768234235430478,1.5768234235430478,0.0 #joint_pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 #joint_knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 #,,,,,,,,,, -schoolbus_ASC_sr2,0,0,-3.289069276,0,0,0,0,0,0,0 -schoolbus_ASC_sr3p,0,0,-3.217740129,0,0,0,0,0,0,0 -schoolbus_ASC_walk,0,0,-2.277998613,0,0,0,0,0,0,0 +schoolbus_ASC_sr2,0.0,0.0,-3.2890692762180707,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +schoolbus_ASC_sr3p,0.0,0.0,-3.217740128665061,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +schoolbus_ASC_walk,0.0,0.0,-2.277998613273528,0.0,0.0,0.0,0.0,0.0,0.0,0.0 #c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt local_ASC,0,0,0,0,0,0,0,0,0,0 premium_ASC,0,0,0,0,0,0,0,0,0,0 mix_ASC,0,0,0,0,0,0,0,0,0,0 -c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt -adjust_tnc_shared,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt diff --git a/activitysim/examples/prototype_semcog/configs/trip_purpose.yaml b/activitysim/examples/production_semcog/configs/trip_purpose.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/trip_purpose.yaml rename to activitysim/examples/production_semcog/configs/trip_purpose.yaml diff --git a/activitysim/examples/prototype_semcog/configs/trip_purpose_and_destination.yaml b/activitysim/examples/production_semcog/configs/trip_purpose_and_destination.yaml old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/trip_purpose_and_destination.yaml rename to activitysim/examples/production_semcog/configs/trip_purpose_and_destination.yaml diff --git a/activitysim/examples/prototype_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/production_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv old mode 100755 new mode 100644 similarity index 100% rename from activitysim/examples/prototype_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/production_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv b/activitysim/examples/production_semcog/configs/trip_purpose_probs.csv old mode 100755 new mode 100644 similarity index 79% rename from activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv rename to activitysim/examples/production_semcog/configs/trip_purpose_probs.csv index 3cd1aaa27..ad92cf215 --- a/activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv +++ b/activitysim/examples/production_semcog/configs/trip_purpose_probs.csv @@ -1,17 +1,17 @@ primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr -work,TRUE,1,18,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066 -work,TRUE,1,18,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027 +work,TRUE,1,18,PTYPE_FULL,0.02,0.004,0,0.566,0.083,0.164,0.093,0.004,0.066 +work,TRUE,1,18,PTYPE_PART,0.024,0,0,0.657,0.076,0.14,0.067,0.009,0.027 work,TRUE,1,18,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115 -work,TRUE,19,48,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046 -work,TRUE,19,48,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057 +work,TRUE,19,48,PTYPE_FULL,0.0204,0.008,0,0.272,0.18,0.3506,0.107,0.016,0.046 +work,TRUE,19,48,PTYPE_PART,0.05,0,0,0.289,0.105,0.367,0.102,0.03,0.057 work,TRUE,19,48,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015 work,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 univ,TRUE,1,48,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0 -univ,TRUE,1,48,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0 -univ,TRUE,1,48,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05 -school,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058 -school,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201 -school,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041 +univ,TRUE,1,48,PTYPE_PART,0.059,0.131,0,0.4,0.2,0.21,0,0,0 +univ,TRUE,1,48,PTYPE_UNIVERSITY,0.166729,0.139607,0,0.118491,0.118491,0.118491,0.11273,0.11273,0.11273 +school,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.508,0.055,0.1,0.206,0.073,0.058 +school,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.43,0.125,0.084,0.112,0.048,0.101 +school,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.722,0.057,0.086,0.023,0.071,0.041 escort,TRUE,1,48,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06 escort,TRUE,1,48,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061 escort,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034 @@ -60,24 +60,23 @@ othdiscr,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.225,0.056,0.389,0.16,0.091,0.079 othdiscr,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.311,0.126,0.051,0.018,0.142,0.352 othdiscr,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.222,0.112,0.172,0.173,0.141,0.18 othdiscr,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.271,0.108,0.393,0.146,0.043,0.039 -atwork,TRUE,1,48,PTYPE_FULL,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_PART,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_UNIVERSITY,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_DRIVING,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -#,,,,,,,,,,,,, -work,FALSE,1,30,PTYPE_FULL,0.175,0,0,0.14,0.27,0.162,0.134,0.05,0.069 -work,FALSE,1,30,PTYPE_PART,0.097,0,0,0.252,0.211,0.192,0.159,0.089,0 -work,FALSE,1,30,PTYPE_UNIVERSITY,0.134,0,0,0.329,0.114,0.212,0.169,0.042,0 -work,FALSE,31,48,PTYPE_FULL,0.151,0.011,0,0.201,0.28,0.127,0.103,0.035,0.092 -work,FALSE,31,48,PTYPE_PART,0.11,0,0,0.243,0.281,0.13,0.119,0.036,0.081 -work,FALSE,31,48,PTYPE_UNIVERSITY,0.058,0.127,0,0.224,0.269,0.079,0.072,0.108,0.063 +atwork,TRUE,1,48,PTYPE_FULL,0,0,0,0.172,0.166,0.403,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_PART,0,0,0,0.172,0.166,0.403,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.172,0.166,0.403,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.172,0.166,0.403,0.241,0.004,0.014 +work,FALSE,1,30,PTYPE_FULL,0.02,0,0,0.24,0.27,0.217,0.134,0.05,0.069 +work,FALSE,1,30,PTYPE_PART,0.03,0,0,0.252,0.211,0.192,0.226,0.089,0 +work,FALSE,1,30,PTYPE_UNIVERSITY,0.02,0,0,0.329,0.114,0.212,0.283,0.042,0 +work,FALSE,31,48,PTYPE_FULL,0.02,0.011,0,0.301,0.28,0.127,0.134,0.035,0.092 +work,FALSE,31,48,PTYPE_PART,0.02,0,0,0.243,0.281,0.22,0.119,0.036,0.081 +work,FALSE,31,48,PTYPE_UNIVERSITY,0.03,0.127,0,0.224,0.269,0.107,0.072,0.108,0.063 work,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 univ,FALSE,1,48,PTYPE_FULL,0.352,0.032,0,0.032,0.146,0.114,0.177,0.028,0.119 univ,FALSE,1,48,PTYPE_PART,0,0,0,0.822,0.178,0,0,0,0 -univ,FALSE,1,48,PTYPE_UNIVERSITY,0.054,0.025,0,0.194,0.209,0.179,0.159,0.067,0.113 -school,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.301,0.117,0.098,0.169,0.186,0.129 -school,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.166,0.158,0.147,0.122,0.133,0.274 -school,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.38,0.148,0.089,0.146,0.102,0.135 +univ,FALSE,1,48,PTYPE_UNIVERSITY,0.044243,0.388076,0,0.068781,0.068781,0.068781,0.120446,0.120446,0.120446 +school,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.301,0.183,0.098,0.169,0.086,0.129 +school,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.166,0.064,0.147,0.122,0.103,0.124 +school,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.38,0.456,0.089,0.146,0.102,0.105 escort,FALSE,1,48,PTYPE_FULL,0,0,0,0.343,0.235,0.114,0.222,0.039,0.047 escort,FALSE,1,48,PTYPE_PART,0,0,0,0.24,0.298,0.128,0.157,0.045,0.132 escort,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.195,0.319,0.287,0.02,0.027,0.152 @@ -126,7 +125,7 @@ othdiscr,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.111,0.284,0.186,0.197,0.111,0.111 othdiscr,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.277,0.304,0.057,0.205,0.157,0 othdiscr,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.114,0.204,0.148,0.291,0.089,0.154 othdiscr,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.335,0.133,0.111,0.282,0.052,0.087 -atwork,FALSE,1,48,PTYPE_FULL,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_PART,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_UNIVERSITY,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_DRIVING,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_FULL,0,0,0,0.117,0.163,0.288,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_PART,0,0,0,0.117,0.163,0.288,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.117,0.163,0.288,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.117,0.163,0.288,0.395,0.019,0.018 diff --git a/activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml b/activitysim/examples/production_semcog/configs/trip_scheduling.yaml old mode 100755 new mode 100644 similarity index 91% rename from activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml rename to activitysim/examples/production_semcog/configs/trip_scheduling.yaml index a006e7436..3582e2546 --- a/activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml +++ b/activitysim/examples/production_semcog/configs/trip_scheduling.yaml @@ -3,7 +3,7 @@ # e.g. depart_alt_base = 5 means first column (column 0) represents period 5 DEPART_ALT_BASE: 1 -MAX_ITERATIONS: 100 +MAX_ITERATIONS: 50 #FAILFIX: drop_and_cleanup FAILFIX: choose_most_initial diff --git a/activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv b/activitysim/examples/production_semcog/configs/trip_scheduling_probs.csv old mode 100755 new mode 100644 similarity index 87% rename from activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv rename to activitysim/examples/production_semcog/configs/trip_scheduling_probs.csv index 5566771bb..56db1fde2 --- a/activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv +++ b/activitysim/examples/production_semcog/configs/trip_scheduling_probs.csv @@ -1,196 +1,196 @@ primary_purpose,outbound,tour_hour,trip_num,HR1,HR2,HR3,HR4,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23,HR24,HR25,HR26,HR27,HR28,HR29,HR30,HR31,HR32,HR33,HR34,HR35,HR36,HR37,HR38,HR39,HR40,HR41,HR42,HR43,HR44,HR45,HR46,HR47,HR48 -work,TRUE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,1,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,2,2,0.0,0.19616890254702915,0.1912603437640753,0.1863517849811215,0.4262189687077741,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,2,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,2,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -work,TRUE,3,2,0.0,0.0,0.13526428093370998,0.2025527289681607,0.2116401961671019,0.22072766336604313,0.22981513056498432,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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othmaint,FALSE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.025763405017451835,0.03525248420676535,0.04474156339607886,0.05423064258539238,0.06371972177470589,0.052531853116484756,0.041343984458263636,0.030156115800042498,0.030006367851650603,0.029856619903258712,0.029706871954866817,0.029557124006474922,0.02940737605808303,0.02925762810969114,0.02910788016129924,0.02895813221290735,0.028808384264515458,0.028658636316123563,0.02850888836773167,0.028359140419339776,0.02820939247094788,0.02805964452255599,0.027909896574164098,0.0277601486257722,0.02761040067738031,0.027460652728988413,0.027310904780596522,0.02716115683220463,0.027011408883812732,0.02686166093542084,0.02671191298702895,0.0 othmaint,FALSE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 othmaint,FALSE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,1,2,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,2,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,2,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,2,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,3,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,3,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,3,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,4,2,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,4,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,4,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,5,2,0.0,0.0,0.0,0.0,0.031751934515880424,0.9682480654841197,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,5,3,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,5,4,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othdiscr,TRUE,6,2,0.0,0.0,0.0,0.0,0.0,0.17032285402499478,0.04225409142521806,0.08066426458679786,0.11907443774837764,0.15748461090995744,0.19589478407153724,0.234304957233117,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+othdiscr,True,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +othdiscr,True,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +othdiscr,True,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 othdiscr,FALSE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,1,3,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,1,4,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,2,1,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,2,2,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,2,3,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,2,4,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,3,1,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,3,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,3,3,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,3,4,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,4,1,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,4,2,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,4,3,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,4,4,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,5,1,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,5,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,5,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,5,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,6,1,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,6,2,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,16,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08384128522626726,0.03380473162769757,0.1281381182926675,0.22247150495763743,0.3168048916226074,0.17724811996524342,0.037691348307879506,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,17,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,17,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.030519865353159144,0.04883390618458373,0.09455451887077178,0.40629186795479594,0.27311257425017954,0.13993328054556317,0.006753986840946776,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,17,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.06233811124076329,0.04796457989032244,0.0335910485398816,0.5393298865396629,0.2853687534430109,0.03140762034635889,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,17,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.470684394230403,0.3235614647434677,0.17643853525653236,0.029315605769596995,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,18,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,18,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6670032640253715,0.28683030929669123,0.046166426677937335,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,18,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2093924219194103,0.18721399556532115,0.165035569211232,0.14285714285714285,0.12067871650305372,0.09850029014896457,0.07632186379487542,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,18,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,19,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,19,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4168505683421995,0.06382943457692267,0.11846805013527428,0.1731066656936259,0.22774528125197752,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,19,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.502447386195227,0.2875787837087834,0.16585087126825768,0.04412295882773192,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,19,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,20,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.25003132023583635,0.5362903946357147,0.1172175969489665,0.07122609504281636,0.02523459313666624,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02446208671855894,0.3333333333333333,0.6422045799481078,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21438391240532625,0.7549633831268727,0.01946892967386199,0.011183774793939042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.34537929121534533,0.6306705203403534,0.02395018844430129,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9275756687493233,0.07242433125067678,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.24626498820715811,0.5875075907073781,0.07796380190012697,0.055409140361821284,0.03285447882351561,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.49488868532476943,0.17458362683218098,0.12744343270080508,0.0803032385694292,0.03316304443805331,0.08961797213476193,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.36496155111662887,0.2824807755583144,0.19999999999999996,0.1175192244416855,0.03503844888337105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7566234191098833,0.21291012065547055,0.03046646023464622,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08738614969569475,0.08892377706296906,0.11591179132204966,0.0881452695756699,0.06037874782929012,0.03261222608291035,0.0048457043365305735,0.009887486509277696,0.014929268682024821,0.019971050854771945,0.025012833027519065,0.03005461520026619,0.03509639737301331,0.04013817954576043,0.04517996171850756,0.05022174389125469,0.05526352606400181,0.06030530823674893,0.06534709040949604,0.07038887258224318,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9404273924038685,0.05957260759613155,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1831099788864871,0.6474888950395127,0.1694011260740003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1078813556421454,0.7350209243987343,0.1570977199591203,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,26,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.18673324352106085,0.707408175496916,0.037893909927191424,0.06796467105483166,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,26,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.078615484866157,0.12998792130249248,0.060637533353902144,0.7307590604774484,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,26,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,27,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,30,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08204004332566066,0.13393813841044885,0.1077569558080398,0.09979458698332919,0.09183221815861856,0.08386984933390794,0.07590748050919731,0.06794511168448669,0.05998274285977606,0.05202037403506544,0.04405800521035481,0.036095636385644184,0.02813326756093356,0.020170898736222933,0.012208529911512316,0.0042461610868016865,0.0,0.0 -escort,TRUE,30,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,31,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,31,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8554336219416518,0.09936064473481036,0.045205733323537806,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,31,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.41341231380068333,0.3333333333333333,0.25325435286598336,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,31,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,32,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,32,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6778293554590633,0.07184337821668807,0.23798913284659004,0.012338133477658615,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,32,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9347912310139336,0.06520876898606645,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,32,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,33,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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eatout,FALSE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 eatout,FALSE,1,2,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 eatout,FALSE,1,3,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/activitysim/examples/production_semcog/configs/university_location_zone_override.yaml b/activitysim/examples/production_semcog/configs/university_location_zone_override.yaml new file mode 100644 index 000000000..3a5abb4dc --- /dev/null +++ b/activitysim/examples/production_semcog/configs/university_location_zone_override.yaml @@ -0,0 +1,20 @@ +# These same settings also apply to trip_destination_univ_zone_override + +# if you want to save the original school zone, specify a column name for the persons table +# this column name is used to select major university students in downstream models (stop_freq) +ORIGINAL_ZONE_COL_NAME: original_school_zone_id + +LANDUSE_UNIV_ENROL_COL_NAME: Univ_Enrollment +# Colunn in the landuse file containing the university codes +LANDUSE_UNIV_CODE_COL_NAME: University_Name +# Specify which university codes should be resampled +UNIV_CODES_TO_OVERRIDE: + - University of Michigan + +# trip_destination_univ_zone_override specific option +# trip purpose to re-sample +TRIP_UNIVERSITY_PURPOSE: univ +# dictionary to map tour modes for tours that had a zone re-sampled +# e.g. walk tours -> walk-transit tours to allow for use of transit +# if left blank, no override occurs +TOUR_MODE_OVERRIDE_DICT: {'WALK': 'WALK_LOC'} diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home.csv b/activitysim/examples/production_semcog/configs/work_from_home.csv old mode 100755 new mode 100644 similarity index 76% rename from activitysim/examples/prototype_semcog/configs/work_from_home.csv rename to activitysim/examples/production_semcog/configs/work_from_home.csv index b5afec73f..10204ffb9 --- a/activitysim/examples/prototype_semcog/configs/work_from_home.csv +++ b/activitysim/examples/production_semcog/configs/work_from_home.csv @@ -2,9 +2,9 @@ Label,Description,Expression,work_at_home,work_away_from_home util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0 util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0 util_female_worker,Female Worker,@df.sex==2,coef_female_worker,0 -util_female_worker_preschool_child,Female worker with a Preschool Child in Household,female_worker_with_preschool_child,coef_female_worker_preschool_child,0 -util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.auPkTotal,coef_access_to_workplaces,0 -util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,nonworking_adult_in_hh,coef_non_working_adult_in_hh,0 +util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.sex==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0 +util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.workplace_location_logsum,coef_access_to_workplaces,0 +util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0 util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0 util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0 util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0 @@ -13,3 +13,4 @@ util_age_45_to_55,Age Group - 45 yrs to 55 yrs,"@df.age.between(45, 55)",coef_ag util_age_55_to_65,Age Group - 55 yrs to 65 yrs,"@df.age.between(55, 65)",coef_age_55_to_65,0 util_age_65_plus,Age Group - Older than 65yrs,@df.age > 65,coef_age_65_plus,0 util_calibration,ABM2 calibration - work from home,1,coef_calibration,0 +util_calibration_temp,Phase2 calibration temp,1,coef_calibration_p2,0 diff --git a/activitysim/examples/production_semcog/configs/work_from_home.yaml b/activitysim/examples/production_semcog/configs/work_from_home.yaml new file mode 100644 index 000000000..c4f265ced --- /dev/null +++ b/activitysim/examples/production_semcog/configs/work_from_home.yaml @@ -0,0 +1,12 @@ + +# borrowed from free parking model + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv b/activitysim/examples/production_semcog/configs/work_from_home_coeffs.csv old mode 100755 new mode 100644 similarity index 94% rename from activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv rename to activitysim/examples/production_semcog/configs/work_from_home_coeffs.csv index a3868e981..d51fd1770 --- a/activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv +++ b/activitysim/examples/production_semcog/configs/work_from_home_coeffs.csv @@ -13,3 +13,4 @@ coef_age_45_to_55,0.21438,F coef_age_55_to_65,0.451657,F coef_age_65_plus,0.583518,F coef_calibration,-0.0162,T +coef_calibration_p2,-0.6284,T diff --git a/activitysim/examples/production_semcog/configs/workplace_location.csv b/activitysim/examples/production_semcog/configs/workplace_location.csv new file mode 100644 index 000000000..9879c9032 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/workplace_location.csv @@ -0,0 +1,84 @@ +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +dest_county,,_COUNTY@skims['COUNTY'],1 +util_dist,Distance,@_DIST,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged +util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc +util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc +util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc +util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time +util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 +#,,, +util_dist_calib,Distance,@_DIST * -0.28,coef_dist +#,,, +util_Detroit_Detroit_ASC,Detroit_Detroit calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==1), 1, 0)",coef_Detroit_Detroit_asc +util_Detroit_Wayne_ASC,Detroit_Wayne calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==2), 1, 0)",coef_Detroit_Wayne_asc +util_Detroit_Oakland_ASC,Detroit_Oakland calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==3), 1, 0)",coef_Detroit_Oakland_asc +util_Detroit_Macomb_ASC,Detroit_Macomb calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==4), 1, 0)",coef_Detroit_Macomb_asc +util_Wayne_Detroit_ASC,Wayne_Detroit calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==1), 1, 0)",coef_Wayne_Detroit_asc +util_Wayne_Wayne_ASC,Wayne_Wayne calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==2), 1, 0)",coef_Wayne_Wayne_asc +util_Wayne_Oakland_ASC,Wayne_Oakland calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==3), 1, 0)",coef_Wayne_Oakland_asc +util_Wayne_Macomb_ASC,Wayne_Macomb calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==4), 1, 0)",coef_Wayne_Macomb_asc +util_Oakland_Detroit_ASC,Oakland_Detroit calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==1), 1, 0)",coef_Oakland_Detroit_asc +util_Oakland_Wayne_ASC,Oakland_Wayne calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==2), 1, 0)",coef_Oakland_Wayne_asc +util_Oakland_Oakland_ASC,Oakland_Oakland calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==3), 1, 0)",coef_Oakland_Oakland_asc +util_Oakland_Macomb_ASC,Oakland_Macomb calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==4), 1, 0)",coef_Oakland_Macomb_asc +util_Macomb_Detroit_ASC,Macomb_Detroit calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==1), 1, 0)",coef_Macomb_Detroit_asc +util_Macomb_Oakland_ASC,Macomb_Oakland calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==3), 1, 0)",coef_Macomb_Oakland_asc +util_Macomb_Macomb_ASC,Macomb_Macomb calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==4), 1, 0)",coef_Macomb_Macomb_asc +util_Washtenaw_Washtenaw_ASC,Washtenaw_Washtenaw calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==5), 1, 0)",coef_Washtenaw_Washtenaw_asc +util_StClair_Macomb_ASC,StClair_Macomb calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==4), 1, 0)",coef_StClair_Macomb_asc +util_Livingston_Oakland_ASC,Livingston_Oakland calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==3), 1, 0)",coef_Livingston_Oakland_asc +util_Livingston_Livingston_ASC,Livingston_Livingston calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==8), 1, 0)",coef_Livingston_Livingston_asc +util_Detroit_Washtenaw_ASC,Detroit_Washtenaw calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==5), 1, 0)",coef_Detroit_Washtenaw_asc +util_Detroit_Monroe_ASC,Detroit_Monroe calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==6), 1, 0)",coef_Detroit_Monroe_asc +util_Detroit_StClair_ASC,Detroit_StClair calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==7), 1, 0)",coef_Detroit_StClair_asc +util_Detroit_Livingston_ASC,Detroit_Livingston calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==8), 1, 0)",coef_Detroit_Livingston_asc +util_Wayne_Washtenaw_ASC,Wayne_Washtenaw calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==5), 1, 0)",coef_Wayne_Washtenaw_asc +util_Wayne_Monroe_ASC,Wayne_Monroe calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==6), 1, 0)",coef_Wayne_Monroe_asc +util_Wayne_StClair_ASC,Wayne_StClair calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==7), 1, 0)",coef_Wayne_StClair_asc +util_Wayne_Livingston_ASC,Wayne_Livingston calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==8), 1, 0)",coef_Wayne_Livingston_asc +util_Oakland_Washtenaw_ASC,Oakland_Washtenaw calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==5), 1, 0)",coef_Oakland_Washtenaw_asc +util_Oakland_Monroe_ASC,Oakland_Monroe calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==6), 1, 0)",coef_Oakland_Monroe_asc +util_Oakland_StClair_ASC,Oakland_StClair calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==7), 1, 0)",coef_Oakland_StClair_asc +util_Oakland_Livingston_ASC,Oakland_Livingston calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==8), 1, 0)",coef_Oakland_Livingston_asc +util_Macomb_Wayne_ASC,Macomb_Wayne calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==2), 1, 0)",coef_Macomb_Wayne_asc +util_Macomb_Washtenaw_ASC,Macomb_Washtenaw calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==5), 1, 0)",coef_Macomb_Washtenaw_asc +util_Macomb_Monroe_ASC,Macomb_Monroe calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==6), 1, 0)",coef_Macomb_Monroe_asc +util_Macomb_StClair_ASC,Macomb_StClair calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==7), 1, 0)",coef_Macomb_StClair_asc +util_Macomb_Livingston_ASC,Macomb_Livingston calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==8), 1, 0)",coef_Macomb_Livingston_asc +util_Washtenaw_Detroit_ASC,Washtenaw_Detroit calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==1), 1, 0)",coef_Washtenaw_Detroit_asc +util_Washtenaw_Wayne_ASC,Washtenaw_Wayne calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==2), 1, 0)",coef_Washtenaw_Wayne_asc +util_Washtenaw_Oakland_ASC,Washtenaw_Oakland calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==3), 1, 0)",coef_Washtenaw_Oakland_asc +util_Washtenaw_Macomb_ASC,Washtenaw_Macomb calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==4), 1, 0)",coef_Washtenaw_Macomb_asc +util_Washtenaw_Monroe_ASC,Washtenaw_Monroe calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==6), 1, 0)",coef_Washtenaw_Monroe_asc +util_Washtenaw_StClair_ASC,Washtenaw_StClair calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==7), 1, 0)",coef_Washtenaw_StClair_asc +util_Washtenaw_Livingston_ASC,Washtenaw_Livingston calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==8), 1, 0)",coef_Washtenaw_Livingston_asc +util_Monroe_Detroit_ASC,Monroe_Detroit calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==1), 1, 0)",coef_Monroe_Detroit_asc +util_Monroe_Wayne_ASC,Monroe_Wayne calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==2), 1, 0)",coef_Monroe_Wayne_asc +util_Monroe_Oakland_ASC,Monroe_Oakland calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==3), 1, 0)",coef_Monroe_Oakland_asc +util_Monroe_Macomb_ASC,Monroe_Macomb calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==4), 1, 0)",coef_Monroe_Macomb_asc +util_Monroe_Washtenaw_ASC,Monroe_Washtenaw calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==5), 1, 0)",coef_Monroe_Washtenaw_asc +util_Monroe_Monroe_ASC,Monroe_Monroe calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==6), 1, 0)",coef_Monroe_Monroe_asc +util_Monroe_StClair_ASC,Monroe_StClair calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==7), 1, 0)",coef_Monroe_StClair_asc +util_Monroe_Livingston_ASC,Monroe_Livingston calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==8), 1, 0)",coef_Monroe_Livingston_asc +util_StClair_Detroit_ASC,StClair_Detroit calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==1), 1, 0)",coef_StClair_Detroit_asc +util_StClair_Wayne_ASC,StClair_Wayne calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==2), 1, 0)",coef_StClair_Wayne_asc +util_StClair_Oakland_ASC,StClair_Oakland calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==3), 1, 0)",coef_StClair_Oakland_asc +util_StClair_Washtenaw_ASC,StClair_Washtenaw calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==5), 1, 0)",coef_StClair_Washtenaw_asc +util_StClair_Monroe_ASC,StClair_Monroe calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==6), 1, 0)",coef_StClair_Monroe_asc +util_StClair_StClair_ASC,StClair_StClair calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==7), 1, 0)",coef_StClair_StClair_asc +util_StClair_Livingston_ASC,StClair_Livingston calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==8), 1, 0)",coef_StClair_Livingston_asc +util_Livingston_Detroit_ASC,Livingston_Detroit calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==1), 1, 0)",coef_Livingston_Detroit_asc +util_Livingston_Wayne_ASC,Livingston_Wayne calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==2), 1, 0)",coef_Livingston_Wayne_asc +util_Livingston_Macomb_ASC,Livingston_Macomb calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==4), 1, 0)",coef_Livingston_Macomb_asc +util_Livingston_Washtenaw_ASC,Livingston_Washtenaw calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==5), 1, 0)",coef_Livingston_Washtenaw_asc +util_Livingston_Monroe_ASC,Livingston_Monroe calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==6), 1, 0)",coef_Livingston_Monroe_asc +util_Livingston_StClair_ASC,Livingston_StClair calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==7), 1, 0)",coef_Livingston_StClair_asc diff --git a/activitysim/examples/prototype_semcog/configs/workplace_location.yaml b/activitysim/examples/production_semcog/configs/workplace_location.yaml old mode 100755 new mode 100644 similarity index 97% rename from activitysim/examples/prototype_semcog/configs/workplace_location.yaml rename to activitysim/examples/production_semcog/configs/workplace_location.yaml index 80a350a6b..c7c209c74 --- a/activitysim/examples/prototype_semcog/configs/workplace_location.yaml +++ b/activitysim/examples/production_semcog/configs/workplace_location.yaml @@ -5,6 +5,7 @@ SIMULATE_CHOOSER_COLUMNS: - home_zone_id - is_student - pemploy + - home_county SAMPLE_SPEC: workplace_location_sample.csv SPEC: workplace_location.csv @@ -52,7 +53,7 @@ MODEL_SELECTOR: workplace CHOOSER_SEGMENT_COLUMN_NAME: income_segment # boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker +CHOOSER_FILTER_COLUMN_NAME: is_worker # FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this # FIXME - these are not needed for this model and should be re/factored out diff --git a/activitysim/examples/production_semcog/configs/workplace_location_coeffs.csv b/activitysim/examples/production_semcog/configs/workplace_location_coeffs.csv new file mode 100644 index 000000000..c9eceefb6 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/workplace_location_coeffs.csv @@ -0,0 +1,75 @@ +coefficient_name,value,constrain +coef_dist,-0.063338148,F +coef_dist_squared,0.001294031,F +coef_dist_cubed,-6.7e-05,F +coef_dist_logged,-0.47960366200000004,F +coef_dist_low_inc,-0.029396589,F +coef_dist_med_inc,-0.024858401000000002,F +coef_dist_high_inc,-0.013891636,F +coef_dist_part_time,-0.047762375999999995,F +coef_dist_student,-0.024044614,F +coef_mode_logsum,0.25,F +coef_Detroit_Detroit_asc,0.3614225021297297,F +coef_Detroit_Wayne_asc,-0.2716253801228931,F +coef_Detroit_Oakland_asc,-0.3415685018386156,F +coef_Detroit_Macomb_asc,-0.1978777638628789,F +coef_Wayne_Detroit_asc,-0.4851973885927375,F +coef_Wayne_Wayne_asc,0.168202664367836,F +coef_Wayne_Oakland_asc,-0.3865026146052553,F +coef_Wayne_Macomb_asc,-0.5717983734454497,F +coef_Oakland_Detroit_asc,-0.5611718256326389,F +coef_Oakland_Wayne_asc,0.1458903328023177,F +coef_Oakland_Oakland_asc,0.1407403155126548,F +coef_Oakland_Macomb_asc,-0.805369764039235,F +coef_Macomb_Detroit_asc,-0.5970868806566709,F +coef_Macomb_Oakland_asc,0.2856124690018894,F +coef_Macomb_Macomb_asc,-0.1343878584047892,F +coef_Washtenaw_Washtenaw_asc,0.1308598189297761,F +coef_StClair_Macomb_asc,0.6602057427329894,F +coef_Livingston_Oakland_asc,-0.3235102805765905,F +coef_Livingston_Livingston_asc,0.1133241576595546,F +coef_Detroit_Washtenaw_asc,0.0,F +coef_Detroit_Monroe_asc,0.0,F +coef_Detroit_StClair_asc,0.0,F +coef_Detroit_Livingston_asc,0.0,F +coef_Wayne_Washtenaw_asc,0.0,F +coef_Wayne_Monroe_asc,0.0,F +coef_Wayne_StClair_asc,0.0,F +coef_Wayne_Livingston_asc,0.0,F +coef_Oakland_Washtenaw_asc,0.0,F +coef_Oakland_Monroe_asc,0.0,F +coef_Oakland_StClair_asc,0.0,F +coef_Oakland_Livingston_asc,0.0,F +coef_Macomb_Wayne_asc,0.0,F +coef_Macomb_Washtenaw_asc,0.0,F +coef_Macomb_Monroe_asc,0.0,F +coef_Macomb_StClair_asc,0.0,F +coef_Macomb_Livingston_asc,0.0,F +coef_Washtenaw_Detroit_asc,0.0,F +coef_Washtenaw_Wayne_asc,0.0,F +coef_Washtenaw_Oakland_asc,0.0,F +coef_Washtenaw_Macomb_asc,0.0,F +coef_Washtenaw_Monroe_asc,0.0,F +coef_Washtenaw_StClair_asc,0.0,F +coef_Washtenaw_Livingston_asc,0.0,F +coef_Monroe_Detroit_asc,0.0,F +coef_Monroe_Wayne_asc,0.0,F +coef_Monroe_Oakland_asc,0.0,F +coef_Monroe_Macomb_asc,0.0,F +coef_Monroe_Washtenaw_asc,0.0,F +coef_Monroe_Monroe_asc,0.0,F +coef_Monroe_StClair_asc,0.0,F +coef_Monroe_Livingston_asc,0.0,F +coef_StClair_Detroit_asc,0.0,F +coef_StClair_Wayne_asc,0.0,F +coef_StClair_Oakland_asc,0.0,F +coef_StClair_Washtenaw_asc,0.0,F +coef_StClair_Monroe_asc,0.0,F +coef_StClair_StClair_asc,0.0,F +coef_StClair_Livingston_asc,0.0,F +coef_Livingston_Detroit_asc,0.0,F +coef_Livingston_Wayne_asc,0.0,F +coef_Livingston_Macomb_asc,0.0,F +coef_Livingston_Washtenaw_asc,0.0,F +coef_Livingston_Monroe_asc,0.0,F +coef_Livingston_StClair_asc,0.0,F diff --git a/activitysim/examples/production_semcog/configs/workplace_location_sample.csv b/activitysim/examples/production_semcog/configs/workplace_location_sample.csv new file mode 100644 index 000000000..1e0b08ff8 --- /dev/null +++ b/activitysim/examples/production_semcog/configs/workplace_location_sample.csv @@ -0,0 +1,82 @@ +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +dest_county,,_COUNTY@skims['COUNTY'],1 +util_dist,Distance,@_DIST,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged +util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc +util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc +util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc +util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time +util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +#,,, +util_dist_calib,Distance,@_DIST * -0.28,coef_dist +#,,, +util_Detroit_Detroit_ASC,Detroit_Detroit calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==1), 1, 0)",coef_Detroit_Detroit_asc +util_Detroit_Wayne_ASC,Detroit_Wayne calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==2), 1, 0)",coef_Detroit_Wayne_asc +util_Detroit_Oakland_ASC,Detroit_Oakland calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==3), 1, 0)",coef_Detroit_Oakland_asc +util_Detroit_Macomb_ASC,Detroit_Macomb calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==4), 1, 0)",coef_Detroit_Macomb_asc +util_Wayne_Detroit_ASC,Wayne_Detroit calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==1), 1, 0)",coef_Wayne_Detroit_asc +util_Wayne_Wayne_ASC,Wayne_Wayne calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==2), 1, 0)",coef_Wayne_Wayne_asc +util_Wayne_Oakland_ASC,Wayne_Oakland calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==3), 1, 0)",coef_Wayne_Oakland_asc +util_Wayne_Macomb_ASC,Wayne_Macomb calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==4), 1, 0)",coef_Wayne_Macomb_asc +util_Oakland_Detroit_ASC,Oakland_Detroit calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==1), 1, 0)",coef_Oakland_Detroit_asc +util_Oakland_Wayne_ASC,Oakland_Wayne calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==2), 1, 0)",coef_Oakland_Wayne_asc +util_Oakland_Oakland_ASC,Oakland_Oakland calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==3), 1, 0)",coef_Oakland_Oakland_asc +util_Oakland_Macomb_ASC,Oakland_Macomb calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==4), 1, 0)",coef_Oakland_Macomb_asc +util_Macomb_Detroit_ASC,Macomb_Detroit calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==1), 1, 0)",coef_Macomb_Detroit_asc +util_Macomb_Oakland_ASC,Macomb_Oakland calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==3), 1, 0)",coef_Macomb_Oakland_asc +util_Macomb_Macomb_ASC,Macomb_Macomb calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==4), 1, 0)",coef_Macomb_Macomb_asc +util_Washtenaw_Washtenaw_ASC,Washtenaw_Washtenaw calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==5), 1, 0)",coef_Washtenaw_Washtenaw_asc +util_StClair_Macomb_ASC,StClair_Macomb calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==4), 1, 0)",coef_StClair_Macomb_asc +util_Livingston_Oakland_ASC,Livingston_Oakland calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==3), 1, 0)",coef_Livingston_Oakland_asc +util_Livingston_Livingston_ASC,Livingston_Livingston calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==8), 1, 0)",coef_Livingston_Livingston_asc +util_Detroit_Washtenaw_ASC,Detroit_Washtenaw calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==5), 1, 0)",coef_Detroit_Washtenaw_asc +util_Detroit_Monroe_ASC,Detroit_Monroe calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==6), 1, 0)",coef_Detroit_Monroe_asc +util_Detroit_StClair_ASC,Detroit_StClair calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==7), 1, 0)",coef_Detroit_StClair_asc +util_Detroit_Livingston_ASC,Detroit_Livingston calibration constant,"@np.where((df['home_county']==1) & (_COUNTY==8), 1, 0)",coef_Detroit_Livingston_asc +util_Wayne_Washtenaw_ASC,Wayne_Washtenaw calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==5), 1, 0)",coef_Wayne_Washtenaw_asc +util_Wayne_Monroe_ASC,Wayne_Monroe calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==6), 1, 0)",coef_Wayne_Monroe_asc +util_Wayne_StClair_ASC,Wayne_StClair calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==7), 1, 0)",coef_Wayne_StClair_asc +util_Wayne_Livingston_ASC,Wayne_Livingston calibration constant,"@np.where((df['home_county']==2) & (_COUNTY==8), 1, 0)",coef_Wayne_Livingston_asc +util_Oakland_Washtenaw_ASC,Oakland_Washtenaw calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==5), 1, 0)",coef_Oakland_Washtenaw_asc +util_Oakland_Monroe_ASC,Oakland_Monroe calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==6), 1, 0)",coef_Oakland_Monroe_asc +util_Oakland_StClair_ASC,Oakland_StClair calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==7), 1, 0)",coef_Oakland_StClair_asc +util_Oakland_Livingston_ASC,Oakland_Livingston calibration constant,"@np.where((df['home_county']==3) & (_COUNTY==8), 1, 0)",coef_Oakland_Livingston_asc +util_Macomb_Wayne_ASC,Macomb_Wayne calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==2), 1, 0)",coef_Macomb_Wayne_asc +util_Macomb_Washtenaw_ASC,Macomb_Washtenaw calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==5), 1, 0)",coef_Macomb_Washtenaw_asc +util_Macomb_Monroe_ASC,Macomb_Monroe calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==6), 1, 0)",coef_Macomb_Monroe_asc +util_Macomb_StClair_ASC,Macomb_StClair calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==7), 1, 0)",coef_Macomb_StClair_asc +util_Macomb_Livingston_ASC,Macomb_Livingston calibration constant,"@np.where((df['home_county']==4) & (_COUNTY==8), 1, 0)",coef_Macomb_Livingston_asc +util_Washtenaw_Detroit_ASC,Washtenaw_Detroit calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==1), 1, 0)",coef_Washtenaw_Detroit_asc +util_Washtenaw_Wayne_ASC,Washtenaw_Wayne calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==2), 1, 0)",coef_Washtenaw_Wayne_asc +util_Washtenaw_Oakland_ASC,Washtenaw_Oakland calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==3), 1, 0)",coef_Washtenaw_Oakland_asc +util_Washtenaw_Macomb_ASC,Washtenaw_Macomb calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==4), 1, 0)",coef_Washtenaw_Macomb_asc +util_Washtenaw_Monroe_ASC,Washtenaw_Monroe calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==6), 1, 0)",coef_Washtenaw_Monroe_asc +util_Washtenaw_StClair_ASC,Washtenaw_StClair calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==7), 1, 0)",coef_Washtenaw_StClair_asc +util_Washtenaw_Livingston_ASC,Washtenaw_Livingston calibration constant,"@np.where((df['home_county']==5) & (_COUNTY==8), 1, 0)",coef_Washtenaw_Livingston_asc +util_Monroe_Detroit_ASC,Monroe_Detroit calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==1), 1, 0)",coef_Monroe_Detroit_asc +util_Monroe_Wayne_ASC,Monroe_Wayne calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==2), 1, 0)",coef_Monroe_Wayne_asc +util_Monroe_Oakland_ASC,Monroe_Oakland calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==3), 1, 0)",coef_Monroe_Oakland_asc +util_Monroe_Macomb_ASC,Monroe_Macomb calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==4), 1, 0)",coef_Monroe_Macomb_asc +util_Monroe_Washtenaw_ASC,Monroe_Washtenaw calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==5), 1, 0)",coef_Monroe_Washtenaw_asc +util_Monroe_Monroe_ASC,Monroe_Monroe calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==6), 1, 0)",coef_Monroe_Monroe_asc +util_Monroe_StClair_ASC,Monroe_StClair calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==7), 1, 0)",coef_Monroe_StClair_asc +util_Monroe_Livingston_ASC,Monroe_Livingston calibration constant,"@np.where((df['home_county']==6) & (_COUNTY==8), 1, 0)",coef_Monroe_Livingston_asc +util_StClair_Detroit_ASC,StClair_Detroit calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==1), 1, 0)",coef_StClair_Detroit_asc +util_StClair_Wayne_ASC,StClair_Wayne calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==2), 1, 0)",coef_StClair_Wayne_asc +util_StClair_Oakland_ASC,StClair_Oakland calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==3), 1, 0)",coef_StClair_Oakland_asc +util_StClair_Washtenaw_ASC,StClair_Washtenaw calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==5), 1, 0)",coef_StClair_Washtenaw_asc +util_StClair_Monroe_ASC,StClair_Monroe calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==6), 1, 0)",coef_StClair_Monroe_asc +util_StClair_StClair_ASC,StClair_StClair calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==7), 1, 0)",coef_StClair_StClair_asc +util_StClair_Livingston_ASC,StClair_Livingston calibration constant,"@np.where((df['home_county']==7) & (_COUNTY==8), 1, 0)",coef_StClair_Livingston_asc +util_Livingston_Detroit_ASC,Livingston_Detroit calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==1), 1, 0)",coef_Livingston_Detroit_asc +util_Livingston_Wayne_ASC,Livingston_Wayne calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==2), 1, 0)",coef_Livingston_Wayne_asc +util_Livingston_Macomb_ASC,Livingston_Macomb calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==4), 1, 0)",coef_Livingston_Macomb_asc +util_Livingston_Washtenaw_ASC,Livingston_Washtenaw calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==5), 1, 0)",coef_Livingston_Washtenaw_asc +util_Livingston_Monroe_ASC,Livingston_Monroe calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==6), 1, 0)",coef_Livingston_Monroe_asc +util_Livingston_StClair_ASC,Livingston_StClair calibration constant,"@np.where((df['home_county']==8) & (_COUNTY==7), 1, 0)",coef_Livingston_StClair_asc diff --git a/activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml b/activitysim/examples/production_semcog/configs/write_trip_matrices.yaml old mode 100755 new mode 100644 similarity index 88% rename from activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml rename to activitysim/examples/production_semcog/configs/write_trip_matrices.yaml index 1ee28c0f8..137c42c95 --- a/activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml +++ b/activitysim/examples/production_semcog/configs/write_trip_matrices.yaml @@ -11,7 +11,7 @@ preprocessor: HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in # save preprocessed trips table to pipeline if desired -SAVE_TRIPS_TABLE: False +SAVE_TRIPS_TABLE: True MATRICES: - file_name: trips_ea.omx @@ -58,6 +58,12 @@ MATRICES: data_field: KNRE_MIX_EA - name: SCHOOLBUS data_field: SCHOOLBUS_EA + - name: TAXI + data_field: TAXI_EA + - name: TNC_SINGLE + data_field: TNC_SINGLE_EA + - name: TNC_SHARED + data_field: TNC_SHARED_EA - file_name: trips_am.omx tables: - name: DRIVEALONE @@ -102,6 +108,12 @@ MATRICES: data_field: KNRE_MIX_AM - name: SCHOOLBUS data_field: SCHOOLBUS_AM + - name: TAXI + data_field: TAXI_AM + - name: TNC_SINGLE + data_field: TNC_SINGLE_AM + - name: TNC_SHARED + data_field: TNC_SHARED_AM - file_name: trips_md.omx tables: - name: DRIVEALONE @@ -146,6 +158,12 @@ MATRICES: data_field: KNRE_MIX_MD - name: SCHOOLBUS data_field: SCHOOLBUS_MD + - name: TAXI + data_field: TAXI_MD + - name: TNC_SINGLE + data_field: TNC_SINGLE_MD + - name: TNC_SHARED + data_field: TNC_SHARED_MD - file_name: trips_pm.omx tables: - name: DRIVEALONE @@ -190,6 +208,12 @@ MATRICES: data_field: KNRE_MIX_PM - name: SCHOOLBUS data_field: SCHOOLBUS_PM + - name: TAXI + data_field: TAXI_PM + - name: TNC_SINGLE + data_field: TNC_SINGLE_PM + - name: TNC_SHARED + data_field: TNC_SHARED_PM - file_name: trips_ev.omx tables: - name: DRIVEALONE @@ -234,6 +258,12 @@ MATRICES: data_field: KNRE_MIX_EV - name: SCHOOLBUS data_field: SCHOOLBUS_EV + - name: TAXI + data_field: TAXI_EV + - name: TNC_SINGLE + data_field: TNC_SINGLE_EV + - name: TNC_SHARED + data_field: TNC_SHARED_EV CONSTANTS: time_periods: diff --git a/activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/production_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv old mode 100755 new mode 100644 similarity index 79% rename from activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/production_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv index d7b80f3a1..121c3202d --- a/activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv +++ b/activitysim/examples/production_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -10,8 +10,8 @@ Description,Target,Expression ,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" # ea trips,, ,DRIVEALONE_EA,((trips.trip_mode == 'DRIVEALONE') & is_ea) * tour_participants -,SHARED2_EA,((trips.trip_mode == 'SHARED2') & is_ea) * tour_participants / OCC_SHARED2 -,SHARED3_EA,((trips.trip_mode == 'SHARED3') & is_ea) * tour_participants / OCC_SHARED3 +,SHARED2_EA,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_ea) * tour_participants / OCC_SHARED2" +,SHARED3_EA,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_ea) * tour_participants / OCC_SHARED3" ,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants ,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants ,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants @@ -30,10 +30,13 @@ Description,Target,Expression ,KNRE_PRM_EA,((trips.trip_mode == 'KNR_PRM') & is_ea & ~trips.outbound) * tour_participants ,KNRE_MIX_EA,((trips.trip_mode == 'KNR_MIX') & is_ea & ~trips.outbound) * tour_participants ,SCHOOLBUS_EA,((trips.trip_mode == 'SCHOOLBUS') & is_ea) * tour_participants +,TAXI_EA,((trips.trip_mode == 'TAXI') & is_ea) * tour_participants +,TNC_SINGLE_EA,((trips.trip_mode == 'TNC_SINGLE') & is_ea) * tour_participants +,TNC_SHARED_EA,((trips.trip_mode == 'TNC_SHARED') & is_ea) * tour_participants # am trips,, ,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants -,SHARED2_AM,((trips.trip_mode == 'SHARED2') & is_am) * tour_participants / OCC_SHARED2 -,SHARED3_AM,((trips.trip_mode == 'SHARED3') & is_am) * tour_participants / OCC_SHARED3 +,SHARED2_AM,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_am) * tour_participants / OCC_SHARED2" +,SHARED3_AM,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_am) * tour_participants / OCC_SHARED3" ,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants ,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants ,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants @@ -52,10 +55,13 @@ Description,Target,Expression ,KNRE_PRM_AM,((trips.trip_mode == 'KNR_PRM') & is_am & ~trips.outbound) * tour_participants ,KNRE_MIX_AM,((trips.trip_mode == 'KNR_MIX') & is_am & ~trips.outbound) * tour_participants ,SCHOOLBUS_AM,((trips.trip_mode == 'SCHOOLBUS') & is_am) * tour_participants +,TAXI_AM,((trips.trip_mode == 'TAXI') & is_am) * tour_participants +,TNC_SINGLE_AM,((trips.trip_mode == 'TNC_SINGLE') & is_am) * tour_participants +,TNC_SHARED_AM,((trips.trip_mode == 'TNC_SHARED') & is_am) * tour_participants # md trips,, ,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants -,SHARED2_MD,((trips.trip_mode == 'SHARED2') & is_md) * tour_participants / OCC_SHARED2 -,SHARED3_MD,((trips.trip_mode == 'SHARED3') & is_md) * tour_participants / OCC_SHARED3 +,SHARED2_MD,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_md) * tour_participants / OCC_SHARED2" +,SHARED3_MD,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_md) * tour_participants / OCC_SHARED3" ,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants ,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants ,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants @@ -74,10 +80,13 @@ Description,Target,Expression ,KNRE_PRM_MD,((trips.trip_mode == 'KNR_PRM') & is_md & ~trips.outbound) * tour_participants ,KNRE_MIX_MD,((trips.trip_mode == 'KNR_MIX') & is_md & ~trips.outbound) * tour_participants ,SCHOOLBUS_MD,((trips.trip_mode == 'SCHOOLBUS') & is_md) * tour_participants +,TAXI_MD,((trips.trip_mode == 'TAXI') & is_md) * tour_participants +,TNC_SINGLE_MD,((trips.trip_mode == 'TNC_SINGLE') & is_md) * tour_participants +,TNC_SHARED_MD,((trips.trip_mode == 'TNC_SHARED') & is_md) * tour_participants # pm trips,, ,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants -,SHARED2_PM,((trips.trip_mode == 'SHARED2') & is_pm) * tour_participants / OCC_SHARED2 -,SHARED3_PM,((trips.trip_mode == 'SHARED3') & is_pm) * tour_participants / OCC_SHARED3 +,SHARED2_PM,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_pm) * tour_participants / OCC_SHARED2" +,SHARED3_PM,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_pm) * tour_participants / OCC_SHARED3" ,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants ,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants ,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants @@ -96,10 +105,13 @@ Description,Target,Expression ,KNRE_PRM_PM,((trips.trip_mode == 'KNR_PRM') & is_pm & ~trips.outbound) * tour_participants ,KNRE_MIX_PM,((trips.trip_mode == 'KNR_MIX') & is_pm & ~trips.outbound) * tour_participants ,SCHOOLBUS_PM,((trips.trip_mode == 'SCHOOLBUS') & is_pm) * tour_participants +,TAXI_PM,((trips.trip_mode == 'TAXI') & is_pm) * tour_participants +,TNC_SINGLE_PM,((trips.trip_mode == 'TNC_SINGLE') & is_pm) * tour_participants +,TNC_SHARED_PM,((trips.trip_mode == 'TNC_SHARED') & is_pm) * tour_participants # ev trips,, ,DRIVEALONE_EV,((trips.trip_mode == 'DRIVEALONE') & is_ev) * tour_participants -,SHARED2_EV,((trips.trip_mode == 'SHARED2') & is_ev) * tour_participants / OCC_SHARED2 -,SHARED3_EV,((trips.trip_mode == 'SHARED3') & is_ev) * tour_participants / OCC_SHARED3 +,SHARED2_EV,"((trips.trip_mode.isin(['SHARED2', 'TAXI', 'TNC_SINGLE'])) & is_ev) * tour_participants / OCC_SHARED2" +,SHARED3_EV,"((trips.trip_mode.isin(['SHARED3', 'TNC_SHARED'])) & is_ev) * tour_participants / OCC_SHARED3" ,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants ,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants ,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants @@ -118,3 +130,6 @@ Description,Target,Expression ,KNRE_PRM_EV,((trips.trip_mode == 'KNR_PRM') & is_ev & ~trips.outbound) * tour_participants ,KNRE_MIX_EV,((trips.trip_mode == 'KNR_MIX') & is_ev & ~trips.outbound) * tour_participants ,SCHOOLBUS_EV,((trips.trip_mode == 'SCHOOLBUS') & is_ev) * tour_participants +,TAXI_EV,((trips.trip_mode == 'TAXI') & is_ev) * tour_participants +,TNC_SINGLE_EV,((trips.trip_mode == 'TNC_SINGLE') & is_ev) * tour_participants +,TNC_SHARED_EV,((trips.trip_mode == 'TNC_SHARED') & is_ev) * tour_participants diff --git a/activitysim/examples/prototype_semcog/configs_mp/logging.yaml b/activitysim/examples/production_semcog/configs_mp/logging.yaml old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/prototype_semcog/configs_mp/logging.yaml rename to activitysim/examples/production_semcog/configs_mp/logging.yaml index e932009c5..afaea1bec --- a/activitysim/examples/prototype_semcog/configs_mp/logging.yaml +++ b/activitysim/examples/production_semcog/configs_mp/logging.yaml @@ -15,7 +15,7 @@ logging: loggers: activitysim: - level: DEBUG + level: INFO handlers: [console, logfile] propagate: false diff --git a/activitysim/examples/prototype_semcog/configs_mp/settings.yaml b/activitysim/examples/production_semcog/configs_mp/settings.yaml old mode 100755 new mode 100644 similarity index 77% rename from activitysim/examples/prototype_semcog/configs_mp/settings.yaml rename to activitysim/examples/production_semcog/configs_mp/settings.yaml index f25ab6798..596474835 --- a/activitysim/examples/prototype_semcog/configs_mp/settings.yaml +++ b/activitysim/examples/production_semcog/configs_mp/settings.yaml @@ -1,25 +1,23 @@ +# Configs File with Sample Rate set by Model Runner inherit_settings: True - # raise error if any sub-process fails without waiting for others to complete fail_fast: True - # - ------------------------- dev config multiprocess: True strict: False +mem_tick: 30 use_shadow_pricing: True - - ## - example sample -households_sample_size: 0 +households_sample_size: 20 chunk_size: 0 -num_processes: 24 - +num_processes: 2 +chunk_method: hybrid_uss +chunk_training_mode: disabled # - tracing -trace_hh_id: +trace_hh_id: trace_od: #trace_hh_id: 1482966 #trace_od: [5, 11] - # to resume after last successful checkpoint, specify resume_after: _ resume_after: @@ -27,12 +25,15 @@ models: ### mp_initialize step - initialize_landuse - initialize_households + ### mp_accessibility step - compute_accessibility - - work_from_home - - add_size_tables ### mp_households step - school_location + - university_location_zone_override - workplace_location + - work_from_home + - transit_pass_subsidy + - transit_pass_ownership - auto_ownership_simulate - free_parking - telecommute_frequency @@ -56,16 +57,27 @@ models: - trip_purpose - trip_destination - trip_purpose_and_destination + - trip_destination_univ_zone_override + - parking_location_choice_at_university - trip_scheduling + - stop_frequency_university_parking - trip_mode_choice ### mp_summarize step - write_data_dictionary - write_tables - write_trip_matrices -multiprocess_steps: + +multiprocess_steps: - name: mp_initialize begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + # num_processes: 10 + slice: + tables: + - accessibility + except: True - name: mp_households begin: school_location slice: @@ -75,7 +87,6 @@ multiprocess_steps: - name: mp_summarize begin: write_data_dictionary - output_tables: action: include prefix: final_ @@ -90,7 +101,6 @@ output_tables: - school_shadow_prices - workplace_shadow_prices - joint_tour_participants - # read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) #read_skim_cache: True # write memmapped cached skims to output directory after reading from omx, for use in subsequent runs diff --git a/activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml b/activitysim/examples/production_semcog/configs_mp/shadow_pricing.yaml old mode 100755 new mode 100644 similarity index 55% rename from activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml rename to activitysim/examples/production_semcog/configs_mp/shadow_pricing.yaml index fda98f4d4..9d37d47d0 --- a/activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml +++ b/activitysim/examples/production_semcog/configs_mp/shadow_pricing.yaml @@ -1,4 +1,3 @@ - inherit_settings: True shadow_pricing_models: @@ -9,6 +8,9 @@ shadow_pricing_models: # (ignored if global use_shadow_pricing switch is False) LOAD_SAVED_SHADOW_PRICES: False +# write out choices by iteration to trace folder +WRITE_ITERATION_CHOICES: False + # number of shadow price iterations for cold start MAX_ITERATIONS: 10 @@ -18,6 +20,9 @@ MAX_ITERATIONS_SAVED: 1 # ignore criteria for zones smaller than size_threshold SIZE_THRESHOLD: 10 +# ignore criteria for zones smaller than target_threshold (total employmnet or enrollment) +TARGET_THRESHOLD: 20 + # zone passes if modeled is within percent_tolerance of predicted_size PERCENT_TOLERANCE: 5 @@ -25,13 +30,29 @@ PERCENT_TOLERANCE: 5 FAIL_THRESHOLD: 1 # CTRAMP or daysim -SHADOW_PRICE_METHOD: ctramp +# SHADOW_PRICE_METHOD: ctramp #SHADOW_PRICE_METHOD: daysim +SHADOW_PRICE_METHOD: simulation # ctramp-style shadow_pricing_method parameters DAMPING_FACTOR: 1 +# apply different targets for each segment specified in destination_size_terms.csv +school_segmentation_targets: + # format is segment: land_use_column + university: Univ_Enrollment + highschool: G9_12 + gradeschool: K_8 + + +# if target names are the same, they will be combined together +workplace_segmentation_targets: + # ideally, need to use employment with removed external workers + work_low: tot_emp + work_med: tot_emp + work_high: tot_emp + work_veryhigh: tot_emp # daysim-style shadow_pricing_method parameters # FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? DAYSIM_ABSOLUTE_TOLERANCE: 50 -DAYSIM_PERCENT_TOLERANCE: 10 +DAYSIM_PERCENT_TOLERANCE: 10 \ No newline at end of file diff --git a/activitysim/examples/production_semcog/data/households.csv b/activitysim/examples/production_semcog/data/households.csv new file mode 100644 index 000000000..d0cdbfe9f --- /dev/null +++ b/activitysim/examples/production_semcog/data/households.csv @@ -0,0 +1,19549 @@ +Unnamed: 0.1,Unnamed: 0,household_id,persons,age_of_head,race_id,cars,children,type,hincp,adjinc,hht,maz,taz +1065161,1065161,1065162,1,75.0,4.0,1.0,1.0,1,0.0,1010145,6.0,22688.0,2109.0 +1065162,1065162,1065163,1,75.0,4.0,1.0,0.0,1,8000.0,1054606,6.0,22688.0,2109.0 +1065163,1065163,1065164,1,75.0,4.0,1.0,0.0,1,0.0,1010145,6.0,22687.0,2109.0 +1065164,1065164,1065165,1,75.0,4.0,1.0,0.0,1,0.0,1010145,6.0,22688.0,2109.0 +1065165,1065165,1065166,1,75.0,4.0,1.0,0.0,1,0.0,1010145,6.0,22688.0,2109.0 +1065166,1065166,1065167,1,75.0,4.0,1.0,0.0,1,0.0,1010145,6.0,22688.0,2109.0 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+2107 +2108 +2109 +2110 +2111 +2112 +2113 +2114 +2115 +2116 +2117 +2118 +2119 +2120 +2121 +2122 +2123 +2124 +2125 +2126 +2127 +2128 +2129 +2130 +2131 +2132 +2133 +2134 +2135 +2136 +2137 +2138 +2139 +2140 +2141 +2142 +2143 +2144 +2145 +2146 +2147 +2148 +2149 +2150 diff --git a/activitysim/examples/production_semcog/extensions/__init__.py b/activitysim/examples/production_semcog/extensions/__init__.py new file mode 100644 index 000000000..11c624f5b --- /dev/null +++ b/activitysim/examples/production_semcog/extensions/__init__.py @@ -0,0 +1,3 @@ +from . import parking_location_choice_at_university +from . import stop_frequency_university_parking +from . import university_location_zone_override diff --git a/activitysim/examples/production_semcog/extensions/parking_location_choice_at_university.py b/activitysim/examples/production_semcog/extensions/parking_location_choice_at_university.py new file mode 100644 index 000000000..3f39d14a9 --- /dev/null +++ b/activitysim/examples/production_semcog/extensions/parking_location_choice_at_university.py @@ -0,0 +1,192 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import pandas as pd +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core import logit + +# from .util import estimation + +logger = logging.getLogger(__name__) + + +def closest_parking_zone_xwalk(univ_zones, parking_zones, network_los): + """ + Create lookup table matching university zone to nearest parking location. + If university zone has parking, that zone is selected. + + Parameters + ---------- + univ_zones : pandas.Series + zones to find the nearest parking location for + parking_zones : pandas.Series + zones with parking spaces + network_los : Network_LOS object + skim information + + Returns + ------- + closest_parking_df : pandas.DataFrame + index of university zone input parameter and a column closest_parking_zone + """ + skim_dict = network_los.get_default_skim_dict() + + closest_zones = [] + for univ_zone in univ_zones.to_numpy(): + if univ_zone in parking_zones.to_numpy(): + # if zone has parking data, choose that zone + closest_zones.append(univ_zone) + else: + # find nearest zone from distance skim + parking_zone_idx = np.argmin( + skim_dict.lookup(univ_zone, parking_zones.to_numpy(), "DIST") + ) + parking_zone = parking_zones.to_numpy()[parking_zone_idx] + closest_zones.append(parking_zone) + + closest_parking_df = pd.DataFrame( + {"univ_zone": univ_zones, "closest_parking_zone": closest_zones} + ) + closest_parking_df.set_index("univ_zone", inplace=True) + + return closest_parking_df + + +@inject.step() +def parking_location_choice_at_university( + trips, tours, land_use, network_los, chunk_size, trace_hh_id +): + """ + This model selects a parking location for groups of trips that are on university campuses where + the tour mode is auto. Parking locations are sampled weighted by the number of parking spots. + + The main interface to this model is the parking_location_choice_at_university() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "parking_location_choice_at_university" + model_settings_file_name = "parking_location_choice_at_university.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + + univ_codes_col = model_settings["LANDUSE_UNIV_CODE_COL_NAME"] + univ_codes = model_settings["UNIV_CODES_THAT_REQUIRE_PARKING"] + + parking_spaces_col = model_settings["LANDUSE_PARKING_SPACES_COL_NAME"] + parking_univ_code_col = model_settings["LANDUSE_PARKING_UNIV_CODE_COL_NAME"] + + parking_tour_modes = model_settings["TOUR_MODES_THAT_REQUIRE_PARKING"] + nearest_lot_tour_purposes = model_settings["TOUR_PURPOSES_TO_NEAREST_LOT"] + + trips = trips.to_frame() + tours = tours.to_frame() + land_use_df = land_use.to_frame() + + # initialize univ parking columns + trips["parked_at_university"] = False + tours["univ_parking_zone_id"] = pd.NA + + all_univ_zones = land_use_df[land_use_df[univ_codes_col].isin(univ_codes)].index + all_parking_zones = land_use_df[land_use_df[parking_spaces_col] > 0].index + + # grabbing all trips and tours that have a destination on a campus and selected tour mode + trip_choosers = trips[trips["destination"].isin(all_univ_zones)] + tour_choosers = tours[ + tours.index.isin(trip_choosers["tour_id"]) + & tours.tour_mode.isin(parking_tour_modes) + ] + + # removing trips that did not have the right tour mode. (Faster than merging tour mode first?) + trip_choosers = trip_choosers[trip_choosers.tour_id.isin(tour_choosers.index)] + trip_choosers.loc[trip_choosers["purpose"] != "Home", "parked_at_university"] = True + + logger.info("Running %s for %d tours", trace_label, len(tour_choosers)) + + closest_parking_df = closest_parking_zone_xwalk( + all_univ_zones, all_parking_zones, network_los + ) + + # Set parking locations for each university independently + for univ_code in univ_codes: + # selecting land use data + univ_zones = land_use_df[land_use_df[univ_codes_col] == univ_code].reset_index() + parking_univ_zones = land_use_df[ + land_use_df[parking_univ_code_col] == univ_code + ].reset_index() + + if len(univ_zones) == 0: + logger.info("No zones found for university code: %s", univ_code) + continue + + if (len(parking_univ_zones) == 0) or ( + parking_univ_zones[parking_spaces_col].sum() == 0 + ): + logger.info("No parking found for university code: %s", univ_code) + continue + + # selecting tours that have trips attending this university's zone(s) + univ_trip_choosers = trip_choosers[ + trip_choosers["destination"].isin(univ_zones.zone_id) + ] + parking_tours = tour_choosers.index.isin(univ_trip_choosers.tour_id) + num_parking_tours = parking_tours.sum() + + # constructing probabilities based on the number of parking spaces + # format is columns for each parking zone alternative and indexed by choosers + # probabilities are the same for each row + probs = ( + parking_univ_zones[parking_spaces_col] + / parking_univ_zones[parking_spaces_col].sum() + ).to_frame() + probs.set_index(parking_univ_zones.zone_id, inplace=True) + probs = probs.T + probs = probs.loc[np.repeat(probs.index, num_parking_tours)] + probs.set_index(tour_choosers[parking_tours].index, inplace=True) + + # making stable choices using ActivitySim's random number generator + choices, rands = logit.make_choices(probs) + choices = choices.map(pd.Series(probs.columns)) + tour_choosers.loc[parking_tours, "univ_parking_zone_id"] = choices + + # for tours that have purpose specified in model setting, set parking location to + # nearest parking lot + if nearest_lot_tour_purposes is not None: + tours_nearest_lot = tour_choosers.primary_purpose.isin( + nearest_lot_tour_purposes + ) & tour_choosers.destination.isin(all_univ_zones) + tour_choosers.loc[ + tours_nearest_lot, "univ_parking_zone_id" + ] = tour_choosers.loc[tours_nearest_lot, "destination"].map( + closest_parking_df["closest_parking_zone"] + ) + + logger.info( + "Selected parking locations for %s tours for university with code: %s", + num_parking_tours, + univ_code, + ) + + # Overriding school_zone_id in persons table + trips.loc[ + trips.index.isin(trip_choosers.index), "parked_at_university" + ] = trip_choosers["parked_at_university"] + tours.loc[ + tours.index.isin(tour_choosers.index), "univ_parking_zone_id" + ] = tour_choosers["univ_parking_zone_id"] + + pipeline.replace_table("trips", trips) + pipeline.replace_table("tours", tours) + + tracing.print_summary( + "parking_location_choice_at_university zones", + tours["univ_parking_zone_id"], + value_counts=True, + ) + + if trace_hh_id: + tracing.trace_df(tours, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/production_semcog/extensions/stop_frequency_university_parking.py b/activitysim/examples/production_semcog/extensions/stop_frequency_university_parking.py new file mode 100644 index 000000000..3d51d60b8 --- /dev/null +++ b/activitysim/examples/production_semcog/extensions/stop_frequency_university_parking.py @@ -0,0 +1,208 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import pandas as pd +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +# from .util import estimation + +logger = logging.getLogger(__name__) + + +@inject.step() +def stop_frequency_university_parking(trips, tours, chunk_size, trace_hh_id): + """ + This model inserts parking trips on drive tours that include university parking as determined in the + parking_location_choice_at_university model. Parking trips are added to the trip table before + and after groups of trips that are on campus zones. + + The main interface to this model is the stop_frequency_university_parking() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "stop_frequency_university_parking" + model_settings_file_name = "stop_frequency_university_parking.yaml" + + model_settings = config.read_model_settings(model_settings_file_name) + parking_name = model_settings["PARKING_TRIP_NAME"] + + trips = trips.to_frame() + tours = tours.to_frame() + + tours_with_parking = tours[tours["univ_parking_zone_id"].notna()] + + trip_choosers = trips[trips.tour_id.isin(tours_with_parking.index)] + trips_without_parking = trips[~trips.tour_id.isin(tours_with_parking.index)] + + if len(trip_choosers) > 0: + + trip_choosers = pd.merge( + trip_choosers.reset_index(), + tours_with_parking["univ_parking_zone_id"].reset_index(), + how="left", + on="tour_id", + ) + trip_choosers.set_index("trip_id", inplace=True, verify_integrity=True) + + # will duplicate first and last campus trips to "insert" parking trips. + # this duplication also sets the depart times where the parking trip to + # campus has the same depart as the original trip and the parking trip + # from campus has a depart time matching the following trip + trip_choosers["duplicates_needed"] = 1 + trip_choosers["park_before"] = False + trip_choosers["park_after"] = False + atwork_campus_subtours = trip_choosers["purpose"] == "atwork" + + # looking at each tour individually + trips_grouped = trip_choosers.groupby(["tour_id"])["parked_at_university"] + # to campus + trip_choosers.loc[ + (trip_choosers["parked_at_university"] == True) + & (trips_grouped.transform("shift", 1).fillna(False) == False), + "park_before", + ] = True + # from campus + trip_choosers.loc[ + (trip_choosers["parked_at_university"] == False) + & (trips_grouped.transform("shift", 1).fillna(False) == True), + "park_after", + ] = True + + trip_choosers.loc[ + trip_choosers["park_before"] | trip_choosers["park_after"], + "duplicates_needed", + ] = 2 + + # atwork subtours that came from a parent tour that is already parked on campus + # do not need parking trips. This assumes atwork subtours can not go back to + # get car from parked location. + parked_atwork_subtour_ids = trip_choosers.loc[ + (trip_choosers["purpose"] == "Work") + & (trip_choosers["primary_purpose"] == "atwork") + & (trip_choosers["parked_at_university"] == True), + "tour_id", + ] + parked_atwork_trips = trip_choosers["tour_id"].isin(parked_atwork_subtour_ids) + + trip_choosers.loc[parked_atwork_trips, "park_before"] = False + trip_choosers.loc[parked_atwork_trips, "park_after"] = False + trip_choosers.loc[parked_atwork_trips, "duplicates_needed"] = 1 + + logger.info( + "creating %d parking trips", + (trip_choosers["park_before"] | trip_choosers["park_after"]).sum(), + ) + + # duplicating trips in table + trip_choosers = trip_choosers.reset_index() + trip_choosers = trip_choosers.take( + np.repeat( + trip_choosers.index.values, trip_choosers.duplicates_needed.values + ) + ) + trip_choosers = trip_choosers.reset_index(drop=True) + + # recompute fields dependent on trip_num sequence + grouped = trip_choosers.groupby(["tour_id", "outbound"]) + trip_choosers["trip_num"] = grouped.cumcount() + 1 + trip_choosers["trip_count"] = trip_choosers["trip_num"] + grouped.cumcount( + ascending=False + ) + + # first duplicatd trip is parking trip if going to campus + park_to_campus = (trip_choosers["park_before"] == True) & ( + trip_choosers["park_before"].shift(-1) == True + ) + + # second duplicatd trip is parking trip if going away from campus + park_from_campus = (trip_choosers["park_after"] == True) & ( + trip_choosers["park_after"].shift(1) == False + ) + + park_trips = park_to_campus | park_from_campus + + trip_choosers.loc[park_trips, "purpose"] = parking_name + trip_choosers.loc[park_trips, "destination_logsum"] = pd.NA + trip_choosers.loc[park_trips, "destination"] = trip_choosers.loc[ + park_trips, "univ_parking_zone_id" + ] + trip_choosers.loc[park_trips, "original_school_taz"] = pd.NA + + # need to change subsequent origin for trips that are going to parking lot + trip_choosers["last_destination"] = trip_choosers.groupby("tour_id")[ + "destination" + ].transform("shift") + trip_choosers["origin"] = np.where( + trip_choosers["last_destination"].notna() + & (trip_choosers["last_destination"] != trip_choosers["origin"]), + trip_choosers["last_destination"], + trip_choosers["origin"], + ) + trip_choosers.drop(columns="last_destination", inplace=True) + + trip_choosers["parked_at_university"] = ( + trip_choosers.groupby("tour_id")["parked_at_university"] + .transform("shift") + .fillna(False) + ) + + # all atwork subtour trips are parked if parent tour is parked at work + trip_choosers.loc[ + trip_choosers["tour_id"].isin(parked_atwork_subtour_ids), + "parked_at_university", + ] = True + + trips = pd.concat( + [ + trip_choosers[trips.reset_index().columns], + trips_without_parking.reset_index(), + ], + ignore_index=True, + ) + + else: + trips.reset_index(inplace=True) + + trips["origin"] = trips["origin"].astype(int) + trips["destination"] = trips["destination"].astype(int) + trips["tour_includes_parking"] = np.where( + trips["tour_id"].isin(tours_with_parking.index), 1, 0 + ) + + # resetting trip_id's + trips["trip_id_pre_parking"] = trips["trip_id"] + + # taken from stop_frequency.py + # With 4 trips per leg originally, can have an additional 4 parking trips per leg + # e.g. if trips 1 and 3 are on university, need parking to and from 1 and parking + # to and from 3 + MAX_TRIPS_PER_LEG = 8 + # canonical_trip_num: 1st trip out = 1, 2nd trip out = 2, 1st in = 5, etc. + canonical_trip_num = (~trips.outbound * MAX_TRIPS_PER_LEG) + trips.trip_num + trips["trip_id"] = trips.tour_id * (2 * MAX_TRIPS_PER_LEG) + canonical_trip_num + trips.sort_values(by="trip_id", inplace=True) + + trips.set_index("trip_id", inplace=True, verify_integrity=True) + + pipeline.replace_table("trips", trips) + # since new trips were added inbetween other trips on the tour, the trip_id's changed + # resetting random number generator for trips... does this have unintended consequences? + pipeline.get_rn_generator().drop_channel("trips") + pipeline.get_rn_generator().add_channel("trips", trips) + + tracing.print_summary( + "stop_frequency_university_parking trip purposes", + trips["purpose"], + value_counts=True, + ) + + if trace_hh_id: + tracing.trace_df(trips, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/production_semcog/extensions/university_location_zone_override.py b/activitysim/examples/production_semcog/extensions/university_location_zone_override.py new file mode 100644 index 000000000..1d22110ce --- /dev/null +++ b/activitysim/examples/production_semcog/extensions/university_location_zone_override.py @@ -0,0 +1,248 @@ +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import pandas as pd +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import inject +from activitysim.core import logit + +# from .util import estimation + +logger = logging.getLogger(__name__) + + +def resample_school_zones( + choosers, land_use, model_settings, col_to_override="school_zone_id" +): + """ + Re-samples the university school zone based only on enrollment. Can apply to the original school + zone id or subsequent university trips. + + Parameters + ---------- + choosers : pd.DataFrame + subset of persons or trips that will have their school zone re-sampled + land_use: pd.DataFrame + land_use data containing columns specifying university coding and enrollment + model_settings: dict + parameters in university_location_zone_override.yaml + col_to_override: + choosers column to set new sampled zone_id to: school_zone_id or (trip) destination + + Returns + ---------- + choosers: pd.DataFrame + with new university zone id set and original zone id stored if specified in config + """ + original_zone_col_name = model_settings["ORIGINAL_ZONE_COL_NAME"] + univ_enrollment_col_name = model_settings["LANDUSE_UNIV_ENROL_COL_NAME"] + landuse_univ_code_col_name = model_settings["LANDUSE_UNIV_CODE_COL_NAME"] + allowed_univ_codes = model_settings["UNIV_CODES_TO_OVERRIDE"] + + if original_zone_col_name is not None: + choosers[original_zone_col_name] = pd.NA + + # Override school_zone_id for each requested university separately + for univ_code in allowed_univ_codes: + # selecting land use data + univ_land_use = land_use[ + land_use[landuse_univ_code_col_name] == univ_code + ].reset_index() + + if len(univ_land_use) == 0: + logger.info("No zones found for university code: %s", univ_code) + continue + + # selecting only university students with school_zone_id matching university code + choosers_to_override = choosers[col_to_override].isin(univ_land_use.zone_id) + + num_choosers_to_override = choosers_to_override.sum() + logger.info( + "Re-sampling %s zones for university with code: %s", + num_choosers_to_override, + univ_code, + ) + + if original_zone_col_name is not None: + choosers.loc[choosers_to_override, original_zone_col_name] = choosers.loc[ + choosers_to_override, col_to_override + ] + + # constructing probabilities based on the university enrollement size + # format is columns for each parking zone alternative and indexed by choosers + # probabilities are the same for each row + probs = ( + univ_land_use[univ_enrollment_col_name] + / univ_land_use[univ_enrollment_col_name].sum() + ).to_frame() + probs.set_index(univ_land_use.zone_id, inplace=True) + probs = probs.T + probs = probs.loc[np.repeat(probs.index, num_choosers_to_override)] + probs.set_index(choosers[choosers_to_override].index, inplace=True) + + # making stable choices using ActivitySim's random number generator + choices, rands = logit.make_choices(probs) + choices = choices.map(pd.Series(probs.columns)) + choosers.loc[choosers_to_override, "univ_parking_zone_id"] = choices + + return choosers + + +@inject.step() +def university_location_zone_override( + persons_merged, persons, land_use, chunk_size, trace_hh_id +): + """ + This model overrides the school taz for students attending large universities. New school tazs + are chosen based on the university enrollment in landuse without accessibility terms. This is + done to replicate the fact that university students can have classes all over campus. + + The main interface to this model is the university_location_zone_override() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "university_location_zone_override" + model_settings_file_name = "university_location_zone_override.yaml" + + choosers = persons.to_frame() + land_use_df = land_use.to_frame() + + univ_school_seg = config.read_model_settings("constants.yaml")[ + "SCHOOL_SEGMENT_UNIV" + ] + choosers = choosers[ + (choosers.school_zone_id > -1) & (choosers.school_segment == univ_school_seg) + ] + + logger.info("Running %s for %d university students", trace_label, len(choosers)) + + model_settings = config.read_model_settings(model_settings_file_name) + + choosers = resample_school_zones( + choosers, land_use_df, model_settings, col_to_override="school_zone_id" + ) + + # Overriding school_zone_id in persons table + persons = persons.to_frame() + persons.loc[persons.index.isin(choosers.index), "school_zone_id"] = choosers[ + "school_zone_id" + ].astype(int) + + # saving original zone if desired + original_zone_col_name = model_settings["ORIGINAL_ZONE_COL_NAME"] + if original_zone_col_name is not None: + persons.loc[ + persons.index.isin(choosers.index), original_zone_col_name + ] = choosers[original_zone_col_name] + + pipeline.replace_table("persons", persons) + + tracing.print_summary( + "university_location_zone_override choices", + persons["school_zone_id"], + value_counts=True, + ) + + if trace_hh_id: + tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + +@inject.step() +def trip_destination_univ_zone_override( + trips, tours, land_use, chunk_size, trace_hh_id +): + """ + This model overrides the university trip destination zone for students attending large universities. + New school tazs are chosen based on the university enrollment in landuse without accessibility terms. + This is done to replicate the fact that university students can have classes all over campus. + If the trip destination is the primary tour destination, the zone is not changed because it was + already handled in university_location_zone_override. + + The main interface to this model is the trip_destination_univ_zone_override() function. + This function is registered as an orca step in the example Pipeline. + """ + + trace_label = "trip_destination_univ_zone_override" + model_settings_file_name = "university_location_zone_override.yaml" + model_settings = config.read_model_settings(model_settings_file_name) + univ_purpose = model_settings["TRIP_UNIVERSITY_PURPOSE"] + tour_mode_override_dict = model_settings["TOUR_MODE_OVERRIDE_DICT"] + + choosers = trips.to_frame() + land_use_df = land_use.to_frame() + tours = tours.to_frame() + + # primary trips are outbound trips where the next trip is not outbound + choosers["is_primary_trip"] = np.where( + (choosers["outbound"] == True) & (choosers["outbound"].shift(-1) == False), + True, + False, + ) + choosers = choosers[ + ~(choosers["is_primary_trip"]) & (choosers["purpose"] == univ_purpose) + ] + + # changing tour mode according to model settings to avoid, e.g. really long walk trips + # This has to be done here and not in university_location_zone_override because + # this model comes after tour mode choice + if tour_mode_override_dict is not None: + tours_with_trip_resampled = choosers["tour_id"] + for orig_tour_mode, new_tour_mode in tour_mode_override_dict.items(): + tour_overrides = (tours.index.isin(choosers.tour_id)) & ( + tours["tour_mode"] == orig_tour_mode + ) + logger.info( + "Changing %d tours with mode %s to mode %s", + tour_overrides.sum(), + orig_tour_mode, + new_tour_mode, + ) + tours.loc[tour_overrides, "tour_mode"] = new_tour_mode + + logger.info("Running %s for %d university students", trace_label, len(choosers)) + + choosers = resample_school_zones( + choosers, land_use_df, model_settings, col_to_override="destination" + ) + + # Overriding school_zone_id in persons table + trips = trips.to_frame() + trips.loc[trips.index.isin(choosers.index), "destination"] = choosers[ + "destination" + ].astype(int) + + # need to change subsequent origin for trips that were changed + trips["last_destination"] = trips.groupby("tour_id")["destination"].transform( + "shift" + ) + trips["origin"] = np.where( + trips["last_destination"].notna() + & (trips["last_destination"] != trips["origin"]), + trips["last_destination"], + trips["origin"], + ) + trips.drop(columns="last_destination", inplace=True) + + # saving old zone choice if requested + original_zone_col_name = model_settings["ORIGINAL_ZONE_COL_NAME"] + if original_zone_col_name is not None: + trips.loc[trips.index.isin(choosers.index), original_zone_col_name] = choosers[ + original_zone_col_name + ] + + pipeline.replace_table("trips", trips) + pipeline.replace_table("tours", tours) + + tracing.print_summary( + "trip_destination_univ_zone_override for zones", + trips[original_zone_col_name], + value_counts=True, + ) + + if trace_hh_id: + tracing.trace_df(trips, label=trace_label, warn_if_empty=True) diff --git a/activitysim/examples/prototype_semcog/scripts/semcog_crop.py b/activitysim/examples/production_semcog/scripts/semcog_crop.py similarity index 100% rename from activitysim/examples/prototype_semcog/scripts/semcog_crop.py rename to activitysim/examples/production_semcog/scripts/semcog_crop.py diff --git a/activitysim/examples/prototype_semcog/simulation.py b/activitysim/examples/production_semcog/simulation.py old mode 100755 new mode 100644 similarity index 93% rename from activitysim/examples/prototype_semcog/simulation.py rename to activitysim/examples/production_semcog/simulation.py index 8313dd45e..cba7add1b --- a/activitysim/examples/prototype_semcog/simulation.py +++ b/activitysim/examples/production_semcog/simulation.py @@ -6,6 +6,8 @@ from activitysim.cli.run import add_run_args, run +import extensions + if __name__ == "__main__": parser = argparse.ArgumentParser() diff --git a/activitysim/examples/production_semcog/test/configs/settings.yaml b/activitysim/examples/production_semcog/test/configs/settings.yaml new file mode 100644 index 000000000..296f553c3 --- /dev/null +++ b/activitysim/examples/production_semcog/test/configs/settings.yaml @@ -0,0 +1,28 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# number of households to simulate +households_sample_size: 20 +chunk_size: 0 + +# - shadow pricing global switches +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - households + - persons + - tours + - trips diff --git a/activitysim/examples/production_semcog/test/configs_mp/network_los.yaml b/activitysim/examples/production_semcog/test/configs_mp/network_los.yaml new file mode 100644 index 000000000..1c4cd79da --- /dev/null +++ b/activitysim/examples/production_semcog/test/configs_mp/network_los.yaml @@ -0,0 +1,6 @@ +inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False diff --git a/activitysim/examples/production_semcog/test/configs_mp/settings.yaml b/activitysim/examples/production_semcog/test/configs_mp/settings.yaml new file mode 100644 index 000000000..139643d06 --- /dev/null +++ b/activitysim/examples/production_semcog/test/configs_mp/settings.yaml @@ -0,0 +1,34 @@ +inherit_settings: True + +# treat warnings as errors +strict: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +multiprocess: True + +use_shadow_pricing: False + +chunk_size: 0 +num_processes: 2 + +# number of households to simulate +households_sample_size: 20 + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +cleanup_pipeline_after_run: True + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - households + - persons + - tours + - trips diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home_annotate_persons_preprocessor.csv b/activitysim/examples/production_semcog/test/configs_mp/work_from_home_annotate_persons_preprocessor.csv similarity index 100% rename from activitysim/examples/prototype_semcog/configs/work_from_home_annotate_persons_preprocessor.csv rename to activitysim/examples/production_semcog/test/configs_mp/work_from_home_annotate_persons_preprocessor.csv diff --git a/activitysim/examples/prototype_semcog/output/cache/.gitignore b/activitysim/examples/production_semcog/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/prototype_semcog/output/cache/.gitignore rename to activitysim/examples/production_semcog/test/output/cache/.gitignore diff --git a/activitysim/examples/prototype_semcog/output/log/.gitignore b/activitysim/examples/production_semcog/test/output/log/.gitignore similarity index 100% rename from activitysim/examples/prototype_semcog/output/log/.gitignore rename to activitysim/examples/production_semcog/test/output/log/.gitignore diff --git a/activitysim/examples/prototype_semcog/output/trace/.gitignore b/activitysim/examples/production_semcog/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/prototype_semcog/output/trace/.gitignore rename to activitysim/examples/production_semcog/test/output/trace/.gitignore diff --git a/activitysim/examples/production_semcog/test/regress/final_trips.csv b/activitysim/examples/production_semcog/test/regress/final_trips.csv new file mode 100644 index 000000000..3402fa28c --- /dev/null +++ b/activitysim/examples/production_semcog/test/regress/final_trips.csv @@ -0,0 +1,169 @@ +trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,original_school_zone_id,parked_at_university,depart,tour_includes_parking,trip_id_pre_parking,trip_mode,mode_choice_logsum +1727022433,2632656,1066353,eatout,1,True,2,22766,22688,107938902,parking,,,False,25.0,1,863511217,DRIVEALONE,-0.8746901452708546 +1727022434,2632656,1066353,eatout,2,True,2,22767,22766,107938902,eatout,,,True,25.0,1,863511217,WALK,6.210014031663612 +1727022441,2632656,1066353,eatout,1,False,2,22766,22767,107938902,parking,,,True,37.0,1,863511221,WALK,6.210037419792028 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+import pandas as pd +import pandas.testing as pdt +import pkg_resources + +from activitysim.core import inject + + +def teardown_function(func): + inject.clear_cache() + inject.reinject_decorated_tables() + + +def run_test_semcog(multiprocess=False): + def example_path(dirname): + resource = os.path.join("examples", "production_semcog", dirname) + return pkg_resources.resource_filename("activitysim", resource) + + def test_path(dirname): + return os.path.join(os.path.dirname(__file__), dirname) + + def regress(): + regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) + final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) + pdt.assert_frame_equal(final_trips_df, regress_trips_df) + + file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") + + if multiprocess: + subprocess.run( + [ + "coverage", + "run", + "-a", + file_path, + "-c", + test_path("configs_mp"), + "-c", + example_path("configs_mp"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ], + check=True, + ) + else: + subprocess.run( + [ + "coverage", + "run", + "-a", + file_path, + "-c", + test_path("configs"), + "-c", + example_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ], + check=True, + ) + + regress() + + +def test_semcog(): + run_test_semcog(multiprocess=False) + + +def test_semcog_mp(): + run_test_semcog(multiprocess=True) + + +if __name__ == "__main__": + run_test_semcog(multiprocess=False) + run_test_semcog(multiprocess=True) diff --git a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore index 1d085cacc..3dd2e62f9 100644 --- a/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore +++ b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore @@ -1 +1,2 @@ -** +*.mmap +*.feather diff --git a/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv old mode 100644 new mode 100755 diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv new file mode 100644 index 000000000..732a58d47 --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +,periods_left,(df.latest - df.earliest) +# binning the periods differently for inbound and outbound +,periods_left_min,"np.where(df['outbound'], periods_left.clip(upper=25), periods_left.clip(upper=34))" +,periods_left_max,"np.where(((periods_left >= 25) & (df['outbound'])) | ((periods_left >= 34) & (~df['outbound'])), 47, periods_left)" +,tour_purpose,"reindex(tours.tour_type, df.tour_id)" +,tour_purpose_grouped,"np.where(tour_purpose.isin(['work','school','univ']), 'mand', 'non_mand')" +,half_tour_stops_remaining_grouped,(df.trip_count - df.trip_num).clip(upper=1) \ No newline at end of file diff --git a/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv new file mode 100644 index 000000000..411be00eb --- /dev/null +++ b/activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv @@ -0,0 +1,245 @@ +periods_left_min,periods_left_max,outbound,tour_purpose_grouped,half_tour_stops_remaining_grouped,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,All +0,0,True,non_mand,0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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+34,47,False,mand,1,0.0,0.0,0.0,0.0,0.1018436706346972,0.0,0.0,0.0,0.0,0.002263878168597051,0.0,0.000983293615706626,0.0,0.008218649939220985,0.023737052572553673,0.021264126033222547,0.05518389314660721,0.010993620292826722,0.20415745418381434,0.07475058698068834,0.07116274568401747,0.07424603443354091,0.06495972196633373,0.03217245961981454,0.08352494702875252,0.03514324066332105,0.026534322261271913,0.04879548794140681,0.004334841005982029,0.02812637911830777,0.0,0.0,0.015797438844245334,0.0,0.011806155865071414,1.0 diff --git a/activitysim/examples/prototype_semcog/change_log.txt b/activitysim/examples/prototype_semcog/change_log.txt deleted file mode 100644 index bb49241b3..000000000 --- a/activitysim/examples/prototype_semcog/change_log.txt +++ /dev/null @@ -1,200 +0,0 @@ -### -##### extensions -##### simulation.py -### - -# added extensions folder -# changed relative imports of estimation to absolute in model extension steps - -#from .util import estimation -from activitysim.abm.models.util import estimation - -# added line to simulation.py to load it:" - -import extensions - - -### -##### cdap.yaml -### -COEFFICIENTS: _dummy_coefficients.csv -INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv - - -### -##### _dummy_coefficients.csv -### - -# dummy file to allow coefficients hard coded into -# cdap.yaml INDIV_AND_HHSIZE1_SPEC and INTERACTION_COEFFICIENTS - -### -##### tour_mode_choice_coeffs_template.csv -### - -# comment out unused coefficients - -# walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork - - -### -##### tour_scheduling_joint_coeffs.csv -### - -WARNING - duplicate coefficients in configs/tour_scheduling_joint_coeffs.csv - value constrain -coefficient_name -coef_eatout_Departure_Constant_12_00_PM_12_30_PM 1.164107 F -coef_eatout_Departure_Constant_06_00_PM_06_30_PM -0.425984 F -coef_eatout_Departure_Constant_06_30_PM_07_00_PM 0.000000 T -coef_eatout_Departure_Constant_07_00_PM_07_30_PM -0.227801 F -coef_eatout_Departure_Constant_After_07_30_PM -0.293904 F -coef_eatout_Departure_Constant_Shift_for_every_... -0.554409 F -coef_eatout_Departure_Constant_12_00_PM_12_30_PM 0.673838 F -coef_eatout_Departure_Constant_06_00_PM_06_30_PM 0.000000 T -coef_eatout_Departure_Constant_06_30_PM_07_00_PM -0.282096 F -coef_eatout_Departure_Constant_07_00_PM_07_30_PM -0.299749 F -coef_eatout_Departure_Constant_After_07_30_PM -0.845301 F -coef_eatout_Departure_Constant_Shift_for_every_... -0.667843 F -coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM 0.629299 F -coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM 0.938529 F -coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM 0.584420 F -coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM 0.842550 F -coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM -0.124886 F -coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM -0.605456 F -coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM 0.000000 T -coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM -0.657383 F -coef_eatout_Arrival_Constant_Shift_for_every_30... -0.204722 F -coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM 2.002032 F -coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM 2.115334 F -coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM 1.647880 F -coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM 1.525310 F -coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM -0.384557 F -coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM -0.044050 F -coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM 0.000000 T -coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM -0.239939 F -coef_eatout_Arrival_Constant_Shift_for_every_30... -0.204771 F -coef_eatout_Duration_Constant_0_hours -11.726604 F -coef_eatout_Duration_Constant_0p5_hous -5.088731 F -coef_eatout_Duration_Constant_1_hour -0.125521 F -coef_eatout_Duration_Constant_1p5_hours 0.000000 T -coef_eatout_Duration_Constant_0_hours -4.268997 F -coef_eatout_Duration_Constant_0p5_hous -1.323298 F -coef_eatout_Duration_Constant_1_hour 0.000000 T -coef_eatout_Duration_Constant_1p5_hours -0.195669 F -coef_disc_Duration_Constant_0_hours -0.944258 F -coef_disc_Duration_Constant_0p5_hous -0.117696 F -coef_disc_Duration_Constant_1_hour 0.438404 F -coef_disc_Duration_Constant_1p5_hours -0.002500 F -coef_disc_Duration_Constant_2_hours 0.000000 T -coef_disc_Duration_Constant_Shift_for_every_30_... -0.108261 F -coef_disc_Duration_Constant_0_hours 0.436988 F -coef_disc_Duration_Constant_0p5_hous 1.359676 F -coef_disc_Duration_Constant_1_hour 1.692673 F -coef_disc_Duration_Constant_1p5_hours 1.118933 F -coef_disc_Duration_Constant_2_hours 0.771256 F -coef_disc_Duration_Constant_Shift_for_every_30_... -0.700936 F - - -### -##### trip_destination.yaml -### - -# will need coefficients file to run estimation... -#COEFFICIENTS: trip_destination_coefficients.csv -COEFFICIENTS: _dummy_coefficients.csv - - -### -##### trip_mode_choice.yaml -### - -#COEFFICIENTS: trip_mode_choice_coeffs.csv -LEGACY_COEFFICIENTS: trip_mode_choice_coeffs.csv - -### -##### trip_mode_choice_coeffs.csv -### - -# change Expression column name to coefficient_name -#Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork - -### -##### atwork_subtour_destination.csv -##### atwork_subtour_destination_sample.csv -### - -# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork -# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 -util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork -util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 - -### -##### tour_scheduling_non_mandatory.csv -### - -# change remaining_periods_available() call to max_time_block_available() -# tt.remaining_periods_available(df.person_id, df.start, df.end) -tt.max_time_block_available(df.person_id) - -#util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes - -# and similar changes in 8 other lines where the same substitution - -### -##### tour_scheduling_atwork_coeffs.csv -### - -# remove duplicate coefficients - -#coef_1200_AM_1230_PM,0,T -#coef_1230_PM_0100_PM,-0.084950396,F -coef_1200_AM_1230_PM,-0.045281832,F -coef_1230_PM_0100_PM,0.214070736,F - -### -##### work_from_home.py -### - - # E211 whitespace before '(' - # print (dest_choice_column_name) - print(dest_choice_column_name) - - # E712 comparison to True should be 'if cond is True:' or 'if cond:' - # persons[dest_choice_column_name] = np.where(persons.work_from_home == True, -1, persons[dest_choice_column_name]) - persons[dest_choice_column_name] = np.where(persons.work_from_home is True, -1, persons[dest_choice_column_name]) - - -### -##### tour_departure_and_duration_segments.csv -### - -# new file - contents of which I just guessed at - you should check - -tour_purpose,time_period,start,end -work,EA,3,5 -work,AM,8,11 -work,MD,14,22 -work,PM,26,30 -work,EV,33,44 -#,,, -school,EA,3,5 -school,AM,8,11 -school,MD,14,22 -school,PM,26,30 -school,EV,33,44 -#,,, -univ,EA,3,5 -univ,AM,8,11 -univ,MD,14,22 -univ,PM,26,30 -univ,EV,33,44 - diff --git a/activitysim/examples/prototype_semcog/configs/auto_ownership.csv b/activitysim/examples/prototype_semcog/configs/auto_ownership.csv deleted file mode 100755 index e9ad2c58e..000000000 --- a/activitysim/examples/prototype_semcog/configs/auto_ownership.csv +++ /dev/null @@ -1,25 +0,0 @@ -Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 -util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 -util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 -util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up -util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 -util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 -util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 -util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 -util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 -util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 -util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k -util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc -util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers -util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers -util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers -util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers -util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker diff --git a/activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv b/activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv deleted file mode 100755 index adad6e991..000000000 --- a/activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv +++ /dev/null @@ -1,68 +0,0 @@ -coefficient_name,value,constrain -coef_cars1_drivers_2,0.0,T -coef_cars1_drivers_3,0.0,T -coef_cars1_persons_16_17,0.0,T -coef_cars234_asc_marin,0.0,T -coef_cars1_persons_25_34,0.0,T -coef_cars1_num_workers_clip_3,0.0,T -coef_cars1_hh_income_30_up,0.0,T -coef_cars1_density_0_10_no_workers,0.0,T -coef_cars1_density_10_up_workers,-0.0006916064567687393,F -coef_retail_non_motor,-0.03,T -coef_cars4_asc,-2.1157137267630084,F -coef_cars3_asc,-0.5389664867851066,F -coef_cars34_persons_16_17,-1.0131713092471806,F -coef_cars2_asc,0.5089927738334394,F -coef_cars34_persons_18_24,-0.22633082627703238,F -coef_cars2_persons_18_24,-0.2779863655452459,F -coef_cars2_persons_16_17,-0.8499905464429038,F -coef_cars34_persons_25_34,-0.5072697829616668,F -coef_cars1_asc_county,-0.5660000000000001,F -coef_retail_transit_workers,-0.26599406864996145,F -coef_cars2_persons_25_34,-0.2845857507016052,F -coef_cars2_asc_county,-0.4429,F -coef_cars1_persons_18_24,0.3122834693531618,F -coef_cars34_density_0_10_no_workers,-1.0611910844303392,F -coef_retail_transit_no_workers,-0.4610891860644177,F -coef_cars1_asc_marin,-0.2434,F -coef_cars34_asc_county,-0.2372,F -coef_cars2_density_0_10_no_workers,-0.4334566014350984,F -coef_cars34_density_10_up_no_workers,-0.1766,T -coef_cars2_density_10_up_no_workers,0.22349472733850723,F -coef_cars2_density_10_up_workers,-0.1106,F -coef_cars1_density_10_up_no_workers,-0.6278796181279449,F -coef_cars2_hh_income_30_up,0.013802348149352129,F -coef_cars3_hh_income_30_up,0.01756826620528943,F -coef_cars4_hh_income_30_up,0.020241067403575532,F -coef_cars1_presence_children_5_17,0.2201929251562138,F -coef_cars1_hh_income_0_30k,0.08754817729196587,F -coef_cars2_hh_income_0_30k,0.11363199169081308,F -coef_cars3_hh_income_0_30k,0.10919644885632351,F -coef_cars4_hh_income_0_30k,0.12371561063030768,F -coef_retail_auto_no_workers,-0.27201647971031934,F -coef_cars34_asc_san_francisco,0.1458,F -coef_retail_auto_workers,-0.3440504516404184,F -coef_cars2_presence_children_5_17,0.3267549927594411,F -coef_cars2_num_workers_clip_3,0.7131266645999695,F -coef_cars1_presence_children_0_4,-0.13410330297845457,F -coef_cars1_asc_san_francisco,0.4259,F -coef_cars2_asc_san_francisco,0.4683,F -coef_cars1_auto_time_saving_per_worker,1.3138947141281918,F -coef_cars34_presence_children_5_17,0.09288972465342095,F -coef_cars3_auto_time_saving_per_worker,0.9830482363478317,F -coef_cars2_auto_time_saving_per_worker,1.0902251470013522,F -coef_cars3_num_workers_clip_3,1.000861534518726,F -coef_cars234_presence_children_0_4,0.049938987712858804,F -coef_cars4_auto_time_saving_per_worker,0.8990148505984551,F -coef_cars4_num_workers_clip_3,1.079450657634896,F -coef_cars1_asc,3.1493581264401556,F -coef_cars1_drivers_4_up,-1.4515192922101592,F -coef_cars4_drivers_2,2.4056683705077937,F -coef_cars2_drivers_2,2.817372734348654,F -coef_cars3_drivers_2,2.6801310124351496,F -coef_cars2_drivers_3,2.5819723625758484,F -coef_cars4_drivers_3,3.964857311371601,F -coef_cars3_drivers_3,4.277120564378806,F -coef_cars2_drivers_4_up,1.5182056527267993,F -coef_cars3_drivers_4_up,3.4916291197074343,F -coef_cars4_drivers_4_up,4.509213244436791,F diff --git a/activitysim/examples/prototype_semcog/configs/free_parking_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/free_parking_annotate_persons_preprocessor.csv deleted file mode 100755 index 677599949..000000000 --- a/activitysim/examples/prototype_semcog/configs/free_parking_annotate_persons_preprocessor.csv +++ /dev/null @@ -1,2 +0,0 @@ -Description,Target,Expression -,workplace_county_id,"reindex(land_use.COUNTY, persons.workplace_zone_id)" diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv deleted file mode 100755 index 6c2089653..000000000 --- a/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv +++ /dev/null @@ -1,4 +0,0 @@ -Description,Target,Expression -departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" -arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" -duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" diff --git a/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv deleted file mode 100755 index 6c2089653..000000000 --- a/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv +++ /dev/null @@ -1,4 +0,0 @@ -Description,Target,Expression -departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" -arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" -duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv deleted file mode 100644 index 3f3110621..000000000 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv +++ /dev/null @@ -1,15 +0,0 @@ -Description,Target,Expression -#,, -departure_shift_generic,departureLinearShift1_generic,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" -arrival_shift_generic,arrivalLinearShift1_generic,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" -departure_shift2_generic,departureLinearShift2_generic,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" -arrival_shift2_generic,arrivalLinearShift2_generic,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" -duration_shift_generic,durationShift_generic,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" -#,, -depConstShift1_generic,depConstShift1_generic,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" -depConstShift2_generic,depConstShift2_generic,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" -arrConstShift1_generic,arrConstShift1_generic,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" -arrConstShift2_generic,arrConstShift2_generic,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" -#,, -,origin_to_destination_distance,1 -,num_person_joint_tours,1 diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv deleted file mode 100755 index 26f140378..000000000 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv +++ /dev/null @@ -1,15 +0,0 @@ -Description,Target,Expression -#,, -departure_shift_escort,departureLinearShift1_escort,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" -arrival_shift_escort,arrivalLinearShift1_escort,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" -departure_shift2_escort,departureLinearShift2_escort,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" -arrival_shift2_escort,arrivalLinearShift2_escort,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" -duration_shift_escort,durationShift_escort,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" -#,, -depConstShift1_escort,depConstShift1_escort,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" -depConstShift2_escort,depConstShift2_escort,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" -arrConstShift1_escort,arrConstShift1_escort,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" -arrConstShift2_escort,arrConstShift2_escort,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" -,, -,origin_to_destination_distance,1 -,num_person_joint_tours,1 diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv deleted file mode 100644 index 4d07acfb8..000000000 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv +++ /dev/null @@ -1,15 +0,0 @@ -Description,Target,Expression -#,, -departure_shift_escort,departureLinearShift1_escort,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" -arrival_shift_escort,arrivalLinearShift1_escort,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" -departure_shift2_escort,departureLinearShift2_escort,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" -arrival_shift2_escort,arrivalLinearShift2_escort,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" -duration_shift_escort,durationShift_escort,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" -#,, -depConstShift1_escort,depConstShift1_escort,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" -depConstShift2_escort,depConstShift2_escort,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" -arrConstShift1_escort,arrConstShift1_escort,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" -arrConstShift2_escort,arrConstShift2_escort,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" -#,, -,origin_to_destination_distance,1 -,num_person_joint_tours,1 diff --git a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv deleted file mode 100644 index 50efe1c78..000000000 --- a/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv +++ /dev/null @@ -1,15 +0,0 @@ -Description,Target,Expression -#,, -departure_shift_shopping,departureLinearShift1_shopping,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" -arrival_shift_shopping,arrivalLinearShift1_shopping,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" -departure_shift2_shopping,departureLinearShift2_shopping,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" -arrival_shift2_shopping,arrivalLinearShift2_shopping,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" -duration_shift_shopping,durationShift_shopping,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" -#,, -depConstShift1_shopping,depConstShift1_shopping,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" -depConstShift2_shopping,depConstShift2_shopping,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" -arrConstShift1_shopping,arrConstShift1_shopping,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" -arrConstShift2_shopping,arrConstShift2_shopping,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" -#,, -,origin_to_destination_distance,1 -,num_person_joint_tours,1 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv deleted file mode 100755 index 8f9709d4b..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv +++ /dev/null @@ -1,13 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,income_in_thousands<50000,,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45 -Number of eating tours tours undertaken by the person,num_eatout_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Subtour departure less than or equal to 11AM,start<19,,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 -Subtour return time greater or equal to 2PM,end>24,,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34 -Subtour duration in hours (integer),end-start,,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56 -dummy for subtour origin (tour destination) at Exurban or Rual (AreaTypes = 6 or 7),destination_area_type >5,,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27 -Primary Destination Accessibility (LOG of it),pracc,,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18 -subtour distance in miles (from tour destination to subtour primary destination one way),distance_in_miles,,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02 -Alternative specific constant for outbound stops,1,,,,,-3.896,-3.896,-3.896,-3.896,-5.709,-5.709,-5.709,-5.709,-7.361,-7.361,-7.361,-7.361 -Alternative specific constant for return stops,1,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,2.127,0,0,2.127,2.127,0,2.127,2.127,2.127 -Phase1_Calibration,1,,-0.214873516,-0.08829587,-0.021076701,-0.583908707,1.759594513,1.768617768,0.015908847,-0.496833561,0.742087614,0.721158833,-0.038026423,1.210255745,0.858858431,2.537970801,3.355148354 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv deleted file mode 100755 index c6dc0b83b..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv +++ /dev/null @@ -1,54 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-2.19,-2.19,-2.19,-2.19,-4.516,-4.516,-4.516,-4.516,-5.255,-5.255,-5.255,-5.255 -Alternative specific constant for return stops,~is_joint,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.94,0,0,0.94,2.026,0,0.94,2.026,2.026 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 -Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 -Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 -Phase1_Calibration,~is_joint,,0.402777606,0.769044419,1.547375234,0.17724742,1.523737838,1.704085866,1.219450496,1.50935493,2.583402672,2.411844468,0.759903236,1.610165394,2.324582352,2.016696053,2.783482815 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv deleted file mode 100755 index c38df6d57..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv +++ /dev/null @@ -1,48 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91 -Number of work tours undertaken by the person,num_work_tours,,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29 -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32 -dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-2.173,-2.173,-2.173,-2.173,-4.294,-4.294,-4.294,-4.294,-4.758,-4.758,-4.758,-4.758 -Alternative specific constant for return stops,~is_joint,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0,0,0,0,-1.807,0,0,-1.807,-1.807 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,-0.281471428,-0.016095822,0.633091127,0.54790928,0.034142435,0.648536014,1.179253849,1.588769694,1.014803541,1.362137736,4.47587972,1.689728522,1.349848675,3.365045965,4.461548606 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv deleted file mode 100755 index 9dc631cee..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv +++ /dev/null @@ -1,50 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176 -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715 -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 10 Miles ,(distance_in_miles < 10),,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756 -dummy for distance in miles,distance_in_miles,,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.581,-1.581,-1.581,-1.581,-3.323,-3.323,-3.323,-3.323,-4.623,-4.623,-4.623,-4.623 -Alternative specific constant for return stops,~is_joint,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.863,0,0,0.863,0.939,0,0.863,0.939,0.939 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.187263987,0.670000983,0.992092361,0.283823906,0.461537002,0.958158373,-0.156485013,0.806611419,0.872137091,0.343003767,0.7657564,2.063744949,1.13525473,1.376716611,2.113754844 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv deleted file mode 100755 index d8f4a60fc..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv +++ /dev/null @@ -1,51 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 -High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 -Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252 -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135 -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours undertaken by the houshold,num_hh_maint_tours,,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468 -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408 -dummy for distance in miles,distance_in_miles,,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.761,-1.761,-1.761,-1.761,-3.661,-3.661,-3.661,-3.661,-5.426,-5.426,-5.426,-5.426 -Alternative specific constant for return stops,~is_joint,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.414,0,0,0.414,0.488,0,0.414,0.488,0.488 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.475190752,0.530588025,1.389497026,0.960593185,0.775245847,1.122815367,1.226361438,1.683900584,1.594911845,1.101961687,2.302278146,3.35277309,2.734475828,2.746850954,3.93916549 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv deleted file mode 100755 index d48e0f739..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv +++ /dev/null @@ -1,45 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299 -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331 -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163 -Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-2.123,-2.123,-2.123,-2.123,-3.798,-3.798,-3.798,-3.798,-5.85,-5.85,-5.85,-5.85 -Alternative specific constant for return stops,1,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0.701,0,0,0.701,1.135,0,0.701,1.135,1.135 -Phase1_Calibration,1,,-0.462185603,0.034598859,0.143710811,-0.107820253,0.247416913,0.525399532,0.065742876,-0.264881606,-0.844194184,-0.191651123,-0.295391166,1.390171934,-1.256414914,0.034312538,0.662067578 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv deleted file mode 100755 index 909e70610..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv +++ /dev/null @@ -1,51 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482 -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709 -Number of school tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977 -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733 -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Dummy for only adults participate in the tour,composition=='adults',,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768 -dummy for distance in miles,distance_in_miles,,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.339,-1.339,-1.339,-1.339,-3.11,-3.11,-3.11,-3.11,-4.487,-4.487,-4.487,-4.487 -Alternative specific constant for return stops,~is_joint,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.252,0,0,0.252,0.514,0,0.252,0.514,0.514 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.061908526,0.211807872,0.269959028,0.159052037,0.386325269,0.209133272,0.563685874,1.002319475,1.182476858,1.537241286,1.004078003,2.038636069,1.69019331,2.060858768,2.809711477 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv deleted file mode 100755 index c04a51f48..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv +++ /dev/null @@ -1,54 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.081,-1.081,-1.081,-1.081,-2.874,-2.874,-2.874,-2.874,-4.552,-4.552,-4.552,-4.552 -Alternative specific constant for return stops,~is_joint,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.496,0,0,0.496,0.882,0,0.496,0.882,0.882 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 -Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 -Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 -Phase1_Calibration,~is_joint,,0.38246443,0.839705649,1.208071759,0.577945215,0.470848758,0.683396345,0.561543342,1.335624596,0.972779572,1.949517699,0.318404726,3.210420697,0.539522475,0.864775287,3.503722118 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv deleted file mode 100755 index 9c9ba1af0..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv +++ /dev/null @@ -1,45 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823 -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703 -Dummy for female,female,,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,, -Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481 -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,, -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-2.628,-2.628,-2.628,-2.628,-3.741,-3.741,-3.741,-3.741,-4.981,-4.981,-4.981,-4.981 -Alternative specific constant for return stops,1,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,1.272,0,0,1.272,1.871,0,1.272,1.871,1.871 -Phase1_Calibration,1,,0.420011539,0.781344394,0.733748807,0.712121201,1.545847839,1.973538669,0.675841505,0.652388889,0.802044327,0.807649045,0.537484229,1.764449963,1.356930627,0.668833391,1.667362221 diff --git a/activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv deleted file mode 100755 index 89389766d..000000000 --- a/activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv +++ /dev/null @@ -1,46 +0,0 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 -High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 -Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 -Num kids between 5 and 15 (including) years old,num_age_5_15,,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08 -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26 -Number of Adults (>= 16 years old),num_adult,,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03 -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16 -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54 -Number of work tours undertaken by the person,num_work_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 -Number of university tours tours undertaken by the person,num_univ_tours,,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48 -Number of school tours tours undertaken by the person,num_school_tours,,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55 -Number of escort tours tours undertaken by the person,num_escort_tours,,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93 -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6 -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles,(distance_in_miles < 20),,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22 -dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-0.833,-0.833,-0.833,-0.833,-2.613,-2.613,-2.613,-2.613,-3.934,-3.934,-3.934,-3.934 -Alternative specific constant for return stops,1,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0,0,0,0,0.695,0,0,0.695,0.695 -Number of subtours in the tour,num_atwork_subtours,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 -Phase1_Calibration,1,,0.223196473,0.325510562,0.203854333,-0.303375802,-0.391244072,-0.093964049,-0.096568307,-0.097190886,-0.494486563,0.334444913,-0.145593481,0.40493641,-0.229067675,0.390561162,0.076806162 diff --git a/activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv b/activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv deleted file mode 100755 index e4aeff94d..000000000 --- a/activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv +++ /dev/null @@ -1,58 +0,0 @@ -coefficient_name,coefficient_name,value,constrain -coef_Services_1day,Services,-1.624,F -coef_SalesOffice_1day,SalesOffice,-0.62,F -coef_ResourceConstruct_1day,ResourceConstruct,-1.57,F -coef_TransportMat_1day,TransportMat,-14.747,F -coef_HasChildren0to5_1day,HasChildren0to5,0,F -coef_HasChildren6to12_1day,HasChildren6to12,0,F -coef_OneAdultInHH_1day,OneAdultInHH,0.177,F -coef_2plusAdultsInHH_1day,2plusAdultsInHH,0,F -coef_Female_1day,Female,0,F -coef_PartTimeWorker_1day,PartTimeWorker,0,F -coef_CollegeStudent_1day,CollegeStudent,0,F -coef_PaysToPark_1day,PaysToPark,0.457,F -coef_Income60to100k_1day,Income60to100k,0.56,F -coef_Income100to150k_1day,Income100to150k,0.644,F -coef_Income150kplus_1day,Income150kplus,0.92,F -coef_0Autos_1day,0Autos,0,F -coef_1Auto_1day,1Auto,0,F -coef_3plusAutos_1day,3plusAutos,0,F -coef_DistanceToWork_1day,DistanceToWork,0.016,F -coef_Services_23day,Services,-0.651,F -coef_SalesOffice_23day,SalesOffice,-0.738,F -coef_ResourceConstruct_23day,ResourceConstruct,0,F -coef_TransportMat_23day,TransportMat,0,F -coef_HasChildren0to5_23day,HasChildren0to5,0,F -coef_HasChildren6to12_23day,HasChildren6to12,0.517,F -coef_OneAdultInHH_23day,OneAdultInHH,0,F -coef_2plusAdultsInHH_23day,2plusAdultsInHH,0,F -coef_Female_23day,Female,0,F -coef_PartTimeWorker_23day,PartTimeWorker,0.425,F -coef_CollegeStudent_23day,CollegeStudent,0.6,F -coef_PaysToPark_23day,PaysToPark,0,F -coef_Income60to100k_23day,Income60to100k,0.389,F -coef_Income100to150k_23day,Income100to150k,0.193,F -coef_Income150kplus_23day,Income150kplus,0.765,F -coef_0Autos_23day,0Autos,0.407,F -coef_1Auto_23day,1Auto,0,F -coef_3plusAutos_23day,3plusAutos,-0.73,F -coef_DistanceToWork_23day,DistanceToWork,0,F -coef_Services_4day,Services,0,F -coef_SalesOffice_4day,SalesOffice,-0.894,F -coef_ResourceConstruct_4day,ResourceConstruct,0,F -coef_TransportMat_4day,TransportMat,0,F -coef_HasChildren0to5_4day,HasChildren0to5,-0.864,F -coef_HasChildren6to12_4day,HasChildren6to12,-0.81,F -coef_OneAdultInHH_4day,OneAdultInHH,-0.043,F -coef_2plusAdultsInHH_4day,2plusAdultsInHH,0,F -coef_Female_4day,Female,0,F -coef_PartTimeWorker_4day,PartTimeWorker,1.112,F -coef_CollegeStudent_4day,CollegeStudent,0,F -coef_PaysToPark_4day,PaysToPark,0,F -coef_Income60to100k_4day,Income60to100k,0,F -coef_Income100to150k_4day,Income100to150k,0,F -coef_Income150kplus_4day,Income150kplus,0,F -coef_0Autos_4day,0Autos,0,F -coef_1Auto_4day,1Auto,0,F -coef_3plusAutos_4day,3plusAutos,0,F -coef_DistanceToWork_4day,DistanceToWork,0,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_segments.csv b/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_segments.csv deleted file mode 100644 index ad710ded1..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_segments.csv +++ /dev/null @@ -1,24 +0,0 @@ -tour_purpose,time_period,start,end -work,EA,3,5 -work,AM,8,11 -work,MD,14,22 -work,PM,26,30 -work,EV,33,44 -#,,, -school,EA,3,5 -school,AM,8,11 -school,MD,14,22 -school,PM,26,30 -school,EV,33,44 -#,,, -univ,EA,3,5 -univ,AM,8,11 -univ,MD,14,22 -univ,PM,26,30 -univ,EV,33,44 -#,,, -,EA,3,5 -,AM,8,11 -,MD,14,22 -,PM,26,30 -,EV,33,44 diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv deleted file mode 100755 index d437d0b10..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ /dev/null @@ -1,98 +0,0 @@ -Description,Target,Expression -#,, -local,_DF_IS_TOUR,'tour_type' in df.columns -,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 -,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False -#,, - local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns -,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" -,work_tour_is_drive,_parent_tour_mode.isin(['DRIVEALONE']) -,work_tour_is_bike,_parent_tour_mode=='BIKE' -,work_tour_is_SOV,_parent_tour_mode.isin(['DRIVEALONE']) -#,, -,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False -,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False -,is_indiv,~is_joint -,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False -,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False -,is_school,(df.tour_type=='school') & (df.is_university==False) if _DF_IS_TOUR else False -#,, -#,c_cost,(0.60 * c_ivt) / df.value_of_time -#,, -,ivot,1.0/df.value_of_time -,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" -,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" -,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" -# FIXME no transit subzones so all zones short walk to transit,, -,_walk_transit_origin,True -,_walk_transit_destination,True -,walk_transit_available,_walk_transit_origin & _walk_transit_destination -,pnr_transit_available,_walk_transit_destination & (df.auto_ownership > 0) -,knr_transit_available,_walk_transit_origin & _walk_transit_destination -,origin_walk_time,shortWalk*60/walkSpeed -,destination_walk_time,shortWalk*60/walkSpeed -# RIDEHAIL,, -,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)" -,dest_density_measure,"(reindex(land_use.tot_pop, df[dest_col_name]) + reindex(land_use.tot_emp, df[dest_col_name])) / (reindex(land_use.tot_acres, df[dest_col_name]) / 640)" -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime -,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime -,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime -#,, -,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False -,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" -,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" -,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" -,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" -,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration/2, _hourly_offpeak_parking_cost * df.duration/2)" -#,, -,distance,od_skims['DIST'] -,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) -,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 -,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) -,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0) & (dot_skims['WLK_LOC_IVT']>0) -,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0) & (dot_skims['WLK_PRM_IVT']>0) -,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0) & (dot_skims['WLK_MIX_IVT']>0) -,pnr_local_available,pnr_transit_available & (odt_skims['PNR_LOC_IVT']>0) & (dot_skims['PNRE_LOC_IVT']>0) -,pnr_premium_available,pnr_transit_available & (odt_skims['PNR_PRM_IVT']>0) & (dot_skims['PNRE_PRM_IVT']>0) -,pnr_mix_available,pnr_transit_available & (odt_skims['PNR_MIX_IVT']>0) & (dot_skims['PNRE_MIX_IVT']>0) -,knr_local_available,knr_transit_available & (odt_skims['KNR_LOC_IVT']>0) & (dot_skims['KNRE_LOC_IVT']>0) -,knr_premium_available,knr_transit_available & (odt_skims['KNR_PRM_IVT']>0) & (dot_skims['KNRE_PRM_IVT']>0) -,knr_mix_available,knr_transit_available & (odt_skims['KNR_MIX_IVT']>0) & (dot_skims['KNRE_MIX_IVT']>0) -#,, -destination in central business district,destination_in_cbd,"(reindex(land_use.AreaType, df[dest_col_name]) < setting('cbd_threshold')) * 1" -#,,FIXME diagnostic -#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) -#,,fare discounts (no discount for use in logsums) -,ptype,df.ptype if _DF_IS_TOUR else 1 -,transit_pass_ownership,df.transit_pass_ownership if _DF_IS_TOUR else 0 -,transit_pass_subsidy,df.transit_pass_subsidy if _DF_IS_TOUR else 0 -,tour_type,df.tour_type if _DF_IS_TOUR else 'other' -,fare_nopass_discount_percent,"ptype.map({k: v for k, v in transit_nopass_fare_discount_percent.items()})" -,fare_pass_discount_percent,"ptype.map({k: v for k, v in transit_pass_fare_discount_percent.items()})" -,fare_subsidy_discount_percent,"np.where(tour_type=='work', transit_subsidy_fare_discount_percent_worktour, 1)" -,fare_discount_percent_ff,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), fare_nopass_discount_percent, 1)" -,fare_discount_percent_tf,"np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), fare_pass_discount_percent, 1)" -,fare_discount_percent_ft,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), fare_nopass_discount_percent * (1-fare_subsidy_discount_percent), 1)" -,fare_discount_percent_tt,"np.where((transit_pass_ownership == True ) & (transit_pass_subsidy == True), fare_pass_discount_percent * (1-fare_subsidy_discount_percent), 1)" -,fare_discount_percent,fare_discount_percent_ff * fare_discount_percent_tf * fare_discount_percent_ft * fare_discount_percent_tt \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv deleted file mode 100755 index d21e4c81c..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv +++ /dev/null @@ -1,349 +0,0 @@ -coefficient_name,value,constrain -coef_nest_root,1,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_KNRACCESS,0.5,T -coef_nest_TRANSIT_PNRACCESS,0.5,T -coef_nest_SCHOOL_BUS,0.72,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.0134,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -walk_ASC_no_auto_eatout,6.589427019,F -walk_ASC_no_auto_escort,4.880440839,F -walk_ASC_no_auto_othdiscr,4.730904319,F -walk_ASC_no_auto_othmaint,3.366533039,F -walk_ASC_no_auto_school,12.00708155,F -walk_ASC_no_auto_shopping,4.456111339,F -walk_ASC_no_auto_social,3.332401219,F -walk_ASC_no_auto_univ,3.063248695,F -walk_ASC_no_auto_work,4.43703301,F -walk_ASC_no_auto_atwork,0.421704944,F -walk_ASC_auto_deficient_eatout,2.887675213,F -walk_ASC_auto_deficient_escort,-1.356970662,F -walk_ASC_auto_deficient_othdiscr,1.862477713,F -walk_ASC_auto_deficient_othmaint,0.914116298,F -walk_ASC_auto_deficient_school,1.097961688,F -walk_ASC_auto_deficient_shopping,1.815249198,F -walk_ASC_auto_deficient_social,2.483254213,F -walk_ASC_auto_deficient_univ,2.850042543,F -walk_ASC_auto_deficient_work,1.637904594,F -walk_ASC_auto_deficient_atwork,-17.85938696,F -walk_ASC_auto_sufficient_eatout,1.896049479,F -walk_ASC_auto_sufficient_escort,-1.53483226,F -walk_ASC_auto_sufficient_othdiscr,1.607706779,F -walk_ASC_auto_sufficient_othmaint,0.07673774,F -walk_ASC_auto_sufficient_school,-1.384129543,F -walk_ASC_auto_sufficient_shopping,0.00804064,F -walk_ASC_auto_sufficient_social,2.051577779,F -walk_ASC_auto_sufficient_univ,2.131895451,F -walk_ASC_auto_sufficient_work,-0.633007823,F -walk_ASC_auto_sufficient_atwork,0.1438828,F -bike_ASC_no_auto_eatout,1.477690443,F -bike_ASC_no_auto_escort,-2.060060749,F -bike_ASC_no_auto_othdiscr,0.233196283,F -bike_ASC_no_auto_othmaint,0.195584651,F -bike_ASC_no_auto_school,0,F -bike_ASC_no_auto_shopping,-0.509693249,F -bike_ASC_no_auto_social,0.630202693,F -bike_ASC_no_auto_univ,1.297297637,F -bike_ASC_no_auto_work,2.338049977,F -bike_ASC_no_auto_atwork,18.45901812,F -bike_ASC_auto_deficient_eatout,-0.743790882,F -bike_ASC_auto_deficient_escort,-3.511021918,F -bike_ASC_auto_deficient_othdiscr,0.732851378,F -bike_ASC_auto_deficient_othmaint,-0.501558818,F -bike_ASC_auto_deficient_school,-0.465024971,F -bike_ASC_auto_deficient_shopping,0.141061422,F -bike_ASC_auto_deficient_social,1.459841118,F -bike_ASC_auto_deficient_univ,0.950303703,F -bike_ASC_auto_deficient_work,0.224440057,F -bike_ASC_auto_deficient_atwork,-3.8074083,F -bike_ASC_auto_sufficient_eatout,-0.885432566,F -bike_ASC_auto_sufficient_escort,-4.318656917,F -bike_ASC_auto_sufficient_othdiscr,-0.756545166,F -bike_ASC_auto_sufficient_othmaint,-2.063850917,F -bike_ASC_auto_sufficient_school,-2.878621083,F -bike_ASC_auto_sufficient_shopping,-1.821758817,F -bike_ASC_auto_sufficient_social,-1.053156466,F -bike_ASC_auto_sufficient_univ,-0.119499054,F -bike_ASC_auto_sufficient_work,-1.806805644,F -bike_ASC_auto_sufficient_atwork,-0.106470168,F -sr2_ASC_auto_deficient_eatout,0.413083337,F -sr2_ASC_auto_deficient_escort,0.664157519,F -sr2_ASC_auto_deficient_othdiscr,0.485000137,F -sr2_ASC_auto_deficient_othmaint,0.926310219,F -sr2_ASC_auto_deficient_school,0.100573545,F -sr2_ASC_auto_deficient_shopping,0.908255079,F -sr2_ASC_auto_deficient_social,1.680701637,F -sr2_ASC_auto_deficient_univ,0.078542755,F -sr2_ASC_auto_deficient_work,-0.304765262,F -sr2_ASC_auto_deficient_atwork,-20.27957549,F -sr2_ASC_auto_sufficient_eatout,0.899117605,F -sr2_ASC_auto_sufficient_escort,0.075825052,F -sr2_ASC_auto_sufficient_othdiscr,0.533158225,F -sr2_ASC_auto_sufficient_othmaint,0.334003882,F -sr2_ASC_auto_sufficient_school,-1.897992866,F -sr2_ASC_auto_sufficient_shopping,0.273532122,F -sr2_ASC_auto_sufficient_social,0.559914555,F -sr2_ASC_auto_sufficient_univ,-0.53703344,F -sr2_ASC_auto_sufficient_work,-0.766476766,F -sr2_ASC_auto_sufficient_atwork,-1.621794475,F -sr3p_ASC_no_auto_eatout,2.190553045,F -sr3p_ASC_no_auto_escort,0.209802522,F -sr3p_ASC_no_auto_othdiscr,2.140722265,F -sr3p_ASC_no_auto_othmaint,1.219543822,F -sr3p_ASC_no_auto_school,9.926556957,F -sr3p_ASC_no_auto_shopping,1.742939742,F -sr3p_ASC_no_auto_social,0.464863045,F -sr3p_ASC_no_auto_univ,-0.087322435,F -sr3p_ASC_no_auto_work,-1.064082,F -sr3p_ASC_no_auto_atwork,-0.940776326,F -sr3p_ASC_auto_deficient_eatout,0.140990485,F -sr3p_ASC_auto_deficient_escort,0.409243052,F -sr3p_ASC_auto_deficient_othdiscr,1.142034725,F -sr3p_ASC_auto_deficient_othmaint,-0.531961788,F -sr3p_ASC_auto_deficient_school,0.587171252,F -sr3p_ASC_auto_deficient_shopping,0.744060546,F -sr3p_ASC_auto_deficient_social,1.595662425,F -sr3p_ASC_auto_deficient_univ,0.335943236,F -sr3p_ASC_auto_deficient_work,-0.800161676,F -sr3p_ASC_auto_deficient_atwork,-20.24587122,F -sr3p_ASC_auto_sufficient_eatout,0.665638218,F -sr3p_ASC_auto_sufficient_escort,0.017050265,F -sr3p_ASC_auto_sufficient_othdiscr,0.407280668,F -sr3p_ASC_auto_sufficient_othmaint,-0.001035875,F -sr3p_ASC_auto_sufficient_school,-1.294501505,F -sr3p_ASC_auto_sufficient_shopping,-0.0031085,F -sr3p_ASC_auto_sufficient_social,0.324957478,F -sr3p_ASC_auto_sufficient_univ,-0.702604136,F -sr3p_ASC_auto_sufficient_work,-1.404899513,F -sr3p_ASC_auto_sufficient_atwork,-2.062496761,F -walk_transit_ASC_no_auto_eatout,1.388223363,F -walk_transit_ASC_no_auto_escort,-3.242571633,F -walk_transit_ASC_no_auto_othdiscr,1.038365063,F -walk_transit_ASC_no_auto_othmaint,1.538982067,F -walk_transit_ASC_no_auto_school,9.278859746,F -walk_transit_ASC_no_auto_shopping,1.081384067,F -walk_transit_ASC_no_auto_social,0.176051663,F -walk_transit_ASC_no_auto_univ,3.321119594,F -walk_transit_ASC_no_auto_work,3.061409806,F -walk_transit_ASC_no_auto_atwork,-2.75530458,F -walk_transit_ASC_auto_deficient_eatout,-0.710721656,F -walk_transit_ASC_auto_deficient_escort,-3.220750388,F -walk_transit_ASC_auto_deficient_othdiscr,0.281329984,F -walk_transit_ASC_auto_deficient_othmaint,-1.319772188,F -walk_transit_ASC_auto_deficient_school,1.023632747,F -walk_transit_ASC_auto_deficient_shopping,0.892296712,F -walk_transit_ASC_auto_deficient_social,0.302686454,F -walk_transit_ASC_auto_deficient_univ,2.231442966,F -walk_transit_ASC_auto_deficient_work,0.186141441,F -walk_transit_ASC_auto_deficient_atwork,-22.64172131,F -walk_transit_ASC_auto_sufficient_eatout,-3.537966973,F -walk_transit_ASC_auto_sufficient_escort,-6.313884394,F -walk_transit_ASC_auto_sufficient_othdiscr,-3.231644303,F -walk_transit_ASC_auto_sufficient_othmaint,-2.926154591,F -walk_transit_ASC_auto_sufficient_school,-3.421589964,F -walk_transit_ASC_auto_sufficient_shopping,-3.582717191,F -walk_transit_ASC_auto_sufficient_social,-2.770652273,F -walk_transit_ASC_auto_sufficient_univ,-0.179769816,F -walk_transit_ASC_auto_sufficient_work,-2.912845227,F -walk_transit_ASC_auto_sufficient_atwork,-6.453181068,F -joint_walk_ASC_no_auto_all,-0.039874578,F -joint_walk_ASC_auto_deficient_all,0.010741394,F -joint_walk_ASC_auto_sufficient_all,-1.975332928,F -joint_bike_ASC_no_auto_all,-2.221523576,F -joint_bike_ASC_auto_deficient_all,-2.104028545,F -joint_bike_ASC_auto_sufficient_all,-5.415064076,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,-1.057362954,F -joint_sr3p_ASC_auto_deficient_all,-1.358121204,F -joint_sr3p_ASC_auto_sufficient_all,-2.71447634,F -joint_walk_transit_ASC_no_auto_all,-2.489686956,F -joint_walk_transit_ASC_auto_deficient_all,-3.485773675,F -joint_walk_transit_ASC_auto_sufficient_all,-15.264534,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F -joint_knr_transit_ASC_auto_deficient_all,-3.534648371,F -joint_knr_transit_ASC_auto_sufficient_all,-8.843788241,F -joint_knr_transit_ASC_no_auto_all,-3.987873766,F -joint_pnr_transit_ASC_auto_deficient_all,-5.146470604,F -joint_pnr_transit_ASC_auto_sufficient_all,-8.812715084,F -joint_pnr_transit_ASC_no_auto_all,0,T -knr_transit_ASC_auto_deficient_atwork,-23.01220348,F -knr_transit_ASC_auto_deficient_eatout,-1.841465519,F -knr_transit_ASC_auto_deficient_escort,-3.212214291,F -knr_transit_ASC_auto_deficient_othdiscr,-0.849413879,F -knr_transit_ASC_auto_deficient_othmaint,-1.311236091,F -knr_transit_ASC_auto_deficient_school,2.329981301,F -knr_transit_ASC_auto_deficient_shopping,0.900832809,F -knr_transit_ASC_auto_deficient_social,-0.828057409,F -knr_transit_ASC_auto_deficient_univ,0.781396375,F -knr_transit_ASC_auto_deficient_work,-1.44770466,F -knr_transit_ASC_auto_sufficient_atwork,-4.401027,F -knr_transit_ASC_auto_sufficient_eatout,-4.103602632,F -knr_transit_ASC_auto_sufficient_escort,-6.720817468,F -knr_transit_ASC_auto_sufficient_othdiscr,-3.797279962,F -knr_transit_ASC_auto_sufficient_othmaint,-3.333087668,F -knr_transit_ASC_auto_sufficient_school,-2.273708088,F -knr_transit_ASC_auto_sufficient_shopping,-3.989650268,F -knr_transit_ASC_auto_sufficient_social,-3.336287932,F -knr_transit_ASC_auto_sufficient_univ,-1.714133165,F -knr_transit_ASC_auto_sufficient_work,-4.698155028,F -knr_transit_ASC_no_auto_atwork,-0.580585596,F -knr_transit_ASC_no_auto_eatout,0.219095046,F -knr_transit_ASC_no_auto_escort,0.32982881,F -knr_transit_ASC_no_auto_othdiscr,0.219095046,F -knr_transit_ASC_no_auto_othmaint,0.32982881,F -knr_transit_ASC_no_auto_school,0.38894965,F -knr_transit_ASC_no_auto_shopping,0.32982881,F -knr_transit_ASC_no_auto_social,0.219095046,F -knr_transit_ASC_no_auto_univ,2.400389489,F -knr_transit_ASC_no_auto_work,1.821801152,F -local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -local_ASC_school_univ,0,F -local_ASC_work,0,F -mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -mix_ASC_school_univ,0,F -mix_ASC_work,0,F -pnr_transit_ASC_auto_deficient_atwork,-998.8196,F -pnr_transit_ASC_auto_deficient_eatout,-0.430359164,F -pnr_transit_ASC_auto_deficient_escort,-1.239230126,F -pnr_transit_ASC_auto_deficient_othdiscr,-0.710234464,F -pnr_transit_ASC_auto_deficient_othmaint,-0.384955706,F -pnr_transit_ASC_auto_deficient_school,1.14553143,F -pnr_transit_ASC_auto_deficient_shopping,-0.504015206,F -pnr_transit_ASC_auto_deficient_social,0.532554236,F -pnr_transit_ASC_auto_deficient_univ,1.899678681,F -pnr_transit_ASC_auto_deficient_work,-0.99603153,F -pnr_transit_ASC_auto_sufficient_atwork,-999.21466,F -pnr_transit_ASC_auto_sufficient_eatout,-7.21057228,F -pnr_transit_ASC_auto_sufficient_escort,-6.200504711,F -pnr_transit_ASC_auto_sufficient_othdiscr,-6.61964812,F -pnr_transit_ASC_auto_sufficient_othmaint,-4.224027811,F -pnr_transit_ASC_auto_sufficient_school,-3.608979199,F -pnr_transit_ASC_auto_sufficient_shopping,-3.770973811,F -pnr_transit_ASC_auto_sufficient_social,-6.85691217,F -pnr_transit_ASC_auto_sufficient_univ,0.143230071,F -pnr_transit_ASC_auto_sufficient_work,-2.571689178,F -pnr_transit_ASC_no_auto_atwork,0,F -pnr_transit_ASC_no_auto_eatout,0,F -pnr_transit_ASC_no_auto_escort,0,F -pnr_transit_ASC_no_auto_othdiscr,0,F -pnr_transit_ASC_no_auto_othmaint,0,F -pnr_transit_ASC_no_auto_school,0,F -pnr_transit_ASC_no_auto_shopping,0,F -pnr_transit_ASC_no_auto_social,0,F -pnr_transit_ASC_no_auto_univ,0,F -pnr_transit_ASC_no_auto_work,0,F -premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -premium_ASC_school_univ,0,F -premium_ASC_work,0,F -schoolbus_ASC_auto_deficient_school,0.835683653,F -schoolbus_ASC_auto_sufficient_school,-1.200290369,F -schoolbus_ASC_no_auto_school,10.43589235,F -schoolbus_ASC_not_school,0,T -sr2_ASC_no_auto_atwork,-0.963211851,F -sr2_ASC_no_auto_eatout,1.502329299,F -sr2_ASC_no_auto_escort,1.796584262,F -sr2_ASC_no_auto_othdiscr,1.502329299,F -sr2_ASC_no_auto_othmaint,1.796584262,F -sr2_ASC_no_auto_school,8.462111263,F -sr2_ASC_no_auto_shopping,1.796584262,F -sr2_ASC_no_auto_social,1.502329299,F -sr2_ASC_no_auto_univ,0.438511398,F -sr2_ASC_no_auto_work,0.768381772,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F -joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv deleted file mode 100755 index 131bb5335..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv +++ /dev/null @@ -1,400 +0,0 @@ -coefficient_name,value,constrain -coef_escort_Mode_Choice_Logsum,1.173173034,F -coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F -coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F -coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F -coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F -coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F -coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F -coef_escort_University_student_Duration_lt_0p5_hrs,-0.426802718,F -coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F -coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.554146191,F -coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F -coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F -coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.477582648,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.204065502,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.091614107,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.039005148,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.117680977,F -coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F -coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F -coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.469240002,F -coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F -coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F -coef_escort_Departure_Constant_07_30_AM_08_00_AM,0,T -coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.214617667,F -coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F -coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F -coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F -coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F -coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F -coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F -coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F -coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F -coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F -coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F -coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0,T -coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.683756801,F -coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F -coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F -coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F -coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.396838392,F -coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F -coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F -coef_escort_Duration_Constant_0_hrs,-0.173757322,F -coef_escort_Duration_Constant_0p5_hrs,0,T -coef_escort_Duration_Constant_1_hrs,-0.431287743,F -coef_escort_Duration_Constant_1p5hrs,-0.700473959,F -coef_escort_Duration_Constant_2_hrs,-1.071871358,F -coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F -coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F -coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F -coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F -coef_escort_Calibration_Constant_Departure_eq_10,0.215386864,F -coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F -coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F -coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,-0.190727375,F -coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,-0.029551313,F -coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F -coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F -coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F -coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F -coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F -coef_shop_Driving_age_student_Duration_gt_1p5_hr,0.122149101,F -coef_shop_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F -coef_shop_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F -coef_shop_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F -coef_shop_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F -coef_shop_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F -coef_shop_Retired_Duration_lt_1p5_hrs,-0.264728908,F -coef_shop_Retired_Duration_gt_1p5_hr,-0.042981757,F -coef_shop_University_Student_Duration_gt_1p5_hr,0.045926523,F -coef_shop_Female_Duration_lt_1p5_hrs,-0.417208254,F -coef_shop_Female_Duration_gt_1p5_hr,0.045801918,F -coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F -coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F -coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F -coef_shop_Distance_Duration_lt_1p5_hrs,-0.214802537,F -coef_shop_Distance_Duration_gt_1p5_hr,0.007991656,F -coef_shop_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F -coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F -coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.959875456,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,1.112594898,F -coef_shop_Departure_Constant_Before_09_00_AM,-0.446394064,F -coef_shop_Departure_Constant_09_00_AM_09_30_AM,-0.021669265,F -coef_shop_Departure_Constant_09_30_AM_10_00_AM,-0.282978638,F -coef_shop_Departure_Constant_10_00_AM_10_30_AM,0,T -coef_shop_Departure_Constant_10_30_AM_11_00_AM,-0.309421311,F -coef_shop_Departure_Constant_After_11_00_AM,-0.541073357,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,-0.072013428,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.000653398,F -coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.18376635,F -coef_shop_Arrival_Constant_Before_12_30_PM,-0.716195343,F -coef_shop_Arrival_Constant_12_30_PM_03_00_PM,-0.502714001,F -coef_shop_Arrival_Constant_03_00_PM_03_30_PM,-0.167868872,F -coef_shop_Arrival_Constant_03_30_PM_04_00_PM,-0.156786941,F -coef_shop_Arrival_Constant_04_00_PM_04_30_PM,0,T -coef_shop_Arrival_Constant_04_30_PM_05_00_PM,-0.057314044,F -coef_shop_Arrival_Constant_05_00_PM_05_30_PM,-0.580040851,F -coef_shop_Arrival_Constant_05_30_PM_07_00_PM,-0.32239566,F -coef_shop_Arrival_Constant_07_00_PM_09_30_PM,-0.347828147,F -coef_shop_Arrival_Constant_After_09_30_PM,-1.123574723,F -coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.499770654,F -coef_shop_Duration_Constant_0_hrs,-0.131743185,F -coef_shop_Duration_Constant_0p5_hrs,0.888857137,F -coef_shop_Duration_Constant_1_hrs,0,T -coef_shop_Duration_Constant_1p5hrs,-0.333413031,F -coef_shop_Duration_Constant_2_hrs,-0.850897912,F -coef_shop_Duration_Constant_Longer_than_2_hrs,-1.203783479,F -coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.293581223,F -coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,-0.215759138,F -coef_shop_Calibration_Constant_Duration_eq_1,-0.138450424,F -coef_shop_Calibration_Constant_Duration_eq_2,-0.092704403,F -coef_shop_Calibration_Constant_Duration_eq_3,-0.087738073,F -coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.139150288,F -coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,-0.065786345,F -coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F -coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F -coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F -coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F -coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F -coef_maint_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F -coef_maint_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F -coef_maint_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F -coef_maint_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F -coef_maint_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F -coef_maint_Retired_Duration_lt_1p5_hrs,-0.264728908,F -coef_maint_Retired_Duration_gt_1p5_hr,-0.042981757,F -coef_maint_University_Student_Duration_gt_1p5_hr,0.045926523,F -coef_maint_Female_Duration_lt_1p5_hrs,-0.417208254,F -coef_maint_Female_Duration_gt_1p5_hr,0.045801918,F -coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F -coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F -coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0,T -coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F -coef_maint_Distance_Duration_lt_1p5_hrs,-0.214802537,F -coef_maint_Distance_Duration_gt_1p5_hr,0.007991656,F -coef_maint_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F -coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F -coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,-0.864112609,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.504598473,F -coef_maint_Departure_Constant_Before_08_00_AM,-0.383711788,F -coef_maint_Departure_Constant_08_00_AM_08_30_AM,-0.076771517,F -coef_maint_Departure_Constant_08_30_AM_09_00_AM,-0.169259979,F -coef_maint_Departure_Constant_09_00_AM_09_30_AM,-0.051785379,F -coef_maint_Departure_Constant_09_30_AM_10_00_AM,-0.214942451,F -coef_maint_Departure_Constant_10_00_AM_10_30_AM,0,T -coef_maint_Departure_Constant_10_30_AM_11_00_AM,-0.427568963,F -coef_maint_Departure_Constant_After_11_00_AM,-0.520863411,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.042879095,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.003157293,F -coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,-0.226803619,F -coef_maint_Arrival_Constant_Before_10_30_AM,-0.223212258,F -coef_maint_Arrival_Constant_10_30_AM_11_00_AM,0,T -coef_maint_Arrival_Constant_11_00_AM_11_30_AM,-0.128382637,F -coef_maint_Arrival_Constant_11_30_AM_01_30_PM,0.167977332,F -coef_maint_Arrival_Constant_01_30_PM_02_30_PM,-0.149495878,F -coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.087679934,F -coef_maint_Arrival_Constant_04_00_PM_04_30_PM,0.121707557,F -coef_maint_Arrival_Constant_After_04_30_PM,0.106745013,F -coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.232610927,F -coef_maint_Duration_Constant_0_hrs,-0.483549396,F -coef_maint_Duration_Constant_0p5_hrs,0,T -coef_maint_Duration_Constant_Longer_than_0p5_hrs,-1.450618319,F -coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,-0.275082922,F -coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.208434683,F -coef_maint_Calibration_Constant_Duration_eq_1,-0.124602605,F -coef_maint_Calibration_Constant_Duration_eq_2,-0.103637715,F -coef_maint_Calibration_Constant_Duration_eq_3,-0.225442145,F -coef_maint_Calibration_Constant_Duration_eq_4,-0.145273012,F -coef_maint_Calibration_Constant_Duration_eq_5,-0.019241539,F -coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.134981987,F -coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.017860742,F -coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,1.002485807,F -coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,0.499822018,F -coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.259409942,F -coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.785123348,F -coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.626003709,F -coef_eatout_University_student_Departure_after_7_00_pm_Linear,0.293827759,F -coef_eatout_Female_Duration_lt_1_hrs,-0.399414247,F -coef_eatout_Female_Duration_gt_1_hrs,0.064593482,F -coef_eatout_Time_Pressure_Departure_before_6_30_pm,0.083673557,F -coef_eatout_Time_Pressure_Duration_lt_1_hrs,1.69632588,F -coef_eatout_Departure_Constant_07_30_AM_09_00_AM,1.222417262,F -coef_eatout_Departure_Constant_10_30_AM_11_00_AM,0.519559134,F -coef_eatout_Departure_Constant_11_00_AM_11_30_AM,1.191543552,F -coef_eatout_Departure_Constant_11_30_AM_12_00_PM,1.66870995,F -#coef_eatout_Departure_Constant_12_00_PM_12_30_PM,1.164106986,F -coef_eatout_Departure_Constant_12_30_PM_01_00_PM,1.057346496,F -coef_eatout_Departure_Constant_01_00_PM_01_30_PM,0.728959087,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_30_pm_Linear,-0.477439748,F -coef_eatout_Departure_Constant_Before_06_00_PM,-1.21554671,F -#coef_eatout_Departure_Constant_06_00_PM_06_30_PM,-0.425984037,F -#coef_eatout_Departure_Constant_06_30_PM_07_00_PM,0,T -#coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.227800647,F -#coef_eatout_Departure_Constant_After_07_30_PM,-0.293904097,F -#coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.55440861,F -coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.531539506,F -coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.673838195,F -coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.422292261,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.033290717,F -coef_eatout_Departure_Constant_Before_05_30_PM,-0.561079452,F -coef_eatout_Departure_Constant_05_30_PM_06_00_PM,-0.178719161,F -coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0,T -coef_eatout_Departure_Constant_06_30_PM_07_00_PM,-0.282095841,F -coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.299748613,F -coef_eatout_Departure_Constant_After_07_30_PM,-0.845300559,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.667843486,F -coef_eatout_Arrival_Constant_9_30_AM_to_11_00_AM,0.486337344,F -#coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.629299404,F -#coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,0.938528731,F -#coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.584420106,F -#coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.842550215,F -coef_eatout_Arrival_Constant_02_30_PM_to_03_00_PM,0.298486505,F -#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.125034982,F -coef_eatout_Arrival_Constant_Before_6_30_PM,-0.029062996,F -coef_eatout_Arrival_Constant_6_30_PM_to_7_00_PM,-0.509075598,F -# coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.124885931,F -# coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.605455664,F -# coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -# coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.657382835,F -coef_eatout_Arrival_Constant_After_9_00_PM,-0.813629712,F -#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204722406,F -coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,2.002032369,F -coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,2.115334472,F -coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,1.647879687,F -coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,1.525310078,F -coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,-0.152980854,F -coef_eatout_Arrival_Constant_Before_7_00_PM,-0.41165142,F -coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.384557379,F -coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.044050359,F -coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.239939049,F -coef_eatout_Arrival_Constant_After_09_00_PM,-0.248639696,F -coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204771082,F -#coef_eatout_Duration_Constant_0_hours,-11.72660422,F -#coef_eatout_Duration_Constant_0p5_hous,-5.08873115,F -#coef_eatout_Duration_Constant_1_hour,-0.125521065,F -#coef_eatout_Duration_Constant_1p5_hours,0,T -coef_eatout_Duration_Constant_2_hours,-0.124807752,F -coef_eatout_Duration_Constant_2p5_hours_or_more,-0.156019835,F -coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.3357414,F -coef_eatout_Duration_Constant_0_hours,-4.268996522,F -coef_eatout_Duration_Constant_0p5_hous,-1.323297693,F -coef_eatout_Duration_Constant_1_hour,0,T -coef_eatout_Duration_Constant_1p5_hours,-0.195669185,F -coef_eatout_Duration_Constant_2_hours_or_more,-0.523723192,F -coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,-0.649331488,F -coef_eatout_Calibration_Constant_Duration_eq_1,-0.333697861,F -coef_eatout_Calibration_Constant_Duration_eq_2,-0.245716,F -coef_eatout_Calibration_Constant_Duration_eq_3,0.052708833,F -coef_eatout_Calibration_Constant_Duration_eq_4,0.041571499,F -coef_eatout_Calibration_Constant_Departure_eq_1,-10,F -coef_eatout_Calibration_Constant_Departure_eq_2,-10,F -coef_eatout_Calibration_Constant_Departure_eq_3,-10,F -coef_eatout_Calibration_Constant_Departure_eq_17,0.706568704,F -coef_eatout_Calibration_Constant_Departure_eq_18,0.634353544,F -coef_eatout_Calibration_Constant_Departure_eq_19,0.584387268,F -coef_eatout_Calibration_Constant_Departure_eq_20,0.469777884,F -coef_eatout_Calibration_Constant_Departure_eq_21,0.39548931,F -coef_social_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F -coef_social_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F -coef_social_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F -coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F -coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F -coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F -coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F -coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F -coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F -coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F -coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F -coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F -coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F -coef_social_Time_Pressure_Duration_lt_1_hr,-0.229264474,F -coef_social_Time_Pressure_Duration_gt_1_hr,0.219325112,F -coef_social_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.529943196,F -coef_social_Departure_Constant_Before_09_00_AM,-0.198438086,F -coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.137620628,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.142078961,F -coef_social_Departure_Constant_Before_05_30_PM,-0.390965052,F -coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.453580491,F -coef_social_Departure_Constant_06_00_PM_06_30_PM,0,T -coef_social_Departure_Constant_06_30_PM_07_00_PM,-0.088537991,F -coef_social_Departure_Constant_07_00_PM_07_30_PM,0.052983115,F -coef_social_Departure_Constant_After_07_30_PM,-0.649629162,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.09574499,F -coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.37674882,F -coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.583355461,F -coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.727855233,F -coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.249551955,F -coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.053771388,F -coef_social_Arrival_Constant_Before_8_30_PM,0.308763611,F -coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,-0.208797698,F -coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,-0.336319511,F -coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0,T -coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,-0.055707591,F -coef_social_Arrival_Constant_After_10_30_PM,-0.612356296,F -coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-0.348479901,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,0.619073863,F -coef_social_Duration_Constant_Less_than_2_hours,-0.584024011,F -coef_social_Duration_Constant_2_hours,-0.271552271,F -coef_social_Duration_Constant_2p5_hours,0,T -coef_social_Duration_Constant_3_hours_or_more,0.042083404,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,-0.13049452,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_2p5_hrs_Linear,0.614440191,F -coef_social_Duration_Constant_Less_than_3_hrs,0.353666691,F -coef_social_Duration_Constant_3_hours,0,T -coef_social_Duration_Constant_3p5_hours,-0.691218836,F -coef_social_Duration_Constant_4_hours_or_more,-1.344375328,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_4p5_hrs_Linear,-0.786970714,F -coef_social_Calibration_Constant_Duration_eq_1,-1.346772472,F -coef_social_Calibration_Constant_Duration_eq_2,0.377121689,F -coef_social_Calibration_Constant_Duration_eq_3,0.179818928,F -coef_social_Calibration_Constant_Duration_eq_4,-0.283418619,F -coef_social_Calibration_Constant_Duration_eq_5,-0.103541313,F -coef_social_Calibration_Constant_Duration_eq_6,-0.03704707,F -coef_social_Calibration_Constant_Duration_eq_7,-0.062437167,F -coef_social_Calibration_Constant_Duration_eq_8,0.047640282,F -coef_social_Calibration_Constant_Duration_eq_9,0.284369793,F -coef_disc_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F -coef_disc_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F -coef_disc_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F -coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F -coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F -coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F -coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F -coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F -coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F -coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F -coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F -coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F -coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F -coef_disc_Time_Pressure_Duration_lt_1_hr,-0.229264474,F -coef_disc_Time_Pressure_Duration_gt_1_hr,0.219325112,F -coef_disc_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.742176805,F -coef_disc_Departure_Constant_Before_7_30_AM_,-1.323901585,F -coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,-0.695441631,F -coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,-0.269903336,F -coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,-0.093709211,F -coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,0.265634082,F -coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,0.287521134,F -coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,0.396547817,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-0.245885745,F -coef_disc_Departure_Constant_Before_05_00_PM,-1.344482349,F -coef_disc_Departure_Constant_05_00_PM_05_30_PM,-0.622632748,F -coef_disc_Departure_Constant_05_30_PM_06_00_PM,-0.456718676,F -coef_disc_Departure_Constant_06_00_PM_06_30_PM,-0.206896106,F -coef_disc_Departure_Constant_06_30_PM_07_00_PM,0,T -coef_disc_Departure_Constant_After_07_00_PM,-0.46439343,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,-0.291998986,F -coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.148649188,F -coef_disc_Arrival_Constant_Before_6_30_PM,0.668775963,F -coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,-0.053520826,F -coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,0.099726391,F -coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,0.063414092,F -coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,-0.18610847,F -coef_disc_Arrival_Constant_After_9_00_PM,-0.423207857,F -coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.525545923,F -#coef_disc_Duration_Constant_0_hours,-0.944257762,F -#coef_disc_Duration_Constant_0p5_hous,-0.117695955,F -#coef_disc_Duration_Constant_1_hour,0.438403665,F -#coef_disc_Duration_Constant_1p5_hours,-0.002500048,F -#coef_disc_Duration_Constant_2_hours,0,T -coef_disc_Duration_Constant_2p5_hours_or_more,0.239192556,F -#coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.108260689,F -coef_disc_Duration_Constant_0_hours,0.436988282,F -coef_disc_Duration_Constant_0p5_hous,1.35967577,F -coef_disc_Duration_Constant_1_hour,1.692672999,F -coef_disc_Duration_Constant_1p5_hours,1.118932964,F -coef_disc_Duration_Constant_2_hours,0.771255733,F -coef_disc_Duration_Constant_2p5_hours,0,T -coef_disc_Duration_Constant_3_hours_or_more,-0.631242175,F -coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.700935645,F -coef_disc_Calibration_Constant_Duration_eq_4,-0.132674257,F -coef_disc_Calibration_Constant_Duration_eq_5,-0.013371871,F -coef_disc_Calibration_Constant_Departure_eq_29,0.232927977,F -coef_disc_Calibration_Constant_Departure_eq_30,0.306104612,F -coef_disc_Calibration_Constant_Departure_eq_31,0.285520678,F -coef_disc_Calibration_Constant_Departure_eq_32,0.115886631,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv deleted file mode 100755 index f345af80a..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv +++ /dev/null @@ -1,348 +0,0 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@((df.tour_type == 'social') & (df.start<12)) * ((np.minimum(12-df.start,48)*(df.start<12)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv deleted file mode 100755 index 42cb13d76..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv +++ /dev/null @@ -1,337 +0,0 @@ -coefficient_name,value,constrain -coef_escort_mode_choice_logsum,1.173173034,F -coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.335017673,F -coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.005298165,F -coef_escort_fulltime_worker_departure_after_8_am_linear,-0.037980109,F -coef_escort_fulltime_worker_departure_after_3_am_linear,0.163254125,F -coef_escort_fulltime_worker_duration_less_than_30_minutes,-0.275077482,F -coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.051530545,F -coef_escort_university_student_duration_less_than_30_minutes,-0.426802718,F -coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.240582361,F -coef_escort_driving_age_student_duration_less_than_30_minutes,-0.554146191,F -coef_escort_driving_age_student_duration_greater_than_30_minutes,0.299387708,F -coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.195482563,F -coef_escort_med_high_income_duration_greater_than_30_minutes,-0.029281467,F -coef_escort_households_with_no_kids_departure_before_7_30_am,0.589083327,F -coef_escort_households_with_no_kids_departure_after_8_00_am,0.086690827,F -coef_escort_households_with_no_kids_departure_before_2_30_pm,0.477582648,F -coef_escort_households_with_no_kids_departure_after_3_00_pm,-0.204065502,F -coef_escort_households_with_no_kids_arrival_before_8_am,-0.360039254,F -coef_escort_households_with_no_kids_arrival_after_8_30_am,0.091614107,F -coef_escort_households_with_no_kids_arrival_before_3_pm,0.432854268,F -coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.131037275,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.109700265,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.224568648,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-0.357416434,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.629285298,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.039005148,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,-0.06556611,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.117680977,F -coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,-0.057322708,F -coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.062899692,F -coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,-0.048533895,F -coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.469240002,F -coef_escort_departure_constant_before_7_am,-2.070292862,F -coef_escort_departure_constant_7_am_to_7_30_am,-0.642734296,F -coef_escort_departure_constant_7_30_am_to_8_am,0,T -coef_escort_departure_constant_8_am_to_8_30_am,-0.214617667,F -coef_escort_departure_constant_8_30_am_to_9_am,-0.147266606,F -coef_escort_departure_constant_after_9_am,-1.356686422,F -coef_escort_departure_constant_1_30_pm_to_2_pm,0.368092381,F -coef_escort_departure_constant_2_pm_to_2_30_pm,1.166803383,F -coef_escort_departure_constant_2_30_pm_to_3_pm,1.28466083,F -coef_escort_departure_constant_3_pm_to_3_30_pm,0.581891245,F -coef_escort_departure_constant_after_3_30_pm,0.834510243,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.175257649,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,-0.019161202,F -coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.44978138,F -coef_escort_arrival_constant_before_7_am,0.549584585,F -coef_escort_arrival_constant_7_am_to_7_30_am,0.488181278,F -coef_escort_arrival_constant_7_30_am_to_8_am,0.236447651,F -coef_escort_arrival_constant_8_am_to_8_30_am,0,T -coef_escort_arrival_constant_8_30_am_to_9_am,-0.683756801,F -coef_escort_arrival_constant_after_9_am,-1.428888485,F -coef_escort_arrival_constant_2_30_pm_to_3_pm,1.311480662,F -coef_escort_arrival_constant_3_pm_to_3_30_pm,1.316883154,F -coef_escort_arrival_constant_3_30_pm_to_4_pm,1.396838392,F -coef_escort_arrival_constant_4_pm_to_4_30_pm,1.03146139,F -coef_escort_arrival_constant_after_4_30_pm,0.907344583,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.148408887,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.389082896,F -coef_escort_duration_constant_0_hour,-0.173757322,F -coef_escort_duration_constant_30_minutes,0,T -coef_escort_duration_constant_1_hour,-0.431287743,F -coef_escort_duration_constant_1_hour_30_minutes,-0.700473959,F -coef_escort_duration_constant_2_hours,-1.071871358,F -coef_escort_duration_constant_longer_than_2_hours,-1.691098421,F -coef_escort_calibration_constant_duration_1,-0.047200214,F -coef_escort_calibration_constant_duration_2,0.035611332,F -coef_escort_calibration_constant_departure_9,0.106814756,F -coef_escort_calibration_constant_departure_10,0.215386864,F -coef_escort_calibration_constant_departure_23,-0.255087318,F -coef_escort_calibration_constant_departure_24,-0.296870428,F -coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_shopping_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_shopping_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_shopping_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_shopping_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_shopping_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_shopping_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_shopping_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_shopping_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_shopping_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.959875456,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,1.112594898,F -coef_shopping_departure_constant_before_9_am,-0.446394064,F -coef_shopping_departure_constant_9_am_to_9_30_am,-0.021669265,F -coef_shopping_departure_constant_9_30_am_to_10_am,-0.282978638,F -coef_shopping_departure_constant_10_am_to_10_30_am,0,T -coef_shopping_departure_constant_10_30_am_to_11_00_am,-0.309421311,F -coef_shopping_departure_constant_after_11_am,-0.541073357,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,-0.072013428,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.000653398,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,-0.18376635,F -coef_shopping_arrival_constant_before_12_30_pm,-0.716195343,F -coef_shopping_arrival_constant_12_30_pm_to_3_pm,-0.502714001,F -coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.167868872,F -coef_shopping_arrival_constant_3_30_pm_to_4_pm,-0.156786941,F -coef_shopping_arrival_constant_4_pm_to_4_30_pm,0,T -coef_shopping_arrival_constant_4_30_pm_to_5_pm,-0.057314044,F -coef_shopping_arrival_constant_5_pm_to_5_30_pm,-0.580040851,F -coef_shopping_arrival_constant_5_30_pm_to_7_pm,-0.32239566,F -coef_shopping_arrival_constant_7_pm_to_9_30_pm,-0.347828147,F -coef_shopping_arrival_constant_after_9_30_pm,-1.123574723,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.499770654,F -coef_shopping_duration_constant_0_hr,-0.131743185,F -coef_shopping_duration_constant_30_minutes,0.888857137,F -coef_shopping_duration_constant_1_hr,0,T -coef_shopping_duration_constant_1_hour_30_minutes,-0.333413031,F -coef_shopping_duration_constant_2_hrs,-0.850897912,F -coef_shopping_duration_constant_longer_than_2_hrs,-1.203783479,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.293581223,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,-0.215759138,F -coef_shopping_calibration_constant_duration_1,-0.138450424,F -coef_shopping_calibration_constant_duration_2,-0.092704403,F -coef_shopping_calibration_constant_duration_3,-0.087738073,F -coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_maintenance_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_maintenance_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_maintenance_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_maintenance_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,0,T -coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_maintenance_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_maintenance_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.864112609,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.504598473,F -coef_maintenance_departure_constant_before_8_am,-0.383711788,F -coef_maintenance_departure_constant_8_am_to_8_30_am,-0.076771517,F -coef_maintenance_departure_constant_8_30_am_to_9_00_am,-0.169259979,F -coef_maintenance_departure_constant_9_am_to_9_30_am,-0.051785379,F -coef_maintenance_departure_constant_9_30_am_to_10_am,-0.214942451,F -coef_maintenance_departure_constant_10_am_to_10_30_am,0,T -coef_maintenance_departure_constant_10_30_am_to_11_am,-0.427568963,F -coef_maintenance_departure_constant_after_11_am,-0.520863411,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.042879095,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.003157293,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.226803619,F -coef_maintenance_arrival_constant_before_10_30_am,-0.223212258,F -coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T -coef_maintenance_arrival_constant_11_am_to_11_30_am,-0.128382637,F -coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.167977332,F -coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,-0.149495878,F -coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.087679934,F -coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.121707557,F -coef_maintenance_arrival_constant_after_4_30_pm,0.106745013,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.232610927,F -coef_maintenance_duration_constant_0_hr,-0.483549396,F -coef_maintenance_duration_constant_30_minutes,0,T -coef_maintenance_duration_constant_longer_than_30_minutes,-1.450618319,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.275082922,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,0.208434683,F -coef_maintenance_calibration_constant_duration_1,-0.124602605,F -coef_maintenance_calibration_constant_duration_2,-0.103637715,F -coef_maintenance_calibration_constant_duration_3,-0.225442145,F -coef_maintenance_calibration_constant_duration_4,-0.145273012,F -coef_maintenance_calibration_constant_duration_5,-0.019241539,F -coef_eatout_distance_to_destination_duration_less_than_1_hr,-0.134981987,F -coef_eatout_distance_to_destination_duration_greater_than_1_hr,0.017860742,F -coef_eatout_low_income_duration_less_than_1_hr,1.002485807,F -coef_eatout_medium_income_duration_less_than_1_hr,0.499822018,F -coef_eatout_zeroauto_HH_duration_greater_than_1_hr,0.259409942,F -coef_eatout_university_student_departure_after_7_pm_linear,0.293827759,F -coef_eatout_female_duration_less_than_1_hr,-0.399414247,F -coef_eatout_female_duration_greater_than_1_hr,0.064593482,F -coef_eatout_time_pressure_departure_before_6_30_pm,0.083673557,F -coef_eatout_time_pressure_duration_less_than_1_hr,1.69632588,F -coef_eatout_departure_constant_7_30_am_to_9_am,1.222417262,F -coef_eatout_departure_constant_10_30_am_to_11_am,0.519559134,F -coef_eatout_departure_constant_11_am_to_11_30_am,1.191543552,F -coef_eatout_departure_constant_11_30_am_to_12_pm,1.66870995,F -coef_eatout_departure_constant_12_pm_to_12_30_pm,1.164106986,F -coef_eatout_departure_constant_12_30_pm_to_1_pm,1.057346496,F -coef_eatout_departure_constant_1_pm_to_1_30_pm,0.728959087,F -coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.477439748,F -coef_eatout_departure_constant_before_6_pm,-1.21554671,F -coef_eatout_departure_constant_6_pm_to_6_30_pm,-0.425984037,F -coef_eatout_departure_constant_6_30_pm_to_7_pm,0,T -coef_eatout_departure_constant_7_pm_to_7_30_pm,-0.227800647,F -coef_eatout_departure_constant_after_7_30_pm,-0.293904097,F -coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.55440861,F -coef_eatout_arrival_constant_9_30_am_to_11_am,0.486337344,F -coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.629299404,F -coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.938528731,F -coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.584420106,F -coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.842550215,F -coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.298486505,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.125034982,F -coef_eatout_arrival_constant_before_6_30_pm,-0.029062996,F -coef_eatout_arrival_constant_6_30_pm_to_7_pm,-0.509075598,F -coef_eatout_arrival_constant_7_pm_to_7_30_pm,-0.124885931,F -coef_eatout_arrival_constant_7_30_pm_to_8_pm,-0.605455664,F -coef_eatout_arrival_constant_8_pm_to_8_30_pm,0,T -coef_eatout_arrival_constant_8_30_pm_to_9_pm,-0.657382835,F -coef_eatout_arrival_constant_after_9_pm,-0.813629712,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.204722406,F -coef_eatout_duration_constant_0_hour,-11.72660422,F -coef_eatout_duration_constant_30_minutes,-5.08873115,F -coef_eatout_duration_constant_1_hour,-0.125521065,F -coef_eatout_duration_constant_1_hour_30_minutes,0,T -coef_eatout_duration_constant_2_hours,-0.124807752,F -coef_eatout_duration_constant_2_hour_30_minutes_or_more,-0.156019835,F -coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.3357414,F -coef_eatout_calibration_constant_duration_1,-0.333697861,F -coef_eatout_calibration_constant_duration_2,-0.245716,F -coef_eatout_calibration_constant_duration_3,0.052708833,F -coef_eatout_calibration_constant_duration_4,0.041571499,F -coef_eatout_calibration_constant_departure_1,-10,F -coef_eatout_calibration_constant_departure_2,-10,F -coef_eatout_calibration_constant_departure_3,-10,F -coef_eatout_calibration_constant_departure_17,0.706568704,F -coef_eatout_calibration_constant_departure_18,0.634353544,F -coef_eatout_calibration_constant_departure_19,0.584387268,F -coef_eatout_calibration_constant_departure_20,0.469777884,F -coef_eatout_calibration_constant_departure_21,0.39548931,F -coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,-0.312282762,F -coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_social_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_social_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_social_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_social_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.529943196,F -coef_social_departure_constant_before_9_am,-0.198438086,F -coef_social_departure_constant_9_am_to_9_30_am,0.137620628,F -coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,-0.142078961,F -coef_social_departure_constant_before_5_30_pm,-0.390965052,F -coef_social_departure_constant_5_30_pm_to_6_pm,-0.453580491,F -coef_social_departure_constant_6_pm_to_6_30_pm,0,T -coef_social_departure_constant_6_30_pm_to_7_pm,-0.088537991,F -coef_social_departure_constant_7_pm_to_7_30_pm,0.052983115,F -coef_social_departure_constant_after_7_30_pm,-0.649629162,F -coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.09574499,F -coef_social_arrival_constant_3_pm_to_3_30_pm,0.37674882,F -coef_social_arrival_constant_3_30_pm_to_4_pm,0.583355461,F -coef_social_arrival_constant_4_pm_to_4_30_pm,0.727855233,F -coef_social_arrival_constant_5_pm_to_6_pm,0.249551955,F -coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,0.053771388,F -coef_social_arrival_constant_before_8_30_pm,0.308763611,F -coef_social_arrival_constant_8_30_pm_to_9_pm,-0.208797698,F -coef_social_arrival_constant_9_pm_to_9_30_pm,-0.336319511,F -coef_social_arrival_constant_9_30_pm_to_10_pm,0,T -coef_social_arrival_constant_10_pm_to_10_30_pm,-0.055707591,F -coef_social_arrival_constant_after_10_30_pm,-0.612356296,F -coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.348479901,F -coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.614440191,F -coef_social_duration_constant_less_than_3_hours,0.353666691,F -coef_social_duration_constant_3_hours,0,T -coef_social_duration_constant_3_hrs_30_minutes,-0.691218836,F -coef_social_duration_constant_4_hours_or_more,-1.344375328,F -coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-0.786970714,F -coef_social_calibration_constant_duration_1,-1.346772472,F -coef_social_calibration_constant_duration_2,0.377121689,F -coef_social_calibration_constant_duration_3,0.179818928,F -coef_social_calibration_constant_duration_4,-0.283418619,F -coef_social_calibration_constant_duration_5,-0.103541313,F -coef_social_calibration_constant_duration_6,-0.03704707,F -coef_social_calibration_constant_duration_7,-0.062437167,F -coef_social_calibration_constant_duration_8,0.047640282,F -coef_social_calibration_constant_duration_9,0.284369793,F -coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,-0.312282762,F -coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_discretionary_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_discretionary_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_discretionary_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_discretionary_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.742176805,F -coef_discretionary_departure_constant_before_7_30_am,-1.323901585,F -coef_discretionary_departure_constant_7_30_am_to_8_am,-0.695441631,F -coef_discretionary_departure_constant_8_am_to_8_30_am,-0.269903336,F -coef_discretionary_departure_constant_8_30_am_to_9_am,-0.093709211,F -coef_discretionary_departure_constant_9_am_to_9_30_am,0.265634082,F -coef_discretionary_departure_constant_9_30_am_to_10_am,0.287521134,F -coef_discretionary_departure_constant_10_am_to_10_30_am,0.396547817,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,-0.245885745,F -coef_discretionary_departure_constant_before_5_pm,-1.344482349,F -coef_discretionary_departure_constant_5_pm_to_5_30_pm,-0.622632748,F -coef_discretionary_departure_constant_5_30_pm_to_6_pm,-0.456718676,F -coef_discretionary_departure_constant_6_pm_to_6_30_pm,-0.206896106,F -coef_discretionary_departure_constant_6_30_pm_to_7_pm,0,T -coef_discretionary_departure_constant_after_7_pm,-0.46439343,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-0.291998986,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.148649188,F -coef_discretionary_arrival_constant_before_6_30_pm,0.668775963,F -coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-0.053520826,F -coef_discretionary_arrival_constant_7_pm_to_7_30_pm,0.099726391,F -coef_discretionary_arrival_constant_7_30_pm_to_8_pm,0.063414092,F -coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0,T -coef_discretionary_arrival_constant_8_30_pm_to_9_pm,-0.18610847,F -coef_discretionary_arrival_constant_after_9_pm,-0.423207857,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.525545923,F -coef_discretionary_duration_constant_0_hour,0.436988282,F -coef_discretionary_duration_constant_30_minutes,1.35967577,F -coef_discretionary_duration_constant_1_hour,1.692672999,F -coef_discretionary_duration_constant_1_hr_30_minutes,1.118932964,F -coef_discretionary_duration_constant_2_hours,0.771255733,F -coef_discretionary_duration_constant_2_hr_30_minutes,0,T -coef_discretionary_duration_constant_3_hours_or_more,-0.631242175,F -coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.700935645,F -coef_discretionary_calibration_constant_duration_4,-0.132674257,F -coef_discretionary_calibration_constant_duration_5,-0.013371871,F -coef_discretionary_calibration_constant_departure_29,0.232927977,F -coef_discretionary_calibration_constant_departure_30,0.306104612,F -coef_discretionary_calibration_constant_departure_31,0.285520678,F -coef_discretionary_calibration_constant_departure_32,0.115886631,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv deleted file mode 100644 index d94961976..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv +++ /dev/null @@ -1,59 +0,0 @@ -coefficient_name,value,constrain -coef_eatout_distance_to_destination_duration_less_than_1_hr,-0.134981987,F -coef_eatout_distance_to_destination_duration_greater_than_1_hr,0.017860742,F -coef_eatout_low_income_duration_less_than_1_hr,1.002485807,F -coef_eatout_medium_income_duration_less_than_1_hr,0.499822018,F -coef_eatout_zeroauto_HH_duration_greater_than_1_hr,0.259409942,F -coef_eatout_university_student_departure_after_7_pm_linear,0.293827759,F -coef_eatout_female_duration_less_than_1_hr,-0.399414247,F -coef_eatout_female_duration_greater_than_1_hr,0.064593482,F -coef_eatout_time_pressure_departure_before_6_30_pm,0.083673557,F -coef_eatout_time_pressure_duration_less_than_1_hr,1.69632588,F -coef_eatout_departure_constant_7_30_am_to_9_am,1.222417262,F -coef_eatout_departure_constant_10_30_am_to_11_am,0.519559134,F -coef_eatout_departure_constant_11_am_to_11_30_am,1.191543552,F -coef_eatout_departure_constant_11_30_am_to_12_pm,1.66870995,F -coef_eatout_departure_constant_12_pm_to_12_30_pm,1.164106986,F -coef_eatout_departure_constant_12_30_pm_to_1_pm,1.057346496,F -coef_eatout_departure_constant_1_pm_to_1_30_pm,0.728959087,F -coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.477439748,F -coef_eatout_departure_constant_before_6_pm,-1.21554671,F -coef_eatout_departure_constant_6_pm_to_6_30_pm,-0.425984037,F -coef_eatout_departure_constant_6_30_pm_to_7_pm,0,T -coef_eatout_departure_constant_7_pm_to_7_30_pm,-0.227800647,F -coef_eatout_departure_constant_after_7_30_pm,-0.293904097,F -coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.55440861,F -coef_eatout_arrival_constant_9_30_am_to_11_am,0.486337344,F -coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.629299404,F -coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.938528731,F -coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.584420106,F -coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.842550215,F -coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.298486505,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.125034982,F -coef_eatout_arrival_constant_before_6_30_pm,-0.029062996,F -coef_eatout_arrival_constant_6_30_pm_to_7_pm,-0.509075598,F -coef_eatout_arrival_constant_7_pm_to_7_30_pm,-0.124885931,F -coef_eatout_arrival_constant_7_30_pm_to_8_pm,-0.605455664,F -coef_eatout_arrival_constant_8_pm_to_8_30_pm,0,T -coef_eatout_arrival_constant_8_30_pm_to_9_pm,-0.657382835,F -coef_eatout_arrival_constant_after_9_pm,-0.813629712,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.204722406,F -coef_eatout_duration_constant_0_hour,-11.72660422,F -coef_eatout_duration_constant_30_minutes,-5.08873115,F -coef_eatout_duration_constant_1_hour,-0.125521065,F -coef_eatout_duration_constant_1_hour_30_minutes,0,T -coef_eatout_duration_constant_2_hours,-0.124807752,F -coef_eatout_duration_constant_2_hour_30_minutes_or_more,-0.156019835,F -coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.3357414,F -coef_eatout_calibration_constant_duration_1,-0.333697861,F -coef_eatout_calibration_constant_duration_2,-0.245716,F -coef_eatout_calibration_constant_duration_3,0.052708833,F -coef_eatout_calibration_constant_duration_4,0.041571499,F -coef_eatout_calibration_constant_departure_1,-10,F -coef_eatout_calibration_constant_departure_2,-10,F -coef_eatout_calibration_constant_departure_3,-10,F -coef_eatout_calibration_constant_departure_17,0.706568704,F -coef_eatout_calibration_constant_departure_18,0.634353544,F -coef_eatout_calibration_constant_departure_19,0.584387268,F -coef_eatout_calibration_constant_departure_20,0.469777884,F -coef_eatout_calibration_constant_departure_21,0.39548931,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv deleted file mode 100644 index 76a5b7c13..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv +++ /dev/null @@ -1,72 +0,0 @@ -coefficient_name,value,constrain -coef_escort_mode_choice_logsum,1.173173034,F -coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.335017673,F -coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.005298165,F -coef_escort_fulltime_worker_departure_after_8_am_linear,-0.037980109,F -coef_escort_fulltime_worker_departure_after_3_am_linear,0.163254125,F -coef_escort_fulltime_worker_duration_less_than_30_minutes,-0.275077482,F -coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.051530545,F -coef_escort_university_student_duration_less_than_30_minutes,-0.426802718,F -coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.240582361,F -coef_escort_driving_age_student_duration_less_than_30_minutes,-0.554146191,F -coef_escort_driving_age_student_duration_greater_than_30_minutes,0.299387708,F -coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.195482563,F -coef_escort_med_high_income_duration_greater_than_30_minutes,-0.029281467,F -coef_escort_households_with_no_kids_departure_before_7_30_am,0.589083327,F -coef_escort_households_with_no_kids_departure_after_8_00_am,0.086690827,F -coef_escort_households_with_no_kids_departure_before_2_30_pm,0.477582648,F -coef_escort_households_with_no_kids_departure_after_3_00_pm,-0.204065502,F -coef_escort_households_with_no_kids_arrival_before_8_am,-0.360039254,F -coef_escort_households_with_no_kids_arrival_after_8_30_am,0.091614107,F -coef_escort_households_with_no_kids_arrival_before_3_pm,0.432854268,F -coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.131037275,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.109700265,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.224568648,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-0.357416434,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.629285298,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.039005148,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,-0.06556611,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.117680977,F -coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,-0.057322708,F -coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.062899692,F -coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,-0.048533895,F -coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.469240002,F -coef_escort_departure_constant_before_7_am,-2.070292862,F -coef_escort_departure_constant_7_am_to_7_30_am,-0.642734296,F -coef_escort_departure_constant_7_30_am_to_8_am,0,T -coef_escort_departure_constant_8_am_to_8_30_am,-0.214617667,F -coef_escort_departure_constant_8_30_am_to_9_am,-0.147266606,F -coef_escort_departure_constant_after_9_am,-1.356686422,F -coef_escort_departure_constant_1_30_pm_to_2_pm,0.368092381,F -coef_escort_departure_constant_2_pm_to_2_30_pm,1.166803383,F -coef_escort_departure_constant_2_30_pm_to_3_pm,1.28466083,F -coef_escort_departure_constant_3_pm_to_3_30_pm,0.581891245,F -coef_escort_departure_constant_after_3_30_pm,0.834510243,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.175257649,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,-0.019161202,F -coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.44978138,F -coef_escort_arrival_constant_before_7_am,0.549584585,F -coef_escort_arrival_constant_7_am_to_7_30_am,0.488181278,F -coef_escort_arrival_constant_7_30_am_to_8_am,0.236447651,F -coef_escort_arrival_constant_8_am_to_8_30_am,0,T -coef_escort_arrival_constant_8_30_am_to_9_am,-0.683756801,F -coef_escort_arrival_constant_after_9_am,-1.428888485,F -coef_escort_arrival_constant_2_30_pm_to_3_pm,1.311480662,F -coef_escort_arrival_constant_3_pm_to_3_30_pm,1.316883154,F -coef_escort_arrival_constant_3_30_pm_to_4_pm,1.396838392,F -coef_escort_arrival_constant_4_pm_to_4_30_pm,1.03146139,F -coef_escort_arrival_constant_after_4_30_pm,0.907344583,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.148408887,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.389082896,F -coef_escort_duration_constant_0_hour,-0.173757322,F -coef_escort_duration_constant_30_minutes,0,T -coef_escort_duration_constant_1_hour,-0.431287743,F -coef_escort_duration_constant_1_hour_30_minutes,-0.700473959,F -coef_escort_duration_constant_2_hours,-1.071871358,F -coef_escort_duration_constant_longer_than_2_hours,-1.691098421,F -coef_escort_calibration_constant_duration_1,-0.047200214,F -coef_escort_calibration_constant_duration_2,0.035611332,F -coef_escort_calibration_constant_departure_9,0.106814756,F -coef_escort_calibration_constant_departure_10,0.215386864,F -coef_escort_calibration_constant_departure_23,-0.255087318,F -coef_escort_calibration_constant_departure_24,-0.296870428,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv deleted file mode 100644 index d9a4e607c..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv +++ /dev/null @@ -1,53 +0,0 @@ -coefficient_name,value,constrain -coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,-0.312282762,F -coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_discretionary_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_discretionary_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_discretionary_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_discretionary_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.742176805,F -coef_discretionary_departure_constant_before_7_30_am,-1.323901585,F -coef_discretionary_departure_constant_7_30_am_to_8_am,-0.695441631,F -coef_discretionary_departure_constant_8_am_to_8_30_am,-0.269903336,F -coef_discretionary_departure_constant_8_30_am_to_9_am,-0.093709211,F -coef_discretionary_departure_constant_9_am_to_9_30_am,0.265634082,F -coef_discretionary_departure_constant_9_30_am_to_10_am,0.287521134,F -coef_discretionary_departure_constant_10_am_to_10_30_am,0.396547817,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,-0.245885745,F -coef_discretionary_departure_constant_before_5_pm,-1.344482349,F -coef_discretionary_departure_constant_5_pm_to_5_30_pm,-0.622632748,F -coef_discretionary_departure_constant_5_30_pm_to_6_pm,-0.456718676,F -coef_discretionary_departure_constant_6_pm_to_6_30_pm,-0.206896106,F -coef_discretionary_departure_constant_6_30_pm_to_7_pm,0,T -coef_discretionary_departure_constant_after_7_pm,-0.46439343,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-0.291998986,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.148649188,F -coef_discretionary_arrival_constant_before_6_30_pm,0.668775963,F -coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-0.053520826,F -coef_discretionary_arrival_constant_7_pm_to_7_30_pm,0.099726391,F -coef_discretionary_arrival_constant_7_30_pm_to_8_pm,0.063414092,F -coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0,T -coef_discretionary_arrival_constant_8_30_pm_to_9_pm,-0.18610847,F -coef_discretionary_arrival_constant_after_9_pm,-0.423207857,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.525545923,F -coef_discretionary_duration_constant_0_hour,0.436988282,F -coef_discretionary_duration_constant_30_minutes,1.35967577,F -coef_discretionary_duration_constant_1_hour,1.692672999,F -coef_discretionary_duration_constant_1_hr_30_minutes,1.118932964,F -coef_discretionary_duration_constant_2_hours,0.771255733,F -coef_discretionary_duration_constant_2_hr_30_minutes,0,T -coef_discretionary_duration_constant_3_hours_or_more,-0.631242175,F -coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.700935645,F -coef_discretionary_calibration_constant_duration_4,-0.132674257,F -coef_discretionary_calibration_constant_duration_5,-0.013371871,F -coef_discretionary_calibration_constant_departure_29,0.232927977,F -coef_discretionary_calibration_constant_departure_30,0.306104612,F -coef_discretionary_calibration_constant_departure_31,0.285520678,F -coef_discretionary_calibration_constant_departure_32,0.115886631,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv deleted file mode 100644 index 967964c3d..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv +++ /dev/null @@ -1,54 +0,0 @@ -Label,Description,Expression,Coefficient -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.duration==4)),coef_maintenance_calibration_constant_duration_5 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv deleted file mode 100644 index 991068579..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv +++ /dev/null @@ -1,53 +0,0 @@ -coefficient_name,value,constrain -coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_maintenance_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_maintenance_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_maintenance_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_maintenance_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,0,T -coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_maintenance_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_maintenance_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.864112609,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.504598473,F -coef_maintenance_departure_constant_before_8_am,-0.383711788,F -coef_maintenance_departure_constant_8_am_to_8_30_am,-0.076771517,F -coef_maintenance_departure_constant_8_30_am_to_9_00_am,-0.169259979,F -coef_maintenance_departure_constant_9_am_to_9_30_am,-0.051785379,F -coef_maintenance_departure_constant_9_30_am_to_10_am,-0.214942451,F -coef_maintenance_departure_constant_10_am_to_10_30_am,0,T -coef_maintenance_departure_constant_10_30_am_to_11_am,-0.427568963,F -coef_maintenance_departure_constant_after_11_am,-0.520863411,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.042879095,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.003157293,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.226803619,F -coef_maintenance_arrival_constant_before_10_30_am,-0.223212258,F -coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T -coef_maintenance_arrival_constant_11_am_to_11_30_am,-0.128382637,F -coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.167977332,F -coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,-0.149495878,F -coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.087679934,F -coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.121707557,F -coef_maintenance_arrival_constant_after_4_30_pm,0.106745013,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.232610927,F -coef_maintenance_duration_constant_0_hr,-0.483549396,F -coef_maintenance_duration_constant_30_minutes,0,T -coef_maintenance_duration_constant_longer_than_30_minutes,-1.450618319,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.275082922,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,0.208434683,F -coef_maintenance_calibration_constant_duration_1,-0.124602605,F -coef_maintenance_calibration_constant_duration_2,-0.103637715,F -coef_maintenance_calibration_constant_duration_3,-0.225442145,F -coef_maintenance_calibration_constant_duration_4,-0.145273012,F -coef_maintenance_calibration_constant_duration_5,-0.019241539,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv deleted file mode 100755 index 318f1b695..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv +++ /dev/null @@ -1,348 +0,0 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv deleted file mode 100755 index 05b6b8ba9..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv +++ /dev/null @@ -1,348 +0,0 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), df.departureLinearShift1_escort,0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), df.durationShift_escort*df.num_non_escort_tours, 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), df.durationShift_escort *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), df.depConstShift2_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), df.arrConstShift2_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,ESCORT - Calibration Constant - Departure = 24,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv deleted file mode 100644 index 07e94fd82..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv +++ /dev/null @@ -1,55 +0,0 @@ -Label,Description,Expression,Coefficient -#SHOPPING,,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -#util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.max_time_block_available(df.person_id)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@(( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.duration==2)),coef_shopping_calibration_constant_duration_3 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv deleted file mode 100644 index fac50e210..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv +++ /dev/null @@ -1,53 +0,0 @@ -coefficient_name,value,constrain -coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_shopping_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_shopping_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_shopping_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_shopping_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_shopping_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_shopping_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_shopping_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_shopping_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_shopping_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.959875456,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,1.112594898,F -coef_shopping_departure_constant_before_9_am,-0.446394064,F -coef_shopping_departure_constant_9_am_to_9_30_am,-0.021669265,F -coef_shopping_departure_constant_9_30_am_to_10_am,-0.282978638,F -coef_shopping_departure_constant_10_am_to_10_30_am,0,T -coef_shopping_departure_constant_10_30_am_to_11_00_am,-0.309421311,F -coef_shopping_departure_constant_after_11_am,-0.541073357,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,-0.072013428,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.000653398,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,-0.18376635,F -coef_shopping_arrival_constant_before_12_30_pm,-0.716195343,F -coef_shopping_arrival_constant_12_30_pm_to_3_pm,-0.502714001,F -coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.167868872,F -coef_shopping_arrival_constant_3_30_pm_to_4_pm,-0.156786941,F -coef_shopping_arrival_constant_4_pm_to_4_30_pm,0,T -coef_shopping_arrival_constant_4_30_pm_to_5_pm,-0.057314044,F -coef_shopping_arrival_constant_5_pm_to_5_30_pm,-0.580040851,F -coef_shopping_arrival_constant_5_30_pm_to_7_pm,-0.32239566,F -coef_shopping_arrival_constant_7_pm_to_9_30_pm,-0.347828147,F -coef_shopping_arrival_constant_after_9_30_pm,-1.123574723,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.499770654,F -coef_shopping_duration_constant_0_hr,-0.131743185,F -coef_shopping_duration_constant_30_minutes,0.888857137,F -coef_shopping_duration_constant_1_hr,0,T -coef_shopping_duration_constant_1_hour_30_minutes,-0.333413031,F -coef_shopping_duration_constant_2_hrs,-0.850897912,F -coef_shopping_duration_constant_longer_than_2_hrs,-1.203783479,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.293581223,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,-0.215759138,F -coef_shopping_calibration_constant_duration_1,-0.138450424,F -coef_shopping_calibration_constant_duration_2,-0.092704403,F -coef_shopping_calibration_constant_duration_3,-0.087738073,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv deleted file mode 100644 index febe0516d..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv +++ /dev/null @@ -1,54 +0,0 @@ -Label,Description,Expression,Coefficient -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@((df.start<12)) * ((np.minimum(12-df.start,48)*(df.start<12)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.duration ==8)),coef_social_calibration_constant_duration_9 diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv deleted file mode 100644 index 9819e0e69..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv +++ /dev/null @@ -1,52 +0,0 @@ -coefficient_name,value,constrain -coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,-0.312282762,F -coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_social_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_social_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_social_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_social_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.529943196,F -coef_social_departure_constant_before_9_am,-0.198438086,F -coef_social_departure_constant_9_am_to_9_30_am,0.137620628,F -coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,-0.142078961,F -coef_social_departure_constant_before_5_30_pm,-0.390965052,F -coef_social_departure_constant_5_30_pm_to_6_pm,-0.453580491,F -coef_social_departure_constant_6_pm_to_6_30_pm,0,T -coef_social_departure_constant_6_30_pm_to_7_pm,-0.088537991,F -coef_social_departure_constant_7_pm_to_7_30_pm,0.052983115,F -coef_social_departure_constant_after_7_30_pm,-0.649629162,F -coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.09574499,F -coef_social_arrival_constant_3_pm_to_3_30_pm,0.37674882,F -coef_social_arrival_constant_3_30_pm_to_4_pm,0.583355461,F -coef_social_arrival_constant_4_pm_to_4_30_pm,0.727855233,F -coef_social_arrival_constant_5_pm_to_6_pm,0.249551955,F -coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,0.053771388,F -coef_social_arrival_constant_before_8_30_pm,0.308763611,F -coef_social_arrival_constant_8_30_pm_to_9_pm,-0.208797698,F -coef_social_arrival_constant_9_pm_to_9_30_pm,-0.336319511,F -coef_social_arrival_constant_9_30_pm_to_10_pm,0,T -coef_social_arrival_constant_10_pm_to_10_30_pm,-0.055707591,F -coef_social_arrival_constant_after_10_30_pm,-0.612356296,F -coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.348479901,F -coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.614440191,F -coef_social_duration_constant_less_than_3_hours,0.353666691,F -coef_social_duration_constant_3_hours,0,T -coef_social_duration_constant_3_hrs_30_minutes,-0.691218836,F -coef_social_duration_constant_4_hours_or_more,-1.344375328,F -coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-0.786970714,F -coef_social_calibration_constant_duration_1,-1.346772472,F -coef_social_calibration_constant_duration_2,0.377121689,F -coef_social_calibration_constant_duration_3,0.179818928,F -coef_social_calibration_constant_duration_4,-0.283418619,F -coef_social_calibration_constant_duration_5,-0.103541313,F -coef_social_calibration_constant_duration_6,-0.03704707,F -coef_social_calibration_constant_duration_7,-0.062437167,F -coef_social_calibration_constant_duration_8,0.047640282,F -coef_social_calibration_constant_duration_9,0.284369793,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv deleted file mode 100755 index f84c333d4..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv +++ /dev/null @@ -1,59 +0,0 @@ -Label,Description,Expression,Coefficient -util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum -util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_before_730_am__Linear -util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_after_800_am_Linear -util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_8hrs -util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_gt_8hrs -util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear -util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_0_to_5_yrs_Departure_Before_730_am -util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_Before_730_am -util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_After_800_am -util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_16_to_17_yrs_Departure_After_800_am -util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_lt_8hrs -util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_gt_8hrs -util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_lt_8hrs -util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_gt_8hrs -util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_16_to_17_yrs_Duration_gt_8hrs -util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start<10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear -util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start>10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear -util_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration < 8hrs,"@(df.end<27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs -util_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end>27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs -util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear -util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear -util_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end<27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs -util_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end>27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs -util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours -util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours -util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>10))) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am -util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours -util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours -util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours -util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours -util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM -util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 -util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 -util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 -util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 -util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start>10),coef_Departure_Constant_After_0800_AM -util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * ((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear -util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11)) * (((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root -util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM -util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ -util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ -util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ -util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ -util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ -util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 -util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ -util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM -util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * ((np.minimum(24-df.end,6)*(df.end<24)) + (np.minimum(df.end-31,12)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear -util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * ((np.minimum(13-df.duration,48)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear -util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs -util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours -util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours -util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours -util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours -util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours -util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs -util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear -util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv deleted file mode 100755 index 589b4ce61..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv +++ /dev/null @@ -1,59 +0,0 @@ -coefficient_name,value,constrain -coef_Mode_Choice_Logsum,0.524017431,F -coef_Low_income_lt25000_Departure_before_730_am__Linear,0.134574548,F -coef_Low_income_lt25000_Departure_after_800_am_Linear,-0.075554725,F -coef_Low_income_lt25000_Duration_lt_8hrs,-0.150039779,F -coef_Low_income_lt25000_Duration_gt_8hrs,-0.043562413,F -coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.102594589,F -coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.178916721,F -coef_Age_13_to_15_yrs_Departure_Before_730_am,-0.164708594,F -coef_Age_13_to_15_yrs_Departure_After_800_am,0.169641715,F -coef_Age_16_to_17_yrs_Departure_After_800_am,0.077527892,F -coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.254486164,F -coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.14409403,F -coef_Age_13_to_15_yrs_Duration_lt_8hrs,-0.211129273,F -coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.102348303,F -coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.1184616,F -coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.011813391,F -coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.0088956,F -coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,-0.011793416,F -coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,0.001485453,F -coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.1625279,F -coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.161840551,F -coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,-0.233061473,F -coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,0.08462748,F -coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.154332088,F -coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,-0.62871831,F -coef_Subsequent_tour_is_school_tour_Departure_after_800_am,-0.41618671,F -coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.261423274,F -coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,-0.263857404,F -coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.537535787,F -coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.545479806,F -coef_Departure_Constant_Before_0600_AM,-10.04417122,F -coef_Departure_Constant_0600_AM_to_0630_AM_7,-3.792318538,F -coef_Departure_Constant_0630_AM_to_0700_AM_8,-1.941704371,F -coef_Departure_Constant_0700_AM_to_0730_AM_9,-0.558080224,F -coef_Departure_Constant_0730_AM_to_0800_AM_10,0,T -coef_Departure_Constant_After_0800_AM,-0.280439854,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.293697164,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-1.220165702,F -coef_Arrival_Constant_Before_0230_PM,0.720751128,F -coef_Arrival_Constant_0230_PM_0300_PM_24_,1.605012317,F -coef_Arrival_Constant_0300_PM_0330_PM_25_,0.463502951,F -coef_Arrival_Constant_0330_PM_0400_PM_26_,0.196107179,F -coef_Arrival_Constant_0400_PM_0430_PM_27_,0,T -coef_Arrival_Constant_0430_PM_0500_PM_28_,-0.389421484,F -coef_Arrival_Constant_0500_PM_0530_PM_29,-1.412720271,F -coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.938567609,F -coef_Arrival_Constant_After_0600_PM,-2.246103785,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.552223894,F -coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.249724903,F -coef_Duration_Constant_Shorter_than_7_hrs,-2.791243553,F -coef_Duration_Constant_7_hours,-1.679006455,F -coef_Duration_Constant_7p5_hours,-0.555288612,F -coef_Duration_Constant_8_hours,0,T -coef_Duration_Constant_8p5_hours,-0.139412248,F -coef_Duration_Constant_9_hours,-0.509620713,F -coef_Duration_Constant_Longer_than_9_hrs,-0.561449384,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.379484906,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,-0.028814477,F diff --git a/activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv deleted file mode 100755 index 19a85e817..000000000 --- a/activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv +++ /dev/null @@ -1,97 +0,0 @@ -coefficient_name,value,constrain -coef_Mode_Choice_Logsum,0.2279,T -coef_Female_Departure_before_7_am,-0.12935306,F -coef_Female_Arrival_after_6_pm,-0.041312616,F -coef_Female_with_preschool_child_Departure_before_7_am,-0.129130464,F -coef_Female_with_preschool_child_Departure_after_7_am,-0.031947595,F -coef_Female_with_preschool_child_Arrival_after_6_pm,-0.048859218,F -coef_Low_income_lt_25000_Departure_before_7_am,0.232768373,F -coef_Low_income_lt_25000_Departure_after_7_am,0.014908169,F -coef_Low_income_lt_25000_Arrival_after_6_pm,0.039105101,F -coef_Med_income_25k_to_60k_Departure_before_7_am,0.123945957,F -coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.020965221,F -coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.09890939,F -coef_Age_16_to_18_yrs_Departure_Before_7_am,-0.459593556,F -coef_Age_16_to_18_yrs_Departure_After_7_am,0.060951693,F -coef_Age_19_to_24_yrs_Departure_After_7_am,0.031477187,F -coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.11723451,F -coef_Age_65_plus_yrs_Departure_After_7_am,0.051923956,F -coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.032734453,F -coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.027623617,F -coef_Age_56_to_64_yrs_Arrival_after_6_pm,-0.049130187,F -coef_Age_65_plus_yrs_Arrival_before_5_pm,0.056774635,F -coef_Age_65_plus_yrs_Arrival_after_6_pm,-0.077532684,F -coef_Zero_auto_HH_Departure_before_7_am,0.396983749,F -coef_Zero_auto_HH_Arrival_after_6_pm,0.050665232,F -coef_Parttime_worker_Departure_before_7_am,-0.264760988,F -coef_Parttime_worker_Departure_after_7_am,0.126626287,F -coef_Parttime_worker_Arrival_before_5_pm,0.175158545,F -coef_Parttime_worker_Arrival_after_6_pm,-0.054124518,F -coef_University_student_Departure_after_7_am,0.024758204,F -coef_University_student_Arrival_before_5_pm,0.035389739,F -coef_University_student_Arrival_after_6_pm,0.06173996,F -coef_Blue_collar_Departure_before_7_am,0.327242475,F -coef_Blue_collar_Departure_after_7_am,0.047214248,F -coef_Blue_collar_Arrival_before_5_pm,0.04197056,F -coef_Service_Departure_before_7_am,0.117783508,F -coef_Service_Departure_after_7_am,0.081611629,F -coef_Service_Arrival_before_5_pm,0,T -coef_Health_Departure_before_7_am,0.135275931,F -coef_Health_Arrival_after_6_pm,0.062010123,F -coef_Retail_and_food_Departure_after_7_am,0.076302969,F -coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F -coef_Retail_and_food_Arrival_after_6_pm,0.027069194,F -coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.011511462,F -coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.003821379,F -coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.00549578,F -coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.002253695,F -coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.069957209,F -coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,-0.019807228,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.084564489,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,-0.023894467,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.018983499,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.032091123,F -coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,0.145890035,F -coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.214531877,F -coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.3069241,F -coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.526297898,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,-0.221304523,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.176348812,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.064893097,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.656942049,F -coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-1.151564775,F -coef_Departure_Constant_Before_06_AM,-2.197677208,F -coef_Departure_Constant_06_AM_06_AM_7,-1.314098638,F -coef_Departure_Constant_06_AM_07_AM_8,-0.558766028,F -coef_Departure_Constant_07_AM_07_AM_9,0,T -coef_Departure_Constant_07_AM_08_AM_10,-0.036957515,F -coef_Departure_Constant_08_AM_08_AM_11,-0.285560423,F -coef_Departure_Constant_08_AM_09_AM_12,-0.555478447,F -coef_Departure_Constant_After_09_AM,-0.865125273,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.435746145,F -coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.191607342,F -coef_Arrival_Constant_Before_03_PM,-0.289333093,F -coef_Arrival_Constant_03_PM_04_PM_26,-0.273555837,F -coef_Arrival_Constant_04_PM_04_PM_27,-0.142653706,F -coef_Arrival_Constant_04_PM_05_PM_28,-0.124814807,F -coef_Arrival_Constant_05_PM_05_PM_29,0.004265544,F -coef_Arrival_Constant_05_PM_06_PM_30,0,T -coef_Arrival_Constant_06_PM_06_PM_31,-0.060515031,F -coef_Arrival_Constant_06_PM_7_PM_32,-0.236621114,F -coef_Arrival_Constant_7_PM_7_PM_33,-0.577646614,F -coef_Arrival_Constant_7_PM_8_PM_34,-0.815994515,F -coef_Arrival_Constant_After_08_PM,-0.854151925,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,-0.469720787,F -coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.074266981,F -coef_Duration_Constant_Shorter_than_8p5_hrs,-0.748584335,F -coef_Duration_Constant_8p5_hours,-0.654814097,F -coef_Duration_Constant_9_hours,-0.372064236,F -coef_Duration_Constant_9p5_hours,-0.144226124,F -coef_Duration_Constant_10_hours,0.013153356,F -coef_Duration_Constant_10p5_hours,0,T -coef_Duration_Constant_11_hours,-0.115847245,F -coef_Duration_Constant_11p5_hours,-0.288506368,F -coef_Duration_Constant_12_hours,-0.524241874,F -coef_Duration_Constant_Longer_than_12_hrs,-0.598634071,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,-0.293607565,F -coef_Calibration_constant_Duration_0,-10,F diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv deleted file mode 100644 index 15a053d16..000000000 --- a/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv +++ /dev/null @@ -1,21 +0,0 @@ -Label,Description,Expression,no_pass,pass -util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft_pass -util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt_pass -util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un_pass -util_nw,Non-working adult,@df.ptype==PTYPE_NONWORK,0,coef_nw_pass -util_rt,Retired,@df.ptype==PTYPE_RETIRED,0,coef_rt_pass -util_inc1,0-$9k,"@df.income.between(0, 9000)",0,coef_inc1_pass -util_inc2,$10-$24k,"@df.income.between(10000, 24000)",0,coef_inc2_pass -util_inc3,$25-$34k,"@df.income.between(25000,34000)",0,coef_inc3_pass -util_inc4,$35-$49k,"@df.income.between(35000,49000)",0,coef_inc4_pass -util_inc10,$250k+,@df.income >= 250000,0,coef_inc10_pass -#util_na20,NAICS 20 (mining/utilities/construct),@df.industry==20,0,coef_na20_pas -#util_na30,NAICS 30 (manufacturing,@df.industry==30,0,coef_na30_pas -#util_na50,NAICS 50 (Info/Fin/insur/real estate/prof/sci/tech/manage/admin),@df.industry==50,0,coef_na50_pas -#util_na70,NAICS 70 (Entertain/accom),@df.industry==70,0,coef_na70_pas -#util_na80,NAICS 80 (Other services),@df.industry==80,0,coef_na80_pas -#util_publ,NAICS 90 (Public admin),@df.industry==90,0,coef_publ_pas -#approximate measure for the time being,,, -util_wrkamt,Auto minus transit time (work),@df.work_auto_savings * -1,0,coef_wrkamt_pas -util_subs,Subsidy offered,@df.transit_pass_subsidy,0,coef_subs_pas -utils_pass_asc,Constant,1,0,coef_pass_asc diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv deleted file mode 100644 index eaaff3fd0..000000000 --- a/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv +++ /dev/null @@ -1,12 +0,0 @@ -Label,Description,Expression,no_subsidy,subsidy -util_ft,Full-time worker,@df.pemploy==PEMPLOY_FULL,0,coef_ft -util_pt,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_pt -util_un,University/College,@df.ptype==PTYPE_SCHOOL,0,coef_un -#util_na70,NAICS 70 (Entertain/accom),@df.industry==70,0,coef_na70 -#util_na80,NAICS 80 (Other services),@df.industry==80,0,coef_na80 -#util_publ,NAICS 90 (Public admin),@df.industry==90,0,coef_publ -#approximate measure for the time being,,, -util_tr_hh_emp,Household transit accessibility,@df.trOpRetail,0,coef_tr_hh_emp -#approximate measure for the time being,,, -util_pkcost,Daily parking cost (dollars),@df.PRKCST+df.OPRKCST,0,coef_pkcost -utils_sub_asc,Constant,1,0,coef_sub_asc diff --git a/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv deleted file mode 100644 index 8e4b511e0..000000000 --- a/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv +++ /dev/null @@ -1,10 +0,0 @@ -coefficient_name,coefficient_name,value,constrain -coef_ft,Full-time worker,1.882665664,F -coef_pt,Part-time worker,1.360746182,F -coef_un,University/College,2.026985147,F -coef_na70,NAICS 70 (Entertain/accom),-0.659568416,F -coef_na80,NAICS 80 (Other services),-0.352588858,F -coef_publ,NAICS 90 (Public admin),0.864991571,F -coef_tr_hh_emp,Transit accessibility to households,0.049608562,F -coef_pkcost,Daily parking cost (dollars),0.060145818,F -coef_sub_asc,Constant,-5.138015944,F diff --git a/activitysim/examples/prototype_semcog/configs/trip_destination.csv b/activitysim/examples/prototype_semcog/configs/trip_destination.csv deleted file mode 100755 index 79b73f1ea..000000000 --- a/activitysim/examples/prototype_semcog/configs/trip_destination.csv +++ /dev/null @@ -1,17 +0,0 @@ -Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose)) # sharrow: np.log1p(size_terms['sizearray'])",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0 # sharrow: size_terms['sizearray'] == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, -distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment joint),@df.is_joint * (od_skims['DIST'] + dp_skims['DIST']),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 -stop proximity to home (outbound),@df.outbound * od_skims['DIST'],-0.38,0,0,0,0,0,0,0,0,0 -stop proximity to home (inbound),@~df.outbound * dp_skims['DIST'],-0.15,0,0,0,0,0,0,0,0,0 -stop proximity to main destination (outbound),@df.outbound * dp_skims['DIST'],-0.26,,,,,,,,, -stop proximity to main destination (inbound),@~df.outbound * od_skims['DIST'],0,,,,,,,,, -#,,,,,,,,,,, -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1,1,1 -Mode choice logsum from origin to stop,od_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 -Can't access stop zone by this tour mode,(od_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Mode choice logsum from stop to destination,dp_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 -Can't access destination zone by this tour mode,(dp_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv b/activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv deleted file mode 100755 index 9908e6972..000000000 --- a/activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv +++ /dev/null @@ -1,399 +0,0 @@ -Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED -#Drive alone no toll,,,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - In-vehicle time,@c_ivt*odt_skims['SOV_TIME'],1,,,,,,,,,,,,,,,,, -DRIVEALONE - Terminal time,@c_walktimeshort * df.total_terminal_time,1,,,,,,,,,,,,,,,,, -DRIVEALONE - Operating cost ,@df.c_cost * costPerMile * odt_skims['SOV_DIST'],1,,,,,,,,,,,,,,,,, -DRIVEALONE - Parking cost ,c_cost * total_parking_cost,1,,,,,,,,,,,,,,,,, -DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),1,,,,,,,,,,,,,,,,, -#Shared ride 2,,,,,,,,,,,,,,,,,,, -SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, -SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, -SHARED2 - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,1,,,,,,,,,,,,,,,, -SHARED2 - Terminal time,@c_walktimeshort * df.total_terminal_time,,1,,,,,,,,,,,,,,,, -SHARED2 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV2_DIST'],,1,,,,,,,,,,,,,,,, -SHARED2 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr2,,1,,,,,,,,,,,,,,,, -SHARED2 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,1,,,,,,,,,,,,,,,, -SHARED2 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,1,,,,,,,,,,,,,,,, -SHARED2 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,1,,,,,,,,,,,,,,,, -#Shared ride 3+,,,,,,,,,,,,,,,,,,, -SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, -SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, -SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, -SHARED3 - In-vehicle time,@c_ivt * odt_skims['HOV3_TIME'],,,1,,,,,,,,,,,,,,, -SHARED3 - Terminal time,@c_walktimeshort * df.total_terminal_time,,,1,,,,,,,,,,,,,,, -SHARED3 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV3_DIST'],,,1,,,,,,,,,,,,,,, -SHARED3 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr3,,,1,,,,,,,,,,,,,,, -SHARED3 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,1,,,,,,,,,,,,,,, -SHARED3 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,1,,,,,,,,,,,,,,, -SHARED3 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,1,,,,,,,,,,,,,,, -#Walk,,,,,,,,,,,,,,,,,,, -WALK - Time up to 2 miles,@c_walktimeshort * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, -WALK - Time beyond 2 of a miles,@c_walktimelong * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, -WALK - Destination zone densityIndex,@c_density_index * df.density_index,,,,1,,,,,,,,,,,,,, -WALK - Topology,@c_topology_walk * df.trip_topology,,,,1,,,,,,,,,,,,,, -#Bike,,,,,,,,,,,,,,,,,,, -BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,-999,,,,,,,,,,,,, -BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, -BIKE - Time up to 6 miles,@c_biketimeshort * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, -BIKE - Time beyond 6 of a miles,@c_biketimelong * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, -BIKE - Destination zone densityIndex,@c_density_index*df.density_index,,,,,1,,,,,,,,,,,,, -BIKE - Topology,@c_topology_bike * df.trip_topology,,,,,1,,,,,,,,,,,,, -#Walk to Local,,,,,,,,,,,,,,,,,,, -WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, -WALK_LOC - In-vehicle time,@c_ivt * odt_skims['WLK_LOC_IVT'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Short iwait time,@c_short_i_wait * (odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh),,,,,,1,,,,,,,,,,,, -WALK_LOC - Long iwait time,@c_long_i_wait * (odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0),,,,,,1,,,,,,,,,,,, -WALK_LOC - transfer wait time,@c_xwait * odt_skims['WLK_LOC_XWAIT'],,,,,,1,,,,,,,,,,,, -WALK_LOC - number of transfers,@c_xfers_wlk * (odt_skims['WLK_LOC_NT']).clip(0),,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk access time,@c_wacc * df.origin_walk_time,,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk other time,@c_waux * odt_skims['WLK_LOC_WAUX'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Fare,@df.c_cost * odt_skims['WLK_LOC_FARE'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,1,,,,,,,,,,,, -WALK_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,1,,,,,,,,,,,, -WALK_LOC - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,1,,,,,,,,,,,, -#Walk to Premium,,,,,,,,,,,,,,,,,,, -WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, -WALK_PRM - In-vehicle time,@c_ivt * odt_skims['WLK_PRM_IVT'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_PRM_IVTT_PMov'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_PRM_IVTT_StCar'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_PRM_IVTT_Brt'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_PRM_IVTT_UrbRail'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_PRM_IVTT_ComRail'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Short iwait time,@c_short_i_wait * (odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,1,,,,,,,,,,, -WALK_PRM - Long iwait time,@c_long_i_wait * (odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0),,,,,,,1,,,,,,,,,,, -WALK_PRM - transfer wait time,@c_xwait * odt_skims['WLK_PRM_XWAIT'],,,,,,,1,,,,,,,,,,, -WALK_PRM - number of transfers,@c_xfers_wlk * (odt_skims['WLK_PRM_NT']).clip(0),,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk otherLight rail/Ferry time,@c_waux * odt_skims['WLK_PRM_WAUX'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Fare,@df.c_cost * odt_skims['WLK_PRM_FARE'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,1,,,,,,,,,,, -WALK_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,1,,,,,,,,,,, -WALK_PRM - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,1,,,,,,,,,,, -#Walk to Mix,,,,,,,,,,,,,,,,,,, -WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, -WALK_MIX - In-vehicle time,@c_ivt * odt_skims['WLK_MIX_IVT'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on Bus,@c_ivt * odt_skims['WLK_MIX_IVTT_Bus'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_MIX_IVTT_PMov'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_MIX_IVTT_StCar'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_MIX_IVTT_Brt'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_MIX_IVTT_UrbRail'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_MIX_IVTT_ComRail'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Short iwait time,@c_short_i_wait * (odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,1,,,,,,,,,, -WALK_MIX - Long iwait time,@c_long_i_wait * (odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,1,,,,,,,,,, -WALK_MIX - transfer wait time,@c_xwait * odt_skims['WLK_MIX_XWAIT'],,,,,,,,1,,,,,,,,,, -WALK_MIX - number of transfers,@c_xfers_wlk * (odt_skims['WLK_MIX_NT']).clip(0),,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk other time,@c_waux * odt_skims['WLK_MIX_WAUX'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Fare,@df.c_cost * odt_skims['WLK_MIX_FARE'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,1,,,,,,,,,, -WALK_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,1,,,,,,,,,, -WALK_MIX - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,,1,,,,,,,,,, -#PNR to Local,,,,,,,,,,,,,,,,,,, -PNR_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,-999,,,,,,,,, -PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,, -PNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,1,,,,,,,,, -PNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,1,,,,,,,,, -PNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Unavailable,outbound & ~pnr_local_available_outbound,,,,,,,,,-999,,,,,,,,, -PNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_LOC_IVT'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['PNR_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_LOC_DTIME'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_LOC_WAUX'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_LOC_FARE'] + costPerMile*odt_skims['PNR_LOC_DDIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Unavailable,inbound & ~pnr_local_available_inbound,,,,,,,,,-999,,,,,,,,, -PNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_LOC_IVT'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['PNRE_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_LOC_DTIME'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_LOC_WAUX'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_LOC_FARE'] + costPerMile*odt_skims['PNRE_LOC_DDIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, -#Drive to Premium,,,,,,,,,,,,,,,,,,, -PNR_PRM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,,,,,,, -PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,, -PNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,1,,,,,,,, -PNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,1,,,,,,,, -PNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Unavailable,outbound & ~pnr_premium_available_outbound,,,,,,,,,,-999,,,,,,,, -PNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_PRM_IVT'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_PRM_DTIME'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_PRM_WAUX'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_PRM_FARE'] + costPerMile * odt_skims['PNR_PRM_DDIST']),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Unavailable,inbound & ~pnr_premium_available_inbound,,,,,,,,,,-999,,,,,,,, -PNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_PRM_IVT'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_PRM_DTIME'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_PRM_WAUX'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_PRM_FARE'] + costPerMile * odt_skims['PNRE_PRM_DDIST']),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,1,,,,,,,, -#PNR to Mix,,,,,,,,,,,,,,,,,,, -PNR_MIX - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,-999,,,,,,, -PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,, -PNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,1,,,,,,, -PNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,1,,,,,,, -PNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Unavailable,outbound & ~pnr_mix_available_outbound,,,,,,,,,,,-999,,,,,,, -PNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVT'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_MIX_DTIME'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_MIX_WAUX'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_MIX_FARE'] + costPerMile * odt_skims['PNR_MIX_DDIST']),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Unavailable,inbound & ~pnr_mix_available_inbound,,,,,,,,,,,-999,,,,,,, -PNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVT'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_MIX_DTIME'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_MIX_WAUX'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_MIX_FARE'] + costPerMile * odt_skims['PNRE_MIX_DDIST']),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, -#KNR to Local,,,,,,,,,,,,,,,,,,, -KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,, -KNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,1,,,,,, -KNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,1,,,,,, -KNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Unavailable,outbound & ~knr_local_available_outbound,,,,,,,,,,,,-999,,,,,, -KNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_LOC_IVT'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['KNR_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_LOC_DTIME'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_LOC_WAUX'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_LOC_FARE'] + costPerMile*odt_skims['KNR_LOC_DDIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Unavailable,inbound & ~knr_local_available_inbound,,,,,,,,,,,,-999,,,,,, -KNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_LOC_IVT'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['KNRE_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_LOC_DTIME'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_LOC_WAUX'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_LOC_FARE'] + costPerMile*odt_skims['KNRE_LOC_DDIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, -#Drive to Premium,,,,,,,,,,,,,,,,,,, -KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,, -KNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,1,,,,, -KNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,1,,,,, -KNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Unavailable,outbound & ~knr_premium_available_outbound,,,,,,,,,,,,,-999,,,,, -KNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_PRM_IVT'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_PRM_DTIME'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_PRM_WAUX'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_PRM_FARE'] + costPerMile * odt_skims['KNR_PRM_DDIST']),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Unavailable,inbound & ~knr_premium_available_inbound,,,,,,,,,,,,,-999,,,,, -KNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_PRM_IVT'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_PRM_DTIME'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_PRM_WAUX'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_PRM_FARE'] + costPerMile * odt_skims['KNRE_PRM_DDIST']),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,1,,,,, -#KNR to Mix,,,,,,,,,,,,,,,,,,, -KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,, -KNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,,1,,,, -KNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,,1,,,, -KNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Unavailable,outbound & ~knr_mix_available_outbound,,,,,,,,,,,,,,-999,,,, -KNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVT'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_MIX_DTIME'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_MIX_WAUX'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_MIX_FARE'] + costPerMile * odt_skims['KNR_MIX_DDIST']),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Unavailable,inbound & ~knr_mix_available_inbound,,,,,,,,,,,,,,-999,,,, -KNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVT'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_MIX_DTIME'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_MIX_WAUX'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_MIX_FARE'] + costPerMile * odt_skims['KNRE_MIX_DDIST']),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, -#Taxi,,,,,,,,,,,,,,,,,,, -Taxi - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,1,, -Taxi - Wait time,@c_ivt * 1.5 * df.origTaxiWaitTime,,,,,,,,,,,,,,,,1,, -#Taxi - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,1,, -#Taxi - Bridge toll,@df.c_cost * odt_skims['HOV2_BTOLL'],,,,,,,,,,,,,,,,1,, -Taxi - Fare,@df.c_cost * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,1,, -#TNC Single,,,,,,,,,,,,,,,,,,, -TNC Single - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,1, -TNC Single - Wait time,@c_ivt * 1.5 * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,1, -#TNC Single - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,1, -#TNC Single - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,1, -TNC Single - Cost,"@df.c_cost * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,1, -#TNC Shared,,,,,,,,,,,,,,,,,,, -TNC Shared - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,1 -TNC Shared - Wait time,@c_ivt * 1.5 * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,1 -#TNC Shared - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,1 -#TNC Shared - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,,1 -TNC Shared - Cost,"@df.c_cost * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,1 -#,,,,,,,,,,,,,,,,,,, -Auto tour mode availability,tour_mode_is_auto,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,,,,-999,,,,-999,-999,-999,-999,-999,-999,-999,,, -Drive to Transit tour mode availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,-999,,, -School bus tour mode availability,tour_mode_is_school_bus,-999,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,, -#indiv tour ASCs,,,,,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- shared ride 2,@sov_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,1,,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- shared ride 3+,@sov_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,1,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- walk,@sov_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,1,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- ride hail,@sov_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,1,1,1 -#Shared Ride 2 tour mode ASC -- shared ride 2 - reference mode,@sr2_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- drive alone,@sr2_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),1,,,,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- walk,@sr2_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- ride hail,@sr2_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,1,1,1 -Shared Ride 3+ tour mode ASC -- drive alone,@sr3p_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),1,,,,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- shared ride 2,@sr3p_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, -#Shared Ride 3+ tour mode ASC -- shared ride 3+ - reference mode,@sr3p_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- walk,@sr3p_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- ride hail,@sr3p_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,1,1,1 -Walk tour mode ASC -- ride hail,@walk_ASC_rh * df.is_indiv * (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,1,1,1 -Bike tour mode ASC -- walk,@bike_ASC_walk * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, -Bike tour mode ASC -- ride hail,@bike_ASC_rh * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 -Walk-transit tour mode ASC -- shared ride 2,@walktransit_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- shared ride 3+,@walktransit_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- walk,@walktransit_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- ride hail,@walk_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,1,1,1 -Drive to Transit tour mode ASC -- ride hail,@drive_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_taxi * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_single * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_shared * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 -#Walk-transit tour mode ASC -- walk-transit - reference mode,@walktransit_ASC_walktransit * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, -#PNR tour mode ASC -- PNR - reference mode,@pnr_ASC_pnr * (df.is_indiv & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, -#KNR tour mode ASC -- KNR - reference mode,@knr_ASC_knr * (df.is_indiv & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, -#joint tour ASCs,,,,,,,,,,,,,,,,,,, -#joint - SR2 tour mode ASC -- shared ride 2 - reference mode,@joint_sr2_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, -joint - SR2 tour mode ASC -- walk,@joint_sr2_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, -#joint - SR3 tour mode ASC -- shared ride 3+ - reference mode,@joint_sr3p_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, -joint - SR3 tour mode ASC -- shared ride 2,@joint_sr3p_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, -joint - SR3 tour mode ASC -- walk,@joint_sr3p_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, -joint - auto tour mode ASC -- ride hail,@joint_auto_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,1,1 -joint - Bike tour mode ASC -- walk,@joint_bike_ASC_walk * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, -joint - Bike tour mode ASC -- ride hail,@joint_bike_ASC_rh * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 -joint - Walk-transit tour mode ASC -- shared ride 2,@joint_walktransit_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, -joint - Walk-transit tour mode ASC -- shared ride 3+,@joint_walktransit_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, -joint - Walk-transit tour mode ASC -- walk,@joint_walktransit_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, -joint - Walk to Transit tour mode ASC -- ride hail,@joint_walk_transit_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,,,,,,,,,,,1,1,1 -joint - Drive to Transit tour mode ASC -- ride hail,@joint_drive_transit_ASC_rh * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_taxi * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_single * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_shared * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 -#joint - Walk-transit tour mode ASC -- walk-transit - reference mode,@joint_walktransit_ASC_walktransit * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, -#joint - PNR tour mode ASC -- PNR - reference mode,@joint_pnr_ASC_pnr * (df.is_joint & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, -#joint - KNR tour mode ASC -- KNR - reference mode,@joint_knr_ASC_knr * (df.is_joint & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, -#,,,,,,,,,,,,,,,,,,, -Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,-999,,,,,,,,,,,,,, -Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,-999,,,,,,,,,,,,, -Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,1,1,1,1,1,,,,,,,,,, -#Walk-express penalty for intermediate stops,@c_walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,1,,,,,,,,,, -#School Bus,,,,,,,,,,,,,,,,,,, -School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,-999,,, -School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,-999,,, -School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,-999,,, -School Bus - In-vehicle time (20 miles per hour),@c_ivt * odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,1,,, -School Bus - Walk Time,@c_wacc*10,,,,,,,,,,,,,,,1,,, -School Bus - Wait Time,@c_short_i_wait*10,,,,,,,,,,,,,,,1,,, -School Bus tour mode ASC -- shared ride 2,@schoolbus_ASC_sr2 * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,1,,,,,,,,,,,,,,,, -School Bus tour mode ASC -- shared ride 3+,@schoolbus_ASC_sr3p * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,1,,,,,,,,,,,,,,, -School Bus tour mode ASC -- walk,@schoolbus_ASC_walk * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,1,,,,,,,,,,,,,, -local_ASC,@local_ASC,,,,,,1,,,1,,,1,,,,,, -premium_ASC,@premium_ASC,,,,,,,1,,,1,,,1,,,,, -mix_ASC,@mix_ASC,,,,,,,,1,,,1,,,1,,,, -Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,,,,1,1,1,1,1,1,1,,,,1,1 -TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,1 diff --git a/activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv deleted file mode 100755 index 1c557d499..000000000 --- a/activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv +++ /dev/null @@ -1,89 +0,0 @@ -Description,Target,Expression, -,is_joint,(df.number_of_participants > 1), -,is_indiv,(df.number_of_participants == 1), -,is_atwork_subtour,~df.parent_tour_id.isnull(), -,is_school,"(df.primary_purpose=='school') & (df.purpose.isin(['school', 'Home']))", -,c_cost,(0.60 * c_ivt) / df.value_of_time, -#,,, -#atwork subtours,,, -#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,,, -,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')", -,work_tour_is_SOV,parent_tour_mode.isin(['DRIVEALONE']), -,work_tour_is_bike,parent_tour_mode=='BIKE', -#,,, -,i_tour_mode,df.tour_mode.map(I_MODE_MAP), -,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES), -,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES), -,tour_mode_is_walk,i_tour_mode.isin([I_WALK_MODE]), -,tour_mode_is_bike,i_tour_mode.isin([I_BIKE_MODE]), -,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES), -,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES), -,tour_mode_not_drive_transit,~tour_mode_is_drive_transit, -,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES), -,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES), -,tour_mode_is_school_bus,i_tour_mode.isin(I_SCHOOLBUS_MODE), -,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES), -#,,, -,inbound,~df.outbound, -,first_trip,df.trip_num == 1, -,last_trip,df.trip_num == df.trip_count, -origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))", -dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))", -,total_terminal_time,_origin_terminal_time + _dest_terminal_time, -#,,, -,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work, -,dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[DESTINATION])", -,origin_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[ORIGIN])", -,origin_duration,"np.where(first_trip, np.where(inbound,df.duration/2 * ~free_parking_available,0), 1)", -,dest_duration,"np.where(last_trip, np.where(inbound, df.duration/2 * ~free_parking_available, 0), 1)", -,origin_parking_cost,origin_duration*origin_hourly_peak_parking_cost, -,dest_parking_cost,dest_duration*dest_hourly_peak_parking_cost, -,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0, -,trip_topology,"np.where(df.outbound, reindex(land_use.TOPOLOGY, df[DESTINATION]), reindex(land_use.TOPOLOGY, df[ORIGIN]))", -,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))", -,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))", -# FIXME no transit subzones so all zones short walk to transit,,, -,_walk_transit_origin,True, -,_walk_transit_destination,True, -,walk_transit_available,_walk_transit_origin & _walk_transit_destination & (tour_mode_not_drive_transit), -,pnr_transit_available,tour_mode_is_pnr_transit, -,knr_transit_available,tour_mode_is_knr_transit, -,origin_walk_time,shortWalk*60/walkSpeed, -,destination_walk_time,shortWalk*60/walkSpeed, -# RIDEHAIL,,, -,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)", -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -#,,, -,sov_available,(odt_skims['SOV_TIME']>0) & tour_mode_not_drive_transit, -,hov2_available,(odt_skims['HOV2_TIME']>0) & tour_mode_not_drive_transit, -,hov3_available,(odt_skims['HOV3_TIME']>0) & tour_mode_not_drive_transit, -,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0), -,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0), -,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0), -,pnr_local_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_LOC_IVT']>0), -,pnr_local_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_LOC_IVT']>0), -,pnr_premium_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_PRM_IVT']>0), -,pnr_premium_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_PRM_IVT']>0), -,pnr_mix_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_MIX_IVT']>0), -,pnr_mix_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_MIX_IVT']>0), -,knr_local_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_LOC_IVT']>0), -,knr_local_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_LOC_IVT']>0), -,knr_premium_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_PRM_IVT']>0), -,knr_premium_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_PRM_IVT']>0), -,knr_mix_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_MIX_IVT']>0), -,knr_mix_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_MIX_IVT']>0), -#,od_dist_walk,od_skims['DISTWALK'], -#,do_dist_walk,od_skims.reverse('DISTWALK'), -#,max_dist_walk,od_skims.max('DISTWALK'), -#,dist_bike,od_skims['DISTBIKE'], -#,dist_only,od_skims['DIST'], diff --git a/activitysim/examples/prototype_semcog/configs/work_from_home.yaml b/activitysim/examples/prototype_semcog/configs/work_from_home.yaml deleted file mode 100755 index 623c57b35..000000000 --- a/activitysim/examples/prototype_semcog/configs/work_from_home.yaml +++ /dev/null @@ -1,23 +0,0 @@ - -SPEC: work_from_home.csv -COEFFICIENTS: work_from_home_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - -WORK_FROM_HOME_ALT: 0 - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_worker - -# iterative what-if analysis example -# omit these settings to not iterate -# WORK_FROM_HOME_ITERATIONS: 3 -# WORK_FROM_HOME_CHOOSER_FILTER: is_worker -# WORK_FROM_HOME_TARGET_PERCENT: 0.1 -# WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE: 0.01 -# WORK_FROM_HOME_COEFFICIENT_CONSTANT: coef_work_from_home_constant - -preprocessor: - SPEC: work_from_home_annotate_persons_preprocessor - DF: persons diff --git a/activitysim/examples/prototype_semcog/configs/workplace_location.csv b/activitysim/examples/prototype_semcog/configs/workplace_location.csv deleted file mode 100755 index d065c3e01..000000000 --- a/activitysim/examples/prototype_semcog/configs/workplace_location.csv +++ /dev/null @@ -1,16 +0,0 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist,Distance,@_DIST,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged -util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc -util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc -util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc -util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time -util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 -util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 diff --git a/activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv b/activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv deleted file mode 100755 index f3e7c9df5..000000000 --- a/activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv +++ /dev/null @@ -1,11 +0,0 @@ -coefficient_name,value,constrain -coef_dist,-0.063338148,F -coef_dist_squared,0.001294031,F -coef_dist_cubed,-6.70E-05,F -coef_dist_logged,-0.479603662,F -coef_dist_low_inc,-0.029396589,F -coef_dist_med_inc,-0.024858401,F -coef_dist_high_inc,-0.013891636,F -coef_dist_part_time,-0.047762376,F -coef_dist_student,-0.024044614,F -coef_mode_logsum,0.25,F diff --git a/activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv b/activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv deleted file mode 100755 index 22e3251d6..000000000 --- a/activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv +++ /dev/null @@ -1,14 +0,0 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist,Distance,@_DIST,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged -util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc -util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc -util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc -util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time -util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 diff --git a/activitysim/examples/prototype_semcog/data/households.csv b/activitysim/examples/prototype_semcog/data/households.csv deleted file mode 100644 index 1afef6545..000000000 --- a/activitysim/examples/prototype_semcog/data/households.csv +++ /dev/null @@ -1,19943 +0,0 @@ -household_id,legacy_household_id,building_id,race_id,age_of_head,children,persons,cars,TYPE,HINCP,ADJINC,HHT,zone_id -1216141,1630051320,8188086,2,38,5.0,6,1.0,1,48600.0,1009585,3.0,152 -1216142,1630051321,8182316,2,41,2.0,5,2.0,1,31000.0,1025215,3.0,152 -1216143,1630051322,8194596,2,44,1.0,4,1.0,1,33790.0,1009585,3.0,152 -1216144,1630051323,8187180,2,44,1.0,3,1.0,1,33790.0,1009585,3.0,152 -1216145,1630051324,8187452,2,36,3.0,6,1.0,1,31330.0,1001264,1.0,152 -1216146,1630051325,8189715,2,36,3.0,6,1.0,1,31330.0,1001264,1.0,152 -1216147,1630051326,8197056,2,40,-9.0,3,1.0,1,34900.0,1042852,1.0,152 -1216148,1630051327,8183466,2,44,1.0,3,2.0,1,57000.0,1009585,1.0,152 -1216149,1630051328,8182509,2,37,-9.0,2,1.0,1,35500.0,1009585,3.0,152 -1216150,1630051329,8183217,2,42,2.0,4,1.0,1,87000.0,1009585,3.0,152 -1216151,1630051330,8186920,2,35,2.0,4,2.0,1,88500.0,1025215,1.0,152 -1216152,1630051331,8197782,2,35,2.0,4,2.0,1,88500.0,1025215,1.0,152 -1216153,1630051332,8188491,2,33,1.0,3,3.0,1,97000.0,1009585,2.0,152 -1216154,1630051333,8902331,2,41,1.0,3,2.0,1,75300.0,1073094,1.0,152 -1216155,1630051334,8183734,2,35,3.0,4,1.0,1,20000.0,1073094,3.0,152 -1216156,1630051335,8187061,2,36,3.0,4,1.0,1,25700.0,1009585,3.0,152 -1216157,1630051336,8185775,2,36,3.0,4,1.0,1,25700.0,1009585,3.0,152 -1216158,1630051337,8183575,2,34,-9.0,1,1.0,1,8920.0,1009585,4.0,152 -1216159,1630051338,8186218,2,30,-9.0,1,1.0,1,8000.0,1025215,4.0,152 -1216160,1630051339,8192260,2,31,-9.0,1,2.0,1,0.0,1073094,6.0,152 -1216161,1630051340,8193513,2,32,1.0,3,1.0,1,10000.0,1009585,1.0,152 -1216162,1630051341,8185333,2,32,1.0,3,1.0,1,10000.0,1009585,1.0,152 -1216163,1630051342,8185867,2,44,1.0,3,1.0,1,12200.0,1025215,1.0,152 -1216164,1630051343,8186967,2,36,1.0,3,2.0,1,29800.0,1073094,7.0,152 -1216165,1630051344,8169113,2,31,-9.0,2,1.0,1,29500.0,1025215,1.0,152 -1216166,1630051345,8187228,2,36,-9.0,2,2.0,1,17200.0,1042852,1.0,152 -1216167,1630051346,8192597,2,47,2.0,4,1.0,1,30300.0,1025215,2.0,152 -1216168,1630051347,8184295,2,57,1.0,3,2.0,1,41730.0,1042852,1.0,152 -1216169,1630051348,8187715,2,60,-9.0,1,1.0,1,30000.0,1042852,6.0,152 -1216170,1630051349,8188208,2,55,-9.0,1,1.0,1,58000.0,1009585,6.0,152 -1216171,1630051350,8183680,2,55,-9.0,3,2.0,1,38900.0,1001264,1.0,152 -1216172,1630051351,8186031,2,60,2.0,4,1.0,1,52800.0,1009585,3.0,152 -1216173,1630051352,8187268,2,50,2.0,4,1.0,1,41500.0,1009585,3.0,152 -1216174,1630051353,8182871,2,59,1.0,4,2.0,1,68000.0,1009585,1.0,152 -1216175,1630051354,8186850,2,61,-9.0,3,2.0,1,92600.0,1001264,1.0,152 -1216176,1630051355,8194820,2,56,-9.0,1,0.0,1,0.0,1025215,6.0,152 -1216177,1630051356,8183468,2,50,-9.0,1,0.0,1,2500.0,1001264,6.0,152 -1216178,1630051357,8178935,2,60,-9.0,1,1.0,1,13000.0,1042852,6.0,152 -1216179,1630051358,8182538,2,54,-9.0,1,1.0,1,13700.0,1042852,6.0,152 -1216180,1630051359,8186832,2,52,-9.0,1,1.0,1,1200.0,1009585,4.0,152 -1216181,1630051360,8187942,2,49,-9.0,1,1.0,1,0.0,1001264,6.0,152 -1216182,1630051361,8187740,2,63,-9.0,1,1.0,1,12000.0,1001264,6.0,152 -1216183,1630051362,8182404,2,60,-9.0,1,1.0,1,14300.0,1001264,6.0,152 -1216184,1630051363,8181016,2,51,-9.0,1,1.0,1,1700.0,1042852,6.0,152 -1216185,1630051364,8184578,2,50,-9.0,2,1.0,1,21900.0,1073094,3.0,152 -1216186,1630051365,8181016,2,48,-9.0,2,1.0,1,22150.0,1009585,3.0,152 -1216187,1630051366,8181016,2,48,-9.0,2,1.0,1,22150.0,1009585,3.0,152 -1216188,1630051367,8185033,2,50,-9.0,1,1.0,1,25000.0,1009585,6.0,152 -1216189,1630051368,8188867,2,51,-9.0,4,1.0,1,26800.0,1025215,5.0,152 -1216190,1630051369,8185924,2,51,-9.0,4,1.0,1,26800.0,1025215,5.0,152 -1216191,1630051370,8915415,2,57,-9.0,3,1.0,1,16500.0,1042852,1.0,152 -1216192,1630051371,8188408,2,59,-9.0,3,3.0,1,13400.0,1025215,1.0,152 -1216193,1630051372,8179132,2,48,-9.0,2,4.0,1,11490.0,1009585,5.0,152 -1216194,1630051373,8194752,2,80,1.0,4,2.0,1,54000.0,1025215,1.0,152 -1216195,1630051374,8186261,2,85,-9.0,1,1.0,1,35100.0,1001264,6.0,152 -1216196,1630051375,8181932,2,76,-9.0,1,1.0,1,39700.0,1001264,6.0,152 -1216197,1630051376,8197545,2,79,-9.0,1,1.0,1,40160.0,1001264,5.0,152 -1216198,1630051377,8186861,2,79,-9.0,2,1.0,1,40160.0,1001264,5.0,152 -1216199,1630051378,8187249,2,74,-9.0,2,2.0,1,32600.0,1042852,3.0,152 -1216200,1630051379,8182505,2,70,-9.0,3,2.0,1,56180.0,1009585,3.0,152 -1216201,1630051380,8184029,4,67,2.0,4,1.0,1,26850.0,1042852,3.0,152 -1216202,1630051381,8182223,2,77,-9.0,1,0.0,1,8800.0,1025215,4.0,152 -1216203,1630051382,8184805,2,68,-9.0,1,0.0,1,8700.0,1009585,6.0,152 -1216204,1630051383,8184831,2,94,-9.0,1,1.0,1,12040.0,1009585,6.0,152 -1216205,1630051384,8197730,2,87,-9.0,1,1.0,1,19200.0,1073094,6.0,152 -1216206,1630051385,8194087,2,68,-9.0,1,1.0,1,1500.0,1025215,4.0,152 -1216207,1630051386,8191287,2,73,-9.0,1,1.0,1,18700.0,1073094,6.0,152 -1216208,1630051387,8181016,2,70,-9.0,1,1.0,1,15040.0,1009585,6.0,152 -1216209,1630051388,8187739,2,73,-9.0,1,1.0,1,15600.0,1001264,6.0,152 -1216210,1630051389,8186873,2,71,-9.0,1,1.0,1,23600.0,1042852,6.0,152 -1216211,1630051390,8181016,2,68,-9.0,1,1.0,1,25000.0,1073094,6.0,152 -1216212,1630051391,8169113,2,87,-9.0,1,1.0,1,14900.0,1025215,6.0,152 -1216213,1630051392,8186697,2,77,-9.0,1,1.0,1,0.0,1042852,6.0,152 -1216214,1630051393,8184217,2,75,-9.0,1,1.0,1,11400.0,1073094,6.0,152 -1216215,1630051394,8187153,2,75,-9.0,1,1.0,1,6600.0,1073094,6.0,152 -1216216,1630051395,8184641,2,94,-9.0,1,1.0,1,12040.0,1009585,6.0,152 -1216217,1630051396,8169113,2,72,-9.0,1,1.0,1,2800.0,1073094,6.0,152 -1216218,1630051397,8197324,4,86,-9.0,1,1.0,1,12900.0,1009585,6.0,152 -1216219,1630051398,8186561,2,88,-9.0,1,2.0,1,2500.0,1001264,4.0,152 -1216220,1630051399,8181659,2,65,-9.0,3,1.0,1,24400.0,1042852,3.0,152 -1216221,1630051400,8182882,2,76,-9.0,2,0.0,1,12400.0,1025215,3.0,152 -1216222,1630051401,8187665,2,86,-9.0,2,1.0,1,14100.0,1025215,3.0,152 -1216223,1630051402,8178833,2,71,-9.0,2,1.0,1,21300.0,1001264,3.0,152 -1216224,1630051403,8187308,2,82,-9.0,2,2.0,1,0.0,1025215,1.0,152 -1216225,1630051404,8196849,2,28,-9.0,1,1.0,1,37000.0,1001264,6.0,152 -1216226,1630051405,8193223,2,42,-9.0,1,1.0,1,55000.0,1025215,4.0,152 -1216227,1630051406,8196382,2,40,2.0,4,1.0,1,43600.0,1073094,1.0,152 -1216228,1630051407,8915428,2,37,2.0,4,1.0,1,41000.0,1001264,1.0,152 -1216229,1630051408,8184028,2,36,1.0,4,2.0,1,59600.0,1009585,3.0,152 -1216230,1630051409,8191121,4,41,1.0,4,3.0,1,52000.0,1042852,1.0,152 -1216231,1630051410,8197678,2,29,1.0,3,1.0,1,42970.0,1042852,2.0,152 -1216232,1630051411,8915427,2,38,3.0,4,0.0,1,15000.0,1001264,3.0,152 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deleted file mode 100644 index 538f9d3f9..000000000 --- a/activitysim/examples/prototype_semcog/scripts/reindex_household_ids.py +++ /dev/null @@ -1,192 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. - -""" -reindex household_id in households and persons tables -legacy tables have household_ids starting at 930000000 -which causes headaches for activitysim's automatic generation of trip and tour ids based on hosuehold_id -(predictable trip and tour ids are used for repeatable random number stream generation) -""" - -import os -import sys - -import numpy as np -import pandas as pd - -if sys.version_info[0] < 3: - raise Exception("Must be using Python 3") - -file_names = { - "households": "households.csv", - "persons": "persons.csv", - "land_use": "land_use.csv", -} - -land_use_zone_col = "ZONE" -hh_zone_col = "zone_id" - - -def drop_and_dump(df, drop, msg, tag, output_dir): - - print("Checking for %s" % msg) - if drop.any(): - print( - "WARNING: dropping %s out of %s %s (%s)" % (drop.sum(), len(df), msg, tag) - ) - df[drop].to_csv(os.path.join(output_dir, "%s.csv" % tag), index=False) - df = df[~drop] - - return df - - -def create_subset(input_dir, output_dir, drop_dir): - - ### - # land_use - ### - land_use_df = pd.read_csv(os.path.join(input_dir, file_names["land_use"])) - land_use_df = land_use_df.sort_values(by=land_use_zone_col) - land_use_df.to_csv(os.path.join(output_dir, file_names["land_use"]), index=False) - - print("zones: %s" % len(land_use_df)) - - ### - # households - ### - - households = pd.read_csv( - os.path.join(input_dir, file_names["households"]), - dtype={"household_id": np.int64}, - ) - households = households.sort_values(by="household_id") - households.rename(columns={"household_id": "legacy_household_id"}, inplace=True) - - raw_household_count = len(households) - - # all households must have a zone_id - null_zones = households[hh_zone_col].isnull() - households = drop_and_dump( - households, - null_zones, - msg="households with null zones", - tag="households_with_null_zones", - output_dir=drop_dir, - ) - households[hh_zone_col] = households[hh_zone_col].astype(np.int64) - - # all households zone_ids must be in land_use - orphan_zones = ~households[hh_zone_col].isin(land_use_df[land_use_zone_col]) - households = drop_and_dump( - households, - orphan_zones, - msg="households with unknown zones", - tag="households_with_unknown_zones", - output_dir=drop_dir, - ) - - # reindexed household_id as both index and column - households.index = np.arange(1, len(households) + 1) - households["household_id"] = households.index - - ### - # persons - ### - persons = pd.read_csv( - os.path.join(input_dir, file_names["persons"]), - dtype={"household_id": np.int64, "person_id": np.int64}, - ) - persons = persons.sort_values(by=["household_id", "member_id"]) - persons.rename( - columns={ - "person_id": "legacy_person_id", - "household_id": "legacy_household_id", - }, - inplace=True, - ) - persons.legacy_household_id = persons.legacy_household_id.astype(np.int64) - - raw_person_count = len(persons) - - assert not persons.legacy_household_id.isnull().any() - - orphan_persons = ~persons.legacy_household_id.isin(households.legacy_household_id) - persons = drop_and_dump( - persons, - orphan_persons, - msg="persons without households", - tag="persons_without_households", - output_dir=drop_dir, - ) - - persons = pd.merge( - persons, - households[["legacy_household_id", "household_id"]], - left_on="legacy_household_id", - right_on="legacy_household_id", - how="left", - ) - assert not persons.household_id.isnull().any() - persons.household_id = persons.household_id.astype(np.int64) - - # reindexed person_id as both index and column - persons.index = np.arange(1, len(persons) + 1) - persons["person_id"] = persons.index - - # check that we have the right number of persons in every household" - assert (persons.groupby("household_id").size() == households.persons).all() - - # check that all persons in household have different member_id" - persons_with_dupe_member_id = persons.duplicated( - ["household_id", "member_id"], keep="first" - ) - household_ids_with_dupe_member_id = persons.household_id[ - persons_with_dupe_member_id - ].unique() - households_with_dupe_members = households.household_id.isin( - household_ids_with_dupe_member_id - ) - persons_in_households_with_dupe_members = persons.household_id.isin( - household_ids_with_dupe_member_id - ) - - print( - "%s of %s persons_with_dupe_member_id" - % (persons_with_dupe_member_id.sum(), len(persons)) - ) - persons = drop_and_dump( - persons, - persons_in_households_with_dupe_members, - msg="persons in households with duplicate (household_id, member_id)", - tag="persons_in_households_with_dupe_member_id", - output_dir=drop_dir, - ) - - households = drop_and_dump( - households, - households_with_dupe_members, - msg="households with duplicate persons.member_id", - tag="households_with_dupe_member_id", - output_dir=drop_dir, - ) - - missing_member1 = ~households.household_id.isin( - persons.household_id[persons.member_id == 1] - ) - # print("%s of %s households missing member_id 1" % (missing_member1.sum(), len(households))) - assert not missing_member1.any() - - print( - "Writing %s households. Dropped %s" - % (len(households), raw_household_count - len(households)) - ) - households.to_csv(os.path.join(output_dir, file_names["households"]), index=False) - - print( - "Writing %s persons. Dropped %s" - % (len(persons), raw_person_count - len(persons)) - ) - persons.to_csv(os.path.join(output_dir, file_names["persons"]), index=False) - - -create_subset(input_dir="data_raw/", output_dir="data/", drop_dir="data_raw/dropped") diff --git a/activitysim/examples/prototype_semcog/test/configs/settings.yaml b/activitysim/examples/prototype_semcog/test/configs/settings.yaml deleted file mode 100644 index 94653b605..000000000 --- a/activitysim/examples/prototype_semcog/test/configs/settings.yaml +++ /dev/null @@ -1,33 +0,0 @@ -inherit_settings: True - -# EXAMPLE_HAS_WARNINGS -strict: False - -# number of households to simulate -households_sample_size: 10 -# simulate all households -# households_sample_size: 0 - -chunk_size: 0 - -# - shadow pricing global switches -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only) -want_dest_choice_presampling: True - -# - tracing -# trace household id; comment out or leave empty for no trace -trace_hh_id: - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -# trace_od: [5, 11] -trace_od: - diff --git a/activitysim/examples/prototype_semcog/test/output/.gitignore b/activitysim/examples/prototype_semcog/test/output/.gitignore deleted file mode 100644 index bf5bf15e3..000000000 --- a/activitysim/examples/prototype_semcog/test/output/.gitignore +++ /dev/null @@ -1,7 +0,0 @@ -*.csv -*.log -*.prof -*.h5 -*.txt -*.yaml -*.omx diff --git a/activitysim/examples/prototype_semcog/test/output/cache/.gitignore b/activitysim/examples/prototype_semcog/test/output/cache/.gitignore deleted file mode 100644 index 3dd2e62f9..000000000 --- a/activitysim/examples/prototype_semcog/test/output/cache/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -*.mmap -*.feather diff --git a/activitysim/examples/prototype_semcog/test/output/trace/.gitignore b/activitysim/examples/prototype_semcog/test/output/trace/.gitignore deleted file mode 100644 index 8edb80678..000000000 --- a/activitysim/examples/prototype_semcog/test/output/trace/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -*.csv -*.log -*.txt diff --git a/activitysim/examples/prototype_semcog/test/regress/final_trips.csv b/activitysim/examples/prototype_semcog/test/regress/final_trips.csv deleted file mode 100644 index 42f97f336..000000000 --- a/activitysim/examples/prototype_semcog/test/regress/final_trips.csv +++ /dev/null @@ -1,55 +0,0 @@ -trip_id,person_id,household_id,primary_purpose,trip_num,outbound,trip_count,destination,origin,tour_id,purpose,destination_logsum,depart,trip_mode,mode_choice_logsum -1015768905,3096856,1247711,othmaint,1,True,1,155,170,126971113,othmaint,,24,SHARED3,-0.5224065888961006 -1015768909,3096856,1247711,othmaint,1,False,1,170,155,126971113,home,,24,SHARED3,-0.5214112548235572 -1015768937,3096858,1247711,social,1,True,1,170,170,126971117,social,,29,SHARED2,0.018380193040923982 -1015768941,3096858,1247711,social,1,False,1,170,170,126971117,home,,30,SHARED2,0.018380193040923982 -1015769409,3096857,1247711,work,1,True,1,158,170,126971176,work,,9,BIKE,-1.551361834854566 -1015769413,3096857,1247711,work,1,False,1,170,158,126971176,home,,32,BIKE,-1.5954322251021176 -1015770001,3096859,1247711,school,1,True,1,160,170,126971250,school,,10,SCHOOLBUS,-1.106635583043288 -1015770005,3096859,1247711,school,1,False,1,170,160,126971250,home,,28,WALK,-3.5252223368334636 -1015770153,3096860,1247711,escort,1,True,2,197,170,126971269,shopping,9.340663518005645,10,SHARED3,-0.38521051166029324 -1015770154,3096860,1247711,escort,2,True,2,169,197,126971269,escort,,10,SHARED2,-0.5143323902010828 -1015770157,3096860,1247711,escort,1,False,1,170,169,126971269,home,,11,SHARED3,0.1252561224106634 -1015770281,3096860,1247711,othdiscr,1,True,1,165,170,126971285,othdiscr,,23,WALK,-1.0876453989916266 -1015770285,3096860,1247711,othdiscr,1,False,3,167,165,126971285,escort,9.513677266467274,27,WALK,-0.4332948711405552 -1015770286,3096860,1247711,othdiscr,2,False,3,163,167,126971285,escort,10.062635404175836,28,WALK,-0.5801291209667215 -1015770287,3096860,1247711,othdiscr,3,False,3,170,163,126971285,home,,29,WALK,-1.0333637435819232 -1015770481,3096861,1247711,escort,1,True,1,209,170,126971310,escort,,22,WALK,-1.572053385843179 -1015770485,3096861,1247711,escort,1,False,1,170,209,126971310,home,,22,WALK,-1.4752747100222015 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-1018799537,3106096,1252993,eatout,1,True,1,215,178,127349942,eatout,,45,DRIVEALONE,-0.16384130314024997 -1018799541,3106096,1252993,eatout,1,False,1,178,215,127349942,home,,46,DRIVEALONE,-0.18543271322870275 -1018799561,3106096,1252993,escort,1,True,1,197,178,127349945,escort,,42,SHARED2,-0.09023792542355757 -1018799565,3106096,1252993,escort,1,False,1,178,197,127349945,home,,42,SHARED2,-0.08937318354292034 -1018799737,3106096,1252993,univ,1,True,3,162,178,127349967,eatout,7.30848970144714,8,WALK,-0.599726966285296 -1018799738,3106096,1252993,univ,2,True,3,163,162,127349967,escort,7.51338406324683,12,WALK,-0.6826425039403304 -1018799739,3106096,1252993,univ,3,True,3,175,163,127349967,univ,,25,WALK,-1.3004756476179076 -1018799741,3106096,1252993,univ,1,False,1,178,175,127349967,home,,40,WALK,-0.8656871872367368 -1020377481,3110906,1256006,work,1,True,1,174,173,127547185,work,,11,DRIVEALONE,0.015214549190366217 -1020377485,3110906,1256006,work,1,False,1,173,174,127547185,home,,33,DRIVEALONE,0.015220933756621136 -1020377745,3110907,1256006,school,1,True,1,154,173,127547218,school,,9,SHARED2,-0.18292193406062562 -1020377749,3110907,1256006,school,1,False,1,173,154,127547218,home,,26,SHARED2,-0.1788225634720692 -1020378073,3110908,1256006,school,1,True,1,173,173,127547259,school,,8,SCHOOLBUS,-0.6001706576288278 -1020378077,3110908,1256006,school,1,False,2,167,173,127547259,social,5.486995392285553,21,TAXI,-2.1471364017050782 -1020378078,3110908,1256006,school,2,False,2,173,167,127547259,home,,22,WALK,-2.1510598454458765 -1021352137,3113878,1258160,shopping,1,True,1,208,213,127669017,shopping,,31,SHARED2,0.2342801115423382 -1021352141,3113878,1258160,shopping,1,False,1,213,208,127669017,home,,33,SHARED2,0.21393799448899808 -1021352273,3113878,1258160,social,1,True,1,200,213,127669034,social,,33,WALK,-0.5375736471412192 -1021352277,3113878,1258160,social,1,False,1,213,200,127669034,home,,33,WALK,-0.515560757105025 -1021352297,3113878,1258160,work,1,True,1,149,213,127669037,work,,9,DRIVEALONE,-0.022708832857146595 -1021352301,3113878,1258160,work,1,False,1,213,149,127669037,home,,26,DRIVEALONE,-0.027889305701053062 -1021563089,3114521,1258716,othdiscr,1,True,1,184,198,127695386,othdiscr,,14,WALK,-2.200526355762286 -1021563093,3114521,1258716,othdiscr,1,False,1,198,184,127695386,home,,18,WALK,-2.2005370524197625 -1060133985,3232115,1302573,shopping,1,True,1,172,159,132516748,shopping,,16,TNC_SHARED,-0.3624498540609999 -1060133989,3232115,1302573,shopping,1,False,2,215,172,132516748,othdiscr,12.243546183861788,19,DRIVEALONE,0.13824405758422004 -1060133990,3232115,1302573,shopping,2,False,2,159,215,132516748,home,,19,DRIVEALONE,-0.4253274747222848 -1537045593,4686114,1905064,othdiscr,1,True,1,180,155,192130699,othdiscr,,27,WALK_PRM,-0.19429825736820502 -1537045597,4686114,1905064,othdiscr,1,False,1,155,180,192130699,home,,34,WALK_PRM,-0.32654811068511325 diff --git a/activitysim/examples/prototype_semcog/test/test_semcog.py b/activitysim/examples/prototype_semcog/test/test_semcog.py deleted file mode 100644 index e941238c3..000000000 --- a/activitysim/examples/prototype_semcog/test/test_semcog.py +++ /dev/null @@ -1,59 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -import os -import subprocess -import sys - -import pandas as pd -import pandas.testing as pdt -import pkg_resources - -from activitysim.core import inject - - -def teardown_function(func): - inject.clear_cache() - inject.reinject_decorated_tables() - - -def test_semcog(): - def example_path(dirname): - resource = os.path.join("examples", "prototype_semcog", dirname) - return pkg_resources.resource_filename("activitysim", resource) - - def test_path(dirname): - return os.path.join(os.path.dirname(__file__), dirname) - - def regress(): - regress_trips_df = pd.read_csv(test_path("regress/final_trips.csv")) - final_trips_df = pd.read_csv(test_path("output/final_trips.csv")) - - # person_id,household_id,tour_id,primary_purpose,trip_num,outbound,trip_count,purpose, - # destination,origin,destination_logsum,depart,trip_mode,mode_choice_logsum - # compare_cols = [] - pdt.assert_frame_equal(final_trips_df, regress_trips_df) - - file_path = os.path.join(os.path.dirname(__file__), "../simulation.py") - - run_args = [ - "-c", - test_path("configs"), - "-c", - example_path("configs"), - "-d", - example_path("data"), - "-o", - test_path("output"), - ] - - if os.environ.get("GITHUB_ACTIONS") == "true": - subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) - else: - subprocess.run([sys.executable, file_path] + run_args, check=True) - - regress() - - -if __name__ == "__main__": - - test_semcog() diff --git a/conda-environments/activitysim-dev-base.yml b/conda-environments/activitysim-dev-base.yml index f0bdcb3a3..1e63b621d 100644 --- a/conda-environments/activitysim-dev-base.yml +++ b/conda-environments/activitysim-dev-base.yml @@ -19,8 +19,8 @@ dependencies: - black >= 22.0,<23 - bump2version # for making a release - coveralls -- cytoolz >= 0.8.1 -- dask +- cytoolz = 0.12.* +- dask = 2023.3.* - descartes - filelock - fsspec @@ -30,44 +30,45 @@ dependencies: - ipykernel # so this env will appear in jupyter as a selection - isort - jupyterlab -- larch >= 5.5.8 +- larch = 5.7.* - matplotlib - myst-parser # allows markdown in sphinx - nbconvert - nbformat -- numba >= 0.55.2 +- numba = 0.56.* - numexpr -- numpy >= 1.16.1,<=1.21 +- numpy = 1.23.* - numpydoc -- openmatrix >= 0.3.4.1 -- orca >= 1.6 -- pandas >= 1.1.0 +- openmatrix = 0.3.* +- orca = 1.8 +- pandas = 1.4.* +- platformdirs = 3.2.* - pre-commit -- psutil >= 4.1 -- pyarrow >= 2.0 +- psutil = 5.9.* +- pyarrow = 11.* - pycodestyle -- pydantic +- pydantic = 1.10.* - pydata-sphinx-theme -- pyinstrument -- pypyr +- pyinstrument = 4.4 +- pypyr = 5.8.* - pytables >=3.5.1,<3.7 -- pytest +- pytest = 7.2.* - pytest-cov - pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- rich +- pyyaml = 6.* +- requests = 2.28.* +- rich = 13.3.* - ruby # required for benchmarking pre-commit hooks - setuptools_scm -- scikit-learn >= 1.1 +- scikit-learn = 1.2.* - simwrapper > 1.7 - snakeviz # for profiling -- sphinx -- sphinx_rtd_theme -- sphinx-argparse -- xarray >= 0.21 +- sphinx = 6.1.* +- sphinx_rtd_theme = 1.2.* +- sphinx-argparse = 0.4.* +- xarray = 2023.2.* - xmle -- zarr +- zarr = 2.14.* - pip: - autodoc_pydantic diff --git a/conda-environments/activitysim-dev.yml b/conda-environments/activitysim-dev.yml index ea1a36ab7..cac073df6 100644 --- a/conda-environments/activitysim-dev.yml +++ b/conda-environments/activitysim-dev.yml @@ -15,8 +15,8 @@ dependencies: - black >= 22.0,<23 - bump2version # for making a release - coveralls -- cytoolz >= 0.8.1 -- dask +- cytoolz = 0.12.* +- dask = 2023.3.* - descartes - filelock - fsspec @@ -26,45 +26,46 @@ dependencies: - ipykernel # so this env will appear in jupyter as a selection - isort - jupyterlab -- larch >= 5.5.8 +- larch = 5.7.* - matplotlib - myst-parser # allows markdown in sphinx - nbconvert - nbformat -- numba >= 0.56.4 +- numba = 0.56.* - numexpr -- numpy >= 1.16.1 +- numpy = 1.23.* - numpydoc -- openmatrix >= 0.3.4.1 -- orca >= 1.6 -- pandas >= 1.1.0 +- openmatrix = 0.3.* +- orca = 1.8 +- pandas = 1.4.* +- platformdirs = 3.2.* - pre-commit -- psutil >= 4.1 -- pyarrow >= 2.0 +- psutil = 5.9.* +- pyarrow = 11.* - pycodestyle -- pydantic +- pydantic = 1.10.* - pydata-sphinx-theme -- pyinstrument -- pypyr +- pyinstrument = 4.4 +- pypyr = 5.8.* - pytables >=3.5.1,<3.7 -- pytest +- pytest = 7.2.* - pytest-cov - pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- rich +- pyyaml = 6.* +- requests = 2.28.* +- rich = 13.3.* - ruby # required for benchmarking pre-commit hooks - setuptools_scm -- scikit-learn >= 1.1 +- scikit-learn = 1.2.* - sharrow >= 2.5.2 - simwrapper > 1.7 - snakeviz # for profiling -- sphinx -- sphinx_rtd_theme -- sphinx-argparse -- xarray >= 0.21 +- sphinx = 6.1.* +- sphinx_rtd_theme = 1.2.* +- sphinx-argparse = 0.4.* +- xarray = 2023.2.* - xmle -- zarr +- zarr = 2.14.* - pip: - autodoc_pydantic diff --git a/conda-environments/github-actions-tests.yml b/conda-environments/github-actions-tests.yml index 63d353ea5..28d6c51f8 100644 --- a/conda-environments/github-actions-tests.yml +++ b/conda-environments/github-actions-tests.yml @@ -9,26 +9,27 @@ dependencies: - pip - black >= 22.0,<23 - coveralls -- cytoolz >= 0.8.1 +- cytoolz = 0.12.* +- dask = 2023.3.* - isort - nbmake -- numba >= 0.56.4 -- numpy >= 1.16.1 -- openmatrix >= 0.3.4.1 -- orca >= 1.6 -- pandas >= 1.1.0 -- psutil >= 4.1 -- pyarrow >= 2.0 -- pypyr >= 5.3 +- numba = 0.56.* +- numpy = 1.23.* +- openmatrix = 0.3.* +- orca = 1.8 +- pandas = 1.4.* +- platformdirs = 3.2.* +- psutil = 5.9.* +- pyarrow = 11.* +- pypyr = 5.8.* - pytables >= 3.5.1,<3.7 # orca's constraint -- pytest +- pytest = 7.2.* - pytest-cov - pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- scikit-learn >= 1.1 +- pyyaml = 6.* +- requests = 2.28.* +- scikit-learn = 1.2.* - sharrow >= 2.5.2 - simwrapper > 1.7 -- scikit-learn >= 1.1.1 -- xarray >= 0.21 -- zarr +- xarray = 2023.2.* +- zarr = 2.14.* diff --git a/docs/examples.rst b/docs/examples.rst index add56f8b3..f146b1a58 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -48,7 +48,7 @@ The current examples are: +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ | :ref:`prototype_arc` | ARC agency example | 1 | In development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`prototype_semcog` | SEMCOG agency example | 1 | In development | +| :ref:`example_semcog` | SEMCOG agency example | 1 | In production | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ | :ref:`placeholder_psrc` | PSRC agency example | 2 | Future development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ @@ -290,7 +290,7 @@ After the frequency and time-of-day for work and school tours are determined, th next major model component relates to joint household travel. This component produces a number of joint tours by travel purpose for the entire household, travel party composition in terms of adults and children, and then defines the participation of each household -member in each joint household tour. It is followed by choice of destination and time-ofday. +member in each joint household tour. It is followed by choice of destination and time-of-day. The next stage relates to maintenance and discretionary tours that are modeled at the individual person level. The models include tour frequency, choice of destination and time @@ -513,7 +513,6 @@ Configuration This section has been moved to :ref:`configuration`. - .. _sub-model-spec-files: Sub-Model Specification Files @@ -524,7 +523,7 @@ Python/pandas/numpy expressions, alternatives, and other settings used by each m alternatives file since the alternatives are not easily described as columns in the expressions file. An example of this is the ``non_mandatory_tour_frequency_alternatives.csv`` file, which lists each alternative as a row and each columns indicates the number of non-mandatory tours by purpose. The set of files for the prototype_mtc are below. The -:ref:`prototype_arc`, :ref:`prototype_semcog`, and :ref:`prototype_mtc_extended` examples added additional submodels. +:ref:`prototype_arc`, :ref:`example_semcog`, and :ref:`prototype_mtc_extended` examples added additional submodels. +------------------------------------------------+--------------------------------------------------------------------+ | Model | Specification Files | @@ -1485,9 +1484,9 @@ Example See example commands in `example_manifest.yaml `_ for running prototype_arc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _prototype_semcog : +.. _example_semcog : -prototype_semcog +example_semcog ---------------- .. note:: @@ -1495,7 +1494,7 @@ prototype_semcog This example is in development -The prototype_semcog added a :ref:`work_from_home`, :ref:`telecommute_frequency`, :ref:`transit_pass_subsidy` +The example_semcog added a :ref:`work_from_home`, :ref:`telecommute_frequency`, :ref:`transit_pass_subsidy` and :ref:`transit_pass_ownership` submodel. These submodel specification files are below, and are in addition to the :ref:`prototype_mtc` submodel :ref:`sub-model-spec-files`. These submodels were added to prototype_semcog as extensions, which is a way for users to add submodels within their model setup as opposed to formally adding them to the activitysim package. Extension submodels are run through @@ -1527,11 +1526,28 @@ Example SEMCOG Sub-Model Specification Files | | - transit_pass_ownership_coeffs.csv | +------------------------------------------------+--------------------------------------------------------------------+ +Additional extensions were included specifically as part of the example_semcog model to deal with the unique travel of +university students attending the University of Michigan (UofM). First off, UofM students have their school zones resampled +weighted by enrollment. This happens after both school location choice in the university_location_zone_override model and +after university trip destination choice in the trip_destination_univ_zone_override model. Next, parking trips are handled +explicitly by first choosing a parking location if the trip destination is in a UofM zone. Parking locations are selected +proportionally to the parking lot size as part of the parking_location_choice_at_university. Finally explicit trips are +inserted into the trips table to and from the parking lot locations in the stop_frequency_university_parking model. +While a persons vehicle is parked, trip mode choice treats the tour mode as walk-transit to determine trip mode availability +until the person returns back to their car. For more information, please see SEMCOG's final model documentation and the +SEMCOG model user quide. + +These submodels were added to example_semcog as extensions, which is a way for users to add +submodels within their model setup as opposed to formally adding them to the activitysim package. Extension submodels are run through +the `models` settings. However, the model must be run with the `simulation.py` script instead of the command line interface +in order to load the extensions folder. + + Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running prototype_semcog. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running example_semcog. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. .. _placeholder_psrc : diff --git a/docs/models.rst b/docs/models.rst index ef79a38cd..d7249a353 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -1211,8 +1211,8 @@ Core Table: ``trips`` | Result Field: ``purpose, destination`` | Skims Keys: ``o .. _trip_scheduling: -Trip Scheduling (Probablistic) ------------------------------- +Trip Scheduling (Probabilistic) +------------------------------- For each trip, assign a departure hour based on an input lookup table of percents by tour purpose, direction (inbound/outbound), tour hour, and trip index. @@ -1232,6 +1232,41 @@ The trip scheduling model does not use mode choice logsums. Alternatives: Available time periods in the tour window (i.e. tour start and end period). When processing stops on work tours, the available time periods is constrained by the at-work subtour start and end period as well. +In order to avoid trip failing, a new probabilistic trip scheduling mode was developed named "relative". +When setting the _scheduling_mode_ option to relative, trips are scheduled relative to the previously scheduled trips. +The first trip still departs when the tour starts and for every subsequent trip, the choices are selected with respect to +the previous trip depart time. Inbound trips are no longer handled in reverse order. The key to this relative mode is to +index the probabilities based on how much time is remaining on the tour. For tours that include subtours, the time remaining will +be based on the subtour start time for outbound trips and will resume again for inbound trips after the subtour ends. +By indexing the probabilities based on time remaining and scheduling relative to the previous trip, scheduling trips in relative +mode will not fail. Note also that relative scheduling mode requires the use of logic +version 2 (see warning about logic versions, below). + +An example of trip scheduling in relative mode is included in the :ref:`prototype_mwcog` example. In this example, trip +scheduling probabilities are indexed by the following columns: + * periods_left_min: the minimum bin for the number of time periods left on the tour. + * periods_left_max: the maximum bin for the number of time periods left on the tour. This is the same as periods_left_min until the final time period bin. + * outbound: whether the trip occurs on the outbound leg of a tour. + * tour_purpose_grouped: Tour purpose grouped into mandatory and non-mandatory categories + * half_tour_stops_remaining_grouped: The number of stops remaining on the half tour with the categories of 0 and 1+ +Each of these variables are listed as merge columns in the trip_scheduling.yaml file and are declared in the trip scheduling preprocessor. +The variables above attempt to balance the statistics available for probability creation with the amount of segmentation of trip characteristics. + +.. warning:: + + Earlier versions of ActivitySim contained a logic error in this model, whereby + the earliest departure time for inbound legs was bounded by the maximum outbound + departure time, even if there was a scheduling failure for one or more outbound + leg departures and that bound was NA. For continuity, this process has been + retained in this ActivitySim component as *logic_version* 1, and it remains the + default process if the user does not explicitly specify a logic version in the + model settings yaml file. The revised logic includes bounding inbound legs only + when the maximum outbound departure time is well defined. This version of the + model can be used by explicitly setting `logic_version: 2` (or greater) in the + model settings yaml file. It is strongly recommended that all new model + development efforts use logic version 2; a future version of ActivitySim may + make this the default for this component, and/or remove logic version 1 entirely. + The main interface to the trip scheduling model is the :py:func:`~activitysim.abm.models.trip_scheduling.trip_scheduling` function. This function is registered as an Inject step in the example Pipeline. diff --git a/setup.cfg b/setup.cfg index cb3b41388..b2736a99a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -29,7 +29,7 @@ install_requires = numpy >= 1.16.1 openmatrix >= 0.3.4.1 orca >= 1.6 - pandas >= 1.1.0 + pandas >= 1.4, <2 psutil >= 4.1 pyarrow >= 2.0 pypyr >= 5.3 diff --git a/test/auto_ownership/.gitkeep b/test/auto_ownership/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/auto_ownership/configs/annotate_households.csv b/test/auto_ownership/configs/annotate_households.csv new file mode 100644 index 000000000..ad2c67bab --- /dev/null +++ b/test/auto_ownership/configs/annotate_households.csv @@ -0,0 +1,19 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of non_workers,num_non_workers,households.hhsize - households.num_workers +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)" +num_children,num_children,"_PERSON_COUNT('~adult', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_6_to_15,num_children_6_to_15,"_PERSON_COUNT('6 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +# get definition from travel model two +num_young_adults,num_young_adults,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_retirees,num_young_retirees,"_PERSON_COUNT('65 <= age <= 79', persons, households)" +num_old_retirees,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)" +num_persons_18_to_35,num_persons_18_to_35,"_PERSON_COUNT('18 <= age <= 35', persons, households)" +num_persons_65_plus,num_persons_65_plus,"_PERSON_COUNT('65 <= age', persons, households)" +num_highschool_graduates,num_highschool_graduates,"_PERSON_COUNT('9 <= education_attainment', persons, households)" +#mobility av share, mobility_av_share, setting('mobility_av_share') diff --git a/test/auto_ownership/configs/annotate_landuse.csv b/test/auto_ownership/configs/annotate_landuse.csv new file mode 100644 index 000000000..65237c0ad --- /dev/null +++ b/test/auto_ownership/configs/annotate_landuse.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / land_use.TOTACRE +employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE +population_density,population_density,land_use.TOTPOP / land_use.TOTACRE +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) \ No newline at end of file diff --git a/test/auto_ownership/configs/annotate_persons.csv b/test/auto_ownership/configs/annotate_persons.csv new file mode 100644 index 000000000..d52946719 --- /dev/null +++ b/test/auto_ownership/configs/annotate_persons.csv @@ -0,0 +1,40 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +# TODO add ptype annotation +#,ptype, +#presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" +#presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" +#presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" +#presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" +#presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" +#presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" +#presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" +#presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" +#student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" +#nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" +#,, +#,, FIXME - if person is a university student but has school age student category value then reset student category value +#,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" +#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student +#,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" +#,, FIXME if student category is non-student and employment is student then reset student category value to student +#,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" +#,, +#is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +#preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" +#preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" +#is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) +#is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +#is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +#school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +#school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +#school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +#is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" diff --git a/test/auto_ownership/configs/annotate_persons_after_hh.csv b/test/auto_ownership/configs/annotate_persons_after_hh.csv new file mode 100644 index 000000000..0dfa16be6 --- /dev/null +++ b/test/auto_ownership/configs/annotate_persons_after_hh.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" \ No newline at end of file diff --git a/test/auto_ownership/configs/auto_ownership.csv b/test/auto_ownership/configs/auto_ownership.csv new file mode 100644 index 000000000..4b8abdfa1 --- /dev/null +++ b/test/auto_ownership/configs/auto_ownership.csv @@ -0,0 +1,61 @@ +Label,Description,Expression,0_CARS,1_CAR_1CV,1_CAR_1AV,2_CARS_2CV,2_CARS_2AV,2_CARS_1CV1AV,3_CARS_3CV,3_CARS_3AV,3_CARS_2CV1AV,3_CARS_1CV2AV,4_CARS_4CV +,Boolean indicating that household is in SF county,"@np.where(df.county_id == ID_SAN_FRANCISCO, 1, 0)",coef_boolean_indicating_that_household_is_in_sf_county_0_CARS,,,,,,,,,, +,alternative specific constant for 1 driver household,@df.num_drivers==1,coef_alternative_specific_constant_for_1_driver_household_0_CARS,,,coef_alternative_specific_constant_for_1_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_1_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_1_driver_household_2_CARS_1CV1AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_1_driver_household_3_CARS_1CV2AV,coef_alternative_specific_constant_for_1_driver_household_4_CARS_4CV +,alternative specific constant for 2 driver household,@df.num_drivers==2,coef_alternative_specific_constant_for_2_driver_household_0_CARS,coef_alternative_specific_constant_for_2_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_2_driver_household_1_CAR_1AV,,,,coef_alternative_specific_constant_for_2_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_2_driver_household_3_CARS_1CV2AV,coef_alternative_specific_constant_for_2_driver_household_4_CARS_4CV +,alternative specific constant for 3 driver household,@df.num_drivers==3,coef_alternative_specific_constant_for_3_driver_household_0_CARS,coef_alternative_specific_constant_for_3_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_3_driver_household_1_CAR_1AV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_alternative_specific_constant_for_3_driver_household_4_CARS_4CV +,alternative specific constant for 4+ driver household,@df.num_drivers>=4,coef_alternative_specific_constant_for_4_driver_household_0_CARS,coef_alternative_specific_constant_for_4_driver_household_1_CAR_1CV,coef_alternative_specific_constant_for_4_driver_household_1_CAR_1AV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_2CV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_2AV,coef_alternative_specific_constant_for_4_driver_household_2_CARS_1CV1AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_3CV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_3AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_2CV1AV,coef_alternative_specific_constant_for_4_driver_household_3_CARS_1CV2AV, +,workers to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_workers_to_drivers_ratio_for_1_driver_household_4_CARS_4CV +,workers to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_2_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_workers_to_drivers_ratio_for_2_driver_household_4_CARS_4CV +,workers to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_3_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_workers_to_drivers_ratio_for_3_driver_household_4_CARS_4CV +,workers to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_workers/df.num_drivers), 0)",coef_workers_to_drivers_ratio_for_4_driver_household_0_CARS,coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV, +,young adults to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_1_driver_household_0_CARS,,,,,,,,,, +,young adults to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_2_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,,,,, +,young adults to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_3_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,, +,young adults to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_young_adults/df.num_drivers), 0)",coef_young_adults_to_drivers_ratio_for_4_driver_household_0_CARS,coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV, +,school children to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_school_children_to_drivers_ratio_for_1_driver_household_4_CARS_4CV +,school children to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_2_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_school_children_to_drivers_ratio_for_2_driver_household_4_CARS_4CV +,school children to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_3_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_school_children_to_drivers_ratio_for_3_driver_household_4_CARS_4CV +,school children to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_children_6_to_15/df.num_drivers), 0)",coef_school_children_to_drivers_ratio_for_4_driver_household_0_CARS,coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV, +,young retirees to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_young_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV +,young retirees to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_young_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV +,young retirees to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_young_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV +,young retirees to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_young_retirees/df.num_drivers), 0)",coef_young_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV, +,old retirees to drivers ratio for 1 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==1) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,,,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,coef_old_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV +,old retirees to drivers ratio for 2 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==2) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,,,,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,coef_old_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV +,old retirees to drivers ratio for 3 driver household,"@np.where(df.num_drivers > 0, (df.num_drivers==3) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,,,,,coef_old_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV +,old retirees to drivers ratio for 4+ driver household,"@np.where(df.num_drivers > 0, (df.num_drivers>=4) * (df.num_old_retirees/df.num_drivers), 0)",coef_old_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV, +,household income <30k,@(df.income<30000),coef_household_income_30k_0_CARS,coef_household_income_30k_1_CAR_1CV,coef_household_income_30k_1_CAR_1AV,,,,coef_household_income_30k_3_CARS_3CV,coef_household_income_30k_3_CARS_3AV,coef_household_income_30k_3_CARS_2CV1AV,coef_household_income_30k_3_CARS_1CV2AV,coef_household_income_30k_4_CARS_4CV +,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,coef_household_income_3060k_1_CAR_1CV,coef_household_income_3060k_1_CAR_1AV,,,,coef_household_income_3060k_3_CARS_3CV,coef_household_income_3060k_3_CARS_3AV,coef_household_income_3060k_3_CARS_2CV1AV,coef_household_income_3060k_3_CARS_1CV2AV,coef_household_income_3060k_4_CARS_4CV +,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,coef_household_income_100k_1_CAR_1CV,coef_household_income_100k_1_CAR_1AV,,,,coef_household_income_100k_3_CARS_3CV,coef_household_income_100k_3_CARS_3AV,coef_household_income_100k_3_CARS_2CV1AV,coef_household_income_100k_3_CARS_1CV2AV,coef_household_income_100k_4_CARS_4CV +,low educational level for 1 driver household,@((df.num_drivers==1) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_1_driver_household_0_CARS,,,,,,coef_low_educational_level_for_1_driver_household_3_CARS_3CV,coef_low_educational_level_for_1_driver_household_3_CARS_3AV,coef_low_educational_level_for_1_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_1_driver_household_3_CARS_1CV2AV,coef_low_educational_level_for_1_driver_household_4_CARS_4CV +,low educational level for 2 driver household,@((df.num_drivers==2) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_2_driver_household_0_CARS,coef_low_educational_level_for_2_driver_household_1_CAR_1CV,coef_low_educational_level_for_2_driver_household_1_CAR_1AV,,,,coef_low_educational_level_for_2_driver_household_3_CARS_3CV,coef_low_educational_level_for_2_driver_household_3_CARS_3AV,coef_low_educational_level_for_2_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_2_driver_household_3_CARS_1CV2AV,coef_low_educational_level_for_2_driver_household_4_CARS_4CV +,low educational level for 3 driver household,@((df.num_drivers==3) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_3_driver_household_0_CARS,coef_low_educational_level_for_3_driver_household_1_CAR_1CV,coef_low_educational_level_for_3_driver_household_1_CAR_1AV,,,,,,,,coef_low_educational_level_for_3_driver_household_4_CARS_4CV +,low educational level for 4+ driver household,@((df.num_drivers>=4) & (df.num_highschool_graduates==0)),coef_low_educational_level_for_4_driver_household_0_CARS,coef_low_educational_level_for_4_driver_household_1_CAR_1CV,coef_low_educational_level_for_4_driver_household_1_CAR_1AV,,,,coef_low_educational_level_for_4_driver_household_3_CARS_3CV,coef_low_educational_level_for_4_driver_household_3_CARS_3AV,coef_low_educational_level_for_4_driver_household_3_CARS_2CV1AV,coef_low_educational_level_for_4_driver_household_3_CARS_1CV2AV, +,detached single family house,@df.building_size == 2,coef_detached_single_family_house_0_CARS,coef_detached_single_family_house_1_CAR_1CV,coef_detached_single_family_house_1_CAR_1AV,,,,coef_detached_single_family_house_3_CARS_3CV,coef_detached_single_family_house_3_CARS_3AV,coef_detached_single_family_house_3_CARS_2CV1AV,coef_detached_single_family_house_3_CARS_1CV2AV,coef_detached_single_family_house_4_CARS_4CV +,Intersection density,TotInt/502,,,,,,,coef_intersection_density_3_CARS_3CV,coef_intersection_density_3_CARS_3AV,coef_intersection_density_3_CARS_2CV1AV,coef_intersection_density_3_CARS_1CV2AV,coef_intersection_density_4_CARS_4CV +,Population density,PopDen,coef_population_density_0_CARS,coef_population_density_1_CAR_1CV,coef_population_density_1_CAR_1AV,,,,,,,, +,Retail density,RetEmpDen,coef_retail_density_0_CARS,coef_retail_density_1_CAR_1CV,coef_retail_density_1_CAR_1AV,,,,,,,, +,Non-motorized zonal accessbility,@df.nonmandatory_nm_accessibility,coef_nonmotorized_zonal_accessbility_0_CARS,coef_nonmotorized_zonal_accessbility_1_CAR_1CV,coef_nonmotorized_zonal_accessbility_1_CAR_1AV,,,,coef_nonmotorized_zonal_accessbility_3_CARS_3CV,coef_nonmotorized_zonal_accessbility_3_CARS_3AV,coef_nonmotorized_zonal_accessbility_3_CARS_2CV1AV,coef_nonmotorized_zonal_accessbility_3_CARS_1CV2AV,coef_nonmotorized_zonal_accessbility_4_CARS_4CV +,auto-transit zonal accessibility,@(df.nonmandatory_auto_accessibility - df.nonmandatory_transit_accessibility),coef_autotransit_zonal_accessibility_0_CARS,coef_autotransit_zonal_accessibility_1_CAR_1CV,coef_autotransit_zonal_accessibility_1_CAR_1AV,,,,,,,, +#,worker mandatory tour auto dependency,@((setting('use_accessibility') == 1) & (##workersAutoDependency##)),coef_worker_mandatory_tour_auto_dependency_0_CARS,coef_worker_mandatory_tour_auto_dependency_1_CAR_1CV,coef_worker_mandatory_tour_auto_dependency_1_CAR_1AV,,,,coef_worker_mandatory_tour_auto_dependency_3_CARS_3CV,coef_worker_mandatory_tour_auto_dependency_3_CARS_3AV,coef_worker_mandatory_tour_auto_dependency_3_CARS_2CV1AV,coef_worker_mandatory_tour_auto_dependency_3_CARS_1CV2AV,coef_worker_mandatory_tour_auto_dependency_4_CARS_4CV +#,student mandatory tour auto dependency,@((setting('use_accessibility') == 1) & (##studentsAutoDependency##)),coef_student_mandatory_tour_auto_dependency_0_CARS,coef_student_mandatory_tour_auto_dependency_1_CAR_1CV,coef_student_mandatory_tour_auto_dependency_1_CAR_1AV,,,,,,,, +#,worker mandatory tour rail mode portion,@((setting('use_accessibility') == 1) & (##workersRailProportion##)),coef_worker_mandatory_tour_rail_mode_portion_0_CARS,coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1CV,coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1AV,,,,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3CV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3AV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_2CV1AV,coef_worker_mandatory_tour_rail_mode_portion_3_CARS_1CV2AV,coef_worker_mandatory_tour_rail_mode_portion_4_CARS_4CV +#,student mandatory tour rail mode portion,@((setting('use_accessibility') == 1) & (##studentsRailProportion##)),coef_student_mandatory_tour_rail_mode_portion_0_CARS,coef_student_mandatory_tour_rail_mode_portion_1_CAR_1CV,coef_student_mandatory_tour_rail_mode_portion_1_CAR_1AV,,,,,,,, +,ASC adjustment for non-GQ PRE-AO,@df.hh_unit_type==1,,coef_asc_adjustment_for_nongq_preao_1_CAR_1CV,coef_asc_adjustment_for_nongq_preao_1_CAR_1AV,coef_asc_adjustment_for_nongq_preao_2_CARS_2CV,coef_asc_adjustment_for_nongq_preao_2_CARS_2AV,coef_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,coef_asc_adjustment_for_nongq_preao_3_CARS_3CV,coef_asc_adjustment_for_nongq_preao_3_CARS_3AV,coef_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,coef_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,coef_asc_adjustment_for_nongq_preao_4_CARS_4CV +,ASC adjustment for non-GQ POST-AO,@((df.hh_unit_type==1) & (setting('use_accessibility') == 1)),,coef_asc_adjustment_for_nongq_postao_1_CAR_1CV,coef_asc_adjustment_for_nongq_postao_1_CAR_1AV,coef_asc_adjustment_for_nongq_postao_2_CARS_2CV,coef_asc_adjustment_for_nongq_postao_2_CARS_2AV,coef_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,coef_asc_adjustment_for_nongq_postao_3_CARS_3CV,coef_asc_adjustment_for_nongq_postao_3_CARS_3AV,coef_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,coef_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,coef_asc_adjustment_for_nongq_postao_4_CARS_4CV +,adjustment for 2 workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers==2)),coef_adjustment_for_2_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,, +,adjustment for 3 workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers==3)),coef_adjustment_for_3_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,, +,adjustment for 4+ workers in non-family 0-car household,@((df.hh_type>3) & (df.num_workers>3)),coef_adjustment_for_4_workers_in_nonfamily_0car_household_0_CARS,,,,,,,,,, +,adjustment for 4+ workers in family 0-car household,@((df.hh_type<=3) & (df.num_workers>3)),coef_adjustment_for_4_workers_in_family_0car_household_0_CARS,,,,,,,,,, +,TM2 Round 2 ASC adjustment for 1 workers in 0-car household,@df.num_workers==1,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_4_CARS_4CV +,TM2 Round 2 ASC adjustment for 2 workers in 0-car household,@df.num_workers==2,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_4_CARS_4CV +,TM2 Round 2 ASC adjustment for 3+ workers in 0-car household,@df.num_workers>=3,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_4_CARS_4CV +,TM2 Round 2 ASC adjustment for non-GQ PRE-AO,@df.hh_unit_type==1,coef_tm2_round_2_asc_adjustment_for_nongq_preao_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_nongq_preao_4_CARS_4CV +,TM2 Round 2 ASC adjustment for non-GQ POST-AO,@((df.hh_unit_type==1) & (setting('use_accessibility') == 1)),coef_tm2_round_2_asc_adjustment_for_nongq_postao_0_CARS,,,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2CV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3CV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,coef_tm2_round_2_asc_adjustment_for_nongq_postao_4_CARS_4CV +,AV ASC - Household Income under $50k,@df.income<50000,,,coef_av_asc__household_income_under_50k_1_CAR_1AV,,coef_av_asc__household_income_under_50k_2_CARS_2AV,coef_av_asc__household_income_under_50k_2_CARS_1CV1AV,,coef_av_asc__household_income_under_50k_3_CARS_3AV,coef_av_asc__household_income_under_50k_3_CARS_2CV1AV,coef_av_asc__household_income_under_50k_3_CARS_1CV2AV, +,AV ASC - Household Income 100k+,@df.income>=100000,,,coef_av_asc__household_income_100k_1_CAR_1AV,,coef_av_asc__household_income_100k_2_CARS_2AV,coef_av_asc__household_income_100k_2_CARS_1CV1AV,,coef_av_asc__household_income_100k_3_CARS_3AV,coef_av_asc__household_income_100k_3_CARS_2CV1AV,coef_av_asc__household_income_100k_3_CARS_1CV2AV, +,AV ASC - Young household,@(df.num_persons_18_to_35>=df.num_persons_65_plus),,,coef_av_asc__young_household_1_CAR_1AV,,coef_av_asc__young_household_2_CARS_2AV,coef_av_asc__young_household_2_CARS_1CV1AV,,coef_av_asc__young_household_3_CARS_3AV,coef_av_asc__young_household_3_CARS_2CV1AV,coef_av_asc__young_household_3_CARS_1CV2AV, +,AV ASC - Old household,@(df.num_persons_18_to_35 < df.num_persons_65_plus),,,coef_av_asc__old_household_1_CAR_1AV,,coef_av_asc__old_household_2_CARS_2AV,coef_av_asc__old_household_2_CARS_1CV1AV,,coef_av_asc__old_household_3_CARS_3AV,coef_av_asc__old_household_3_CARS_2CV1AV,coef_av_asc__old_household_3_CARS_1CV2AV, +#,"AV Coeff Per Hour of travel by auto for work, summed across all workers in household",@((setting('use_accessibility') == 1) & (##@workAutoTime##)),,,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_1_CAR_1AV",,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_2AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_1CV1AV",,"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_3AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_2CV1AV","coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_1CV2AV", +,AV ASC - 100% AV Share,@setting('mobility_av_share')==1.0,,coef_unavailable,,coef_unavailable,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable +,AV ASC - 0% AV Share,@setting('mobility_av_share')==0,,,coef_unavailable,,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable,coef_unavailable, diff --git a/test/auto_ownership/configs/auto_ownership.yaml b/test/auto_ownership/configs/auto_ownership.yaml new file mode 100644 index 000000000..2cdde0464 --- /dev/null +++ b/test/auto_ownership/configs/auto_ownership.yaml @@ -0,0 +1,53 @@ + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coefficients.csv + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: 1 + alternatives: + - name: own no car + coefficient: 0.668 + alternatives: + - name: zero car + coefficient: 0.3 + alternatives: + - 0_CARS + - name: own cars + coefficient: 0.668 + alternatives: + - name: one car + coefficient: 0.3 + alternatives: + - 1_CAR_1CV + - 1_CAR_1AV + - name: two cars + coefficient: 0.3 + alternatives: + - 2_CARS_2CV + - 2_CARS_2AV + - 2_CARS_1CV1AV + - name: three cars + coefficient: 0.3 + alternatives: + - 3_CARS_3CV + - 3_CARS_3AV + - 3_CARS_2CV1AV + - 3_CARS_1CV2AV + - name: four cars + coefficient: 0.3 + alternatives: + - 4_CARS_4CV + +CONSTANTS: + ID_SAN_FRANCISCO: 1 + ID_SAN_MATEO: 2 + ID_SANTA_CLARA: 3 + ID_ALAMEDA: 4 + ID_CONTRA_COSTA: 5 + ID_SOLANO: 6 + ID_NAPA: 7 + ID_SONOMA: 8 + ID_MARIN: 9 diff --git a/test/auto_ownership/configs/auto_ownership_coefficients.csv b/test/auto_ownership/configs/auto_ownership_coefficients.csv new file mode 100644 index 000000000..1d88703f1 --- /dev/null +++ b/test/auto_ownership/configs/auto_ownership_coefficients.csv @@ -0,0 +1,393 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_boolean_indicating_that_household_is_in_sf_county_0_CARS,1.233276051,F +coef_alternative_specific_constant_for_1_driver_household_0_CARS,-1.920143842,F +coef_alternative_specific_constant_for_2_driver_household_0_CARS,-3.470781624,F +coef_alternative_specific_constant_for_3_driver_household_0_CARS,-3.329633459,F +coef_alternative_specific_constant_for_4_driver_household_0_CARS,-6.111494495,F +coef_workers_to_drivers_ratio_for_1_driver_household_0_CARS,-1.464256557,F +coef_workers_to_drivers_ratio_for_2_driver_household_0_CARS,-1.464256557,F +coef_workers_to_drivers_ratio_for_3_driver_household_0_CARS,-1.464256557,F +coef_workers_to_drivers_ratio_for_4_driver_household_0_CARS,-1.464256557,F +coef_young_adults_to_drivers_ratio_for_1_driver_household_0_CARS,-1.769767608,F +coef_young_adults_to_drivers_ratio_for_2_driver_household_0_CARS,-1.769767608,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_0_CARS,-1.769767608,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_0_CARS,-1.769767608,F +coef_school_children_to_drivers_ratio_for_1_driver_household_0_CARS,-0.346415088,F +coef_school_children_to_drivers_ratio_for_2_driver_household_0_CARS,-0.346415088,F +coef_school_children_to_drivers_ratio_for_3_driver_household_0_CARS,-0.346415088,F +coef_school_children_to_drivers_ratio_for_4_driver_household_0_CARS,-0.346415088,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,-0.38172487,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,-0.38172487,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,-0.38172487,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,-0.38172487,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_0_CARS,0.608700053,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_0_CARS,0.608700053,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_0_CARS,0.608700053,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_0_CARS,0.608700053,F +coef_household_income_30k_0_CARS,2.654926169,F +coef_household_income_3060k_0_CARS,0.918219369,F +coef_household_income_100k_0_CARS,-0.44306913,F +coef_low_educational_level_for_1_driver_household_0_CARS,0.767760942,F +coef_low_educational_level_for_2_driver_household_0_CARS,0.767760942,F +coef_low_educational_level_for_3_driver_household_0_CARS,0.767760942,F +coef_low_educational_level_for_4_driver_household_0_CARS,0.767760942,F +coef_detached_single_family_house_0_CARS,-1.774689947,F +coef_population_density_0_CARS,0.046206562,F +coef_retail_density_0_CARS,0.074563803,F +coef_nonmotorized_zonal_accessbility_0_CARS,0.060875253,F +coef_autotransit_zonal_accessibility_0_CARS,-0.200776981,F +coef_worker_mandatory_tour_auto_dependency_0_CARS,-0.16516642,F +coef_student_mandatory_tour_auto_dependency_0_CARS,-0.04422711,F +coef_worker_mandatory_tour_rail_mode_portion_0_CARS,0.171079447,F +coef_student_mandatory_tour_rail_mode_portion_0_CARS,0.26285636,F +coef_adjustment_for_2_workers_in_nonfamily_0car_household_0_CARS,0.725034403,F +coef_adjustment_for_3_workers_in_nonfamily_0car_household_0_CARS,1.194820674,F +coef_adjustment_for_4_workers_in_nonfamily_0car_household_0_CARS,2.844226409,F +coef_adjustment_for_4_workers_in_family_0car_household_0_CARS,2.116557339,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_0_CARS,1.097234124,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_0_CARS,0.893156568,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_0_CARS,0.937133013,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_0_CARS,-1.511025686,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_0_CARS,-0.115788174,F +coef_alternative_specific_constant_for_2_driver_household_1_CAR_1CV,-2.20339718,F +coef_alternative_specific_constant_for_3_driver_household_1_CAR_1CV,-2.473061365,F +coef_alternative_specific_constant_for_4_driver_household_1_CAR_1CV,-3.983965837,F +coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.407798013,F +coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.407798013,F +coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1CV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1CV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1CV,0.232672708,F +coef_household_income_30k_1_CAR_1CV,0.632172646,F +coef_household_income_3060k_1_CAR_1CV,0.342605571,F +coef_household_income_100k_1_CAR_1CV,-0.44306913,F +coef_low_educational_level_for_2_driver_household_1_CAR_1CV,0.432294097,F +coef_low_educational_level_for_3_driver_household_1_CAR_1CV,0.432294097,F +coef_low_educational_level_for_4_driver_household_1_CAR_1CV,0.432294097,F +coef_detached_single_family_house_1_CAR_1CV,-0.528137281,F +coef_population_density_1_CAR_1CV,0.02268226,F +coef_retail_density_1_CAR_1CV,0.029544267,F +coef_nonmotorized_zonal_accessbility_1_CAR_1CV,0.039970159,F +coef_autotransit_zonal_accessibility_1_CAR_1CV,-0.009060243,F +coef_worker_mandatory_tour_auto_dependency_1_CAR_1CV,-0.16516642,F +coef_student_mandatory_tour_auto_dependency_1_CAR_1CV,-0.04422711,F +coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1CV,0.171079447,F +coef_student_mandatory_tour_rail_mode_portion_1_CAR_1CV,0.26285636,F +coef_asc_adjustment_for_nongq_preao_1_CAR_1CV,0.04582774,F +coef_asc_adjustment_for_nongq_postao_1_CAR_1CV,-0.013942306,F +coef_alternative_specific_constant_for_2_driver_household_1_CAR_1AV,-2.20339718,F +coef_alternative_specific_constant_for_3_driver_household_1_CAR_1AV,-2.473061365,F +coef_alternative_specific_constant_for_4_driver_household_1_CAR_1AV,-3.983965837,F +coef_workers_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.407798013,F +coef_workers_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.407798013,F +coef_workers_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_1_CAR_1AV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_1_CAR_1AV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_1_CAR_1AV,0.232672708,F +coef_household_income_30k_1_CAR_1AV,0.632172646,F +coef_household_income_3060k_1_CAR_1AV,0.342605571,F +coef_household_income_100k_1_CAR_1AV,-0.44306913,F +coef_low_educational_level_for_2_driver_household_1_CAR_1AV,0.432294097,F +coef_low_educational_level_for_3_driver_household_1_CAR_1AV,0.432294097,F +coef_low_educational_level_for_4_driver_household_1_CAR_1AV,0.432294097,F +coef_detached_single_family_house_1_CAR_1AV,-0.528137281,F +coef_population_density_1_CAR_1AV,0.02268226,F +coef_retail_density_1_CAR_1AV,0.029544267,F +coef_nonmotorized_zonal_accessbility_1_CAR_1AV,0.039970159,F +coef_autotransit_zonal_accessibility_1_CAR_1AV,-0.009060243,F +coef_worker_mandatory_tour_auto_dependency_1_CAR_1AV,-0.16516642,F +coef_student_mandatory_tour_auto_dependency_1_CAR_1AV,-0.04422711,F +coef_worker_mandatory_tour_rail_mode_portion_1_CAR_1AV,0.171079447,F +coef_student_mandatory_tour_rail_mode_portion_1_CAR_1AV,0.26285636,F +coef_asc_adjustment_for_nongq_preao_1_CAR_1AV,0.04582774,F +coef_asc_adjustment_for_nongq_postao_1_CAR_1AV,-0.013942306,F +coef_av_asc__household_income_under_50k_1_CAR_1AV,-1,F +coef_av_asc__household_income_100k_1_CAR_1AV,1,F +coef_av_asc__young_household_1_CAR_1AV,0.5,F +coef_av_asc__old_household_1_CAR_1AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_1_CAR_1AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS_2CV,-0.934889684,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS_2CV,-0.317496682,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS_2CV,-0.909626603,F +coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.474996826,F +coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.407798013,F +coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.108758563,F +coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2CV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2CV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2CV,0.232672708,F +coef_asc_adjustment_for_nongq_preao_2_CARS_2CV,-1.602468896,F +coef_asc_adjustment_for_nongq_postao_2_CARS_2CV,-0.132328921,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2CV,-0.151698573,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2CV,-0.17099367,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2CV,-0.553211417,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2CV,0.723793144,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2CV,0.16375467,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS_2AV,-0.934889684,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS_2AV,-0.317496682,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS_2AV,-0.909626603,F +coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.474996826,F +coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.407798013,F +coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_2AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_2AV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_2AV,0.232672708,F +coef_asc_adjustment_for_nongq_preao_2_CARS_2AV,-1.602468896,F +coef_asc_adjustment_for_nongq_postao_2_CARS_2AV,-0.132328921,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_2AV,-0.151698573,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_2AV,-0.17099367,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_2AV,-0.553211417,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_2AV,0.723793144,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_2AV,0.16375467,F +coef_av_asc__household_income_under_50k_2_CARS_2AV,-1,F +coef_av_asc__household_income_100k_2_CARS_2AV,1,F +coef_av_asc__young_household_2_CARS_2AV,0.5,F +coef_av_asc__old_household_2_CARS_2AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_2AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_2_CARS_1CV1AV,-0.934889684,F +coef_alternative_specific_constant_for_3_driver_household_2_CARS_1CV1AV,-0.317496682,F +coef_alternative_specific_constant_for_4_driver_household_2_CARS_1CV1AV,-0.909626603,F +coef_workers_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.474996826,F +coef_workers_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.407798013,F +coef_workers_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.598263093,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.018572553,F +coef_school_children_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,-0.19861915,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_2_CARS_1CV1AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_2_CARS_1CV1AV,0.232672708,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_2_CARS_1CV1AV,0.232672708,F +coef_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,-1.602468896,F +coef_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,-0.132328921,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_2_CARS_1CV1AV,-0.151698573,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_2_CARS_1CV1AV,-0.17099367,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_2_CARS_1CV1AV,-0.553211417,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_2_CARS_1CV1AV,0.723793144,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_2_CARS_1CV1AV,0.16375467,F +coef_av_asc__household_income_under_50k_2_CARS_1CV1AV,-1,F +coef_av_asc__household_income_100k_2_CARS_1CV1AV,1,F +coef_av_asc__young_household_2_CARS_1CV1AV,0.5,F +coef_av_asc__old_household_2_CARS_1CV1AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_2_CARS_1CV1AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS_3CV,-1.904733531,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS_3CV,-0.829354142,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS_3CV,-0.524427892,F +coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.474996826,F +coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.474996826,F +coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.108758563,F +coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.108758563,F +coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3CV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3CV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3CV,0.232672708,F +coef_household_income_30k_3_CARS_3CV,-0.601152419,F +coef_household_income_3060k_3_CARS_3CV,-0.305680609,F +coef_household_income_100k_3_CARS_3CV,0.087029823,F +coef_low_educational_level_for_1_driver_household_3_CARS_3CV,-0.176660377,F +coef_low_educational_level_for_2_driver_household_3_CARS_3CV,-0.176660377,F +coef_low_educational_level_for_4_driver_household_3_CARS_3CV,0.432294097,F +coef_detached_single_family_house_3_CARS_3CV,0.427320086,F +coef_intersection_density_3_CARS_3CV,-0.326525635,F +coef_nonmotorized_zonal_accessbility_3_CARS_3CV,-0.052524462,F +coef_worker_mandatory_tour_auto_dependency_3_CARS_3CV,0.104064535,F +coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3CV,-0.206713378,F +coef_asc_adjustment_for_nongq_preao_3_CARS_3CV,-2.169791463,F +coef_asc_adjustment_for_nongq_postao_3_CARS_3CV,-0.180285245,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3CV,-0.209786195,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3CV,-0.382492356,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3CV,0.065128979,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3CV,0.617090781,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3CV,0.222715484,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS_3AV,-1.904733531,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS_3AV,-0.829354142,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS_3AV,-0.524427892,F +coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.474996826,F +coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.474996826,F +coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_3AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_3AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_3AV,0.232672708,F +coef_household_income_30k_3_CARS_3AV,-0.601152419,F +coef_household_income_3060k_3_CARS_3AV,-0.305680609,F +coef_household_income_100k_3_CARS_3AV,0.087029823,F +coef_low_educational_level_for_1_driver_household_3_CARS_3AV,-0.176660377,F +coef_low_educational_level_for_2_driver_household_3_CARS_3AV,-0.176660377,F +coef_low_educational_level_for_4_driver_household_3_CARS_3AV,0.432294097,F +coef_detached_single_family_house_3_CARS_3AV,0.427320086,F +coef_intersection_density_3_CARS_3AV,-0.326525635,F +coef_nonmotorized_zonal_accessbility_3_CARS_3AV,-0.052524462,F +coef_worker_mandatory_tour_auto_dependency_3_CARS_3AV,0.104064535,F +coef_worker_mandatory_tour_rail_mode_portion_3_CARS_3AV,-0.206713378,F +coef_asc_adjustment_for_nongq_preao_3_CARS_3AV,-2.169791463,F +coef_asc_adjustment_for_nongq_postao_3_CARS_3AV,-0.180285245,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_3AV,-0.209786195,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_3AV,-0.382492356,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_3AV,0.065128979,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_3AV,0.617090781,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_3AV,0.222715484,F +coef_av_asc__household_income_under_50k_3_CARS_3AV,-1,F +coef_av_asc__household_income_100k_3_CARS_3AV,1,F +coef_av_asc__young_household_3_CARS_3AV,0.5,F +coef_av_asc__old_household_3_CARS_3AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_3AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS_2CV1AV,-1.904733531,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS_2CV1AV,-0.829354142,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS_2CV1AV,-0.524427892,F +coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.474996826,F +coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.474996826,F +coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_2CV1AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_2CV1AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_2CV1AV,0.232672708,F +coef_household_income_30k_3_CARS_2CV1AV,-0.601152419,F +coef_household_income_3060k_3_CARS_2CV1AV,-0.305680609,F +coef_household_income_100k_3_CARS_2CV1AV,0.087029823,F +coef_low_educational_level_for_1_driver_household_3_CARS_2CV1AV,-0.176660377,F +coef_low_educational_level_for_2_driver_household_3_CARS_2CV1AV,-0.176660377,F +coef_low_educational_level_for_4_driver_household_3_CARS_2CV1AV,0.432294097,F +coef_detached_single_family_house_3_CARS_2CV1AV,0.427320086,F +coef_intersection_density_3_CARS_2CV1AV,-0.326525635,F +coef_nonmotorized_zonal_accessbility_3_CARS_2CV1AV,-0.052524462,F +coef_worker_mandatory_tour_auto_dependency_3_CARS_2CV1AV,0.104064535,F +coef_worker_mandatory_tour_rail_mode_portion_3_CARS_2CV1AV,-0.206713378,F +coef_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,-2.169791463,F +coef_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,-0.180285245,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_2CV1AV,-0.209786195,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_2CV1AV,-0.382492356,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_2CV1AV,0.065128979,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_2CV1AV,0.617090781,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_2CV1AV,0.222715484,F +coef_av_asc__household_income_under_50k_3_CARS_2CV1AV,-1,F +coef_av_asc__household_income_100k_3_CARS_2CV1AV,1,F +coef_av_asc__young_household_3_CARS_2CV1AV,0.5,F +coef_av_asc__old_household_3_CARS_2CV1AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_2CV1AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_3_CARS_1CV2AV,-1.904733531,F +coef_alternative_specific_constant_for_2_driver_household_3_CARS_1CV2AV,-0.829354142,F +coef_alternative_specific_constant_for_4_driver_household_3_CARS_1CV2AV,-0.524427892,F +coef_workers_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.474996826,F +coef_workers_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.474996826,F +coef_workers_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.407798013,F +coef_young_adults_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.598263093,F +coef_school_children_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.108758563,F +coef_school_children_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.018572553,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,-0.19861915,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_3_CARS_1CV2AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_3_CARS_1CV2AV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_4_driver_household_3_CARS_1CV2AV,0.232672708,F +coef_household_income_30k_3_CARS_1CV2AV,-0.601152419,F +coef_household_income_3060k_3_CARS_1CV2AV,-0.305680609,F +coef_household_income_100k_3_CARS_1CV2AV,0.087029823,F +coef_low_educational_level_for_1_driver_household_3_CARS_1CV2AV,-0.176660377,F +coef_low_educational_level_for_2_driver_household_3_CARS_1CV2AV,-0.176660377,F +coef_low_educational_level_for_4_driver_household_3_CARS_1CV2AV,0.432294097,F +coef_detached_single_family_house_3_CARS_1CV2AV,0.427320086,F +coef_intersection_density_3_CARS_1CV2AV,-0.326525635,F +coef_nonmotorized_zonal_accessbility_3_CARS_1CV2AV,-0.052524462,F +coef_worker_mandatory_tour_auto_dependency_3_CARS_1CV2AV,0.104064535,F +coef_worker_mandatory_tour_rail_mode_portion_3_CARS_1CV2AV,-0.206713378,F +coef_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,-2.169791463,F +coef_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,-0.180285245,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_3_CARS_1CV2AV,-0.209786195,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_3_CARS_1CV2AV,-0.382492356,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_3_CARS_1CV2AV,0.065128979,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_3_CARS_1CV2AV,0.617090781,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_3_CARS_1CV2AV,0.222715484,F +coef_av_asc__household_income_under_50k_3_CARS_1CV2AV,-1,F +coef_av_asc__household_income_100k_3_CARS_1CV2AV,1,F +coef_av_asc__young_household_3_CARS_1CV2AV,0.5,F +coef_av_asc__old_household_3_CARS_1CV2AV,-1,F +"coef_av__coeff_per_hour_of_travel_by_auto_for_work,_summed_across_all_workers_in_household_3_CARS_1CV2AV",0.25,F +coef_alternative_specific_constant_for_1_driver_household_4_CARS_4CV,-2.488003709,F +coef_alternative_specific_constant_for_2_driver_household_4_CARS_4CV,-1.778311581,F +coef_alternative_specific_constant_for_3_driver_household_4_CARS_4CV,-0.533915593,F +coef_workers_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.474996826,F +coef_workers_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.474996826,F +coef_workers_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.474996826,F +coef_school_children_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.108758563,F +coef_school_children_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.108758563,F +coef_school_children_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.108758563,F +coef_young_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,0.28676361,F +coef_young_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,0.28676361,F +coef_old_retirees_to_drivers_ratio_for_1_driver_household_4_CARS_4CV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_2_driver_household_4_CARS_4CV,-0.901020484,F +coef_old_retirees_to_drivers_ratio_for_3_driver_household_4_CARS_4CV,-0.901020484,F +coef_household_income_30k_4_CARS_4CV,-0.936061162,F +coef_household_income_3060k_4_CARS_4CV,-0.271548734,F +coef_household_income_100k_4_CARS_4CV,0.144123363,F +coef_low_educational_level_for_1_driver_household_4_CARS_4CV,-0.176660377,F +coef_low_educational_level_for_2_driver_household_4_CARS_4CV,-0.176660377,F +coef_low_educational_level_for_3_driver_household_4_CARS_4CV,-0.176660377,F +coef_detached_single_family_house_4_CARS_4CV,0.539511974,F +coef_intersection_density_4_CARS_4CV,-0.326525635,F +coef_nonmotorized_zonal_accessbility_4_CARS_4CV,-0.100612397,F +coef_worker_mandatory_tour_auto_dependency_4_CARS_4CV,0.104064535,F +coef_worker_mandatory_tour_rail_mode_portion_4_CARS_4CV,-0.206713378,F +coef_asc_adjustment_for_nongq_preao_4_CARS_4CV,-2.641133846,F +coef_asc_adjustment_for_nongq_postao_4_CARS_4CV,-0.186095557,F +coef_tm2_round_2_asc_adjustment_for_1_workers_in_0car_household_4_CARS_4CV,-0.308288035,F +coef_tm2_round_2_asc_adjustment_for_2_workers_in_0car_household_4_CARS_4CV,-0.551034746,F +coef_tm2_round_2_asc_adjustment_for_3_workers_in_0car_household_4_CARS_4CV,-0.048670057,F +coef_tm2_round_2_asc_adjustment_for_nongq_preao_4_CARS_4CV,0.69421287,F +coef_tm2_round_2_asc_adjustment_for_nongq_postao_4_CARS_4CV,0.2639107,F diff --git a/activitysim/examples/prototype_semcog/configs/constants.yaml b/test/auto_ownership/configs/constants.yaml old mode 100755 new mode 100644 similarity index 86% rename from activitysim/examples/prototype_semcog/configs/constants.yaml rename to test/auto_ownership/configs/constants.yaml index 7d3864fd9..b0bd5a1f3 --- a/activitysim/examples/prototype_semcog/configs/constants.yaml +++ b/test/auto_ownership/configs/constants.yaml @@ -61,3 +61,8 @@ PTYPE_NAME: CDAP_ACTIVITY_MANDATORY: M CDAP_ACTIVITY_NONMANDATORY: N CDAP_ACTIVITY_HOME: H + +# Correction for transit skim expressions +# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and +# therefore need to be divided by the scale factor if used in expressions +TRANSIT_SCALE_FACTOR: 100 diff --git a/test/auto_ownership/configs/initialize_households.yaml b/test/auto_ownership/configs/initialize_households.yaml new file mode 100644 index 000000000..5d98a703d --- /dev/null +++ b/test/auto_ownership/configs/initialize_households.yaml @@ -0,0 +1,37 @@ +annotate_tables: + - tablename: persons + column_map: + HHID: household_id + PERID: person_id + AGEP: age + SEX: sex + SCHL: education_attainment + OCCP: occupation + WKHP: hours_worked + WKW: weeks_worked + EMPLOYED: employment_status + ESR: esr + SCHG: grade_attending + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + column_map: + HHID: household_id + MAZ: home_zone_id + HHINCADJ: income + NWRKRS_ESR: num_workers + VEH: auto_ownership + NP: hhsize + HHT: hh_type + BLD: building_size + TYPE: hh_unit_type + MTCCountyID: county_id + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons + - land_use \ No newline at end of file diff --git a/test/auto_ownership/configs/initialize_landuse.yaml b/test/auto_ownership/configs/initialize_landuse.yaml new file mode 100644 index 000000000..7cc1d7e34 --- /dev/null +++ b/test/auto_ownership/configs/initialize_landuse.yaml @@ -0,0 +1,54 @@ +annotate_tables: + - tablename: land_use + column_map: + MAZ_ORIGINAL: zone_id + CountyID: county_id + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + annotate: + SPEC: annotate_landuse + DF: land_use + - tablename: accessibility + column_map: + column_1: nonmandatory_auto_accessibility + column_2: nonmandatory_transit_accessibility + column_3: nonmandatory_nm_accessibility + column_4: nonmandatory_sov0_accessibility + column_5: nonmandatory_sov1_accessibility + column_6: nonmandatory_sov2_accessibility + column_7: nonmandatory_hov0_accessibility + column_8: nonmandatory_hov1_accessibility + column_9: nonmandatory_hov2_accessibility + column_10: shop_hov_insufficient_accessibility + column_11: shop_hov_sufficient_accessibility + column_12: shop_hov_oversufficient_accessibility + column_13: maint_hov_insufficient_accessibility + column_14: maint_hov_sufficient_accessibility + column_15: maint_hov_oversufficient_accessibility + column_16: eat_hov_insufficient_accessibility + column_17: eat_hov_sufficient_accessibility + column_18: eat_hov_oversufficient_accessibility + column_19: visit_hov_insufficient_accessibility + column_20: visit_hov_sufficient_accessibility + column_21: visit_hov_oversufficient_accessibility + column_22: discr_hov_insufficient_accessibility + column_23: discr_hov_sufficient_accessibility + column_24: discr_hov_oversufficient_accessibility + column_25: escort_hov_insufficient_accessibility + column_26: escort_hov_sufficient_accessibility + column_27: escort_hov_oversufficient_accessibility + column_28: shop_sov_insufficient_accessibility + column_29: shop_sov_sufficient_accessibility + column_30: shop_sov_oversufficient_accessibility + column_31: maint_sov_insufficient_accessibility + column_32: maint_sov_sufficient_accessibility + column_33: maint_sov_oversufficient_accessibility + column_40: discr_sov_insufficient_accessibility + column_41: discr_sov_sufficient_accessibility + column_42: discr_sov_oversufficient_accessibility + column_45: total_emp_accessibility + column_47: hh_walktransit_accessibility + mgra : zone_id \ No newline at end of file diff --git a/activitysim/examples/prototype_semcog/configs/network_los.yaml b/test/auto_ownership/configs/network_los.yaml old mode 100755 new mode 100644 similarity index 60% rename from activitysim/examples/prototype_semcog/configs/network_los.yaml rename to test/auto_ownership/configs/network_los.yaml index 43a28e533..391125a38 --- a/activitysim/examples/prototype_semcog/configs/network_los.yaml +++ b/test/auto_ownership/configs/network_los.yaml @@ -5,10 +5,10 @@ write_skim_cache: True zone_system: 1 -taz_skims: skims*.omx +taz_skims: skims.omx skim_time_periods: time_window: 1440 - period_minutes: 30 - periods: [0, 6, 12, 24, 32, 48] - labels: ['EA', 'AM', 'MD', 'PM', 'EV'] + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/test/auto_ownership/configs/settings.yaml b/test/auto_ownership/configs/settings.yaml new file mode 100644 index 000000000..ce94eef8e --- /dev/null +++ b/test/auto_ownership/configs/settings.yaml @@ -0,0 +1,65 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + unique_hh_id: household_id + NP: hhsize + hh_workers_from_esr: num_workers + VEH: auto_ownership + MAZ: home_zone_id + HINCP: income + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + unique_hh_id: household_id + AGEP: age + SPORDER: PNUM + SEX: sex + employed: pemploy + student_status: pstudent + person_type: ptype + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ_ORIGINAL: zone_id + CountyID: county_id + TAZ_ORIGINAL: TAZ + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + +#trace_hh_id: 357022 +trace_hh_id: 36 + +use_accessibility: 0 + +mobility_av_share: 0.0 \ No newline at end of file diff --git a/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv b/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 000000000..bddab06b9 --- /dev/null +++ b/test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/test/auto_ownership/data/.gitkeep b/test/auto_ownership/data/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/auto_ownership/test_auto_ownership.py b/test/auto_ownership/test_auto_ownership.py new file mode 100644 index 000000000..63a49969d --- /dev/null +++ b/test/auto_ownership/test_auto_ownership.py @@ -0,0 +1,434 @@ +import logging +import pytest +import os +import shutil +import pandas as pd +import numpy as np +from numpy import dot +from numpy.linalg import norm +import orca +from numpy.random import randint +import scipy.stats as stats + +# import models is necessary to initalize the model steps with orca +from activitysim.abm import models +from activitysim.core import pipeline, config +from activitysim.core import tracing + +logger = logging.getLogger(__name__) + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return "auto_ownership" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables(prepare_module_inputs) -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "MAZ_ORIGINAL", + "persons": "person_id", + "households": "household_id", + "accessibility": "MAZ_ORIGINAL", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return False + + +# Used by conftest.py reconnect_pipeline method +@pytest.fixture(scope="module") +def load_checkpoint() -> bool: + """ + checkpoint to be loaded from the pipeline when reconnecting. + """ + return "initialize_households" + + +@pytest.mark.skipif( + os.path.isfile("test/auto_ownership/output/pipeline.h5"), + reason="no need to recreate pipeline store if alreayd exist", +) +def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["initialize_landuse", "initialize_households"]) + + pipeline.close_pipeline() + + +def test_auto_ownership(reconnect_pipeline: pipeline.Pipeline, caplog): + + caplog.set_level(logging.INFO) + + # run model step + pipeline.run( + models=["auto_ownership_simulate"], resume_after="initialize_households" + ) + + # get the updated pipeline data + household_df = pipeline.get_table("households") + # logger.info("household_df columns: ", household_df.columns.tolist()) + + # target_col = "autos" + target_col = "pre_autos" + choice_col = "auto_ownership" + simulated_col = "autos_model" + + similarity_threshold = 0.99 + + ao_alternatives_df = pd.DataFrame.from_dict( + { + choice_col: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + "auto_choice_label": [ + "0_CARS", + "1_CAR_1CV", + "1_CAR_1AV", + "2_CARS_2CV", + "2_CARS_2AV", + "2_CARS_1CV1AV", + "3_CARS_3CV", + "3_CARS_3AV", + "3_CARS_2CV1AV", + "3_CARS_1CV2AV", + "4_CARS_4CV", + ], + simulated_col: [0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4], + } + ) + + household_df = pd.merge(household_df, ao_alternatives_df, how="left", on=choice_col) + + # AO summary from the model + simulated_df = create_summary( + household_df, key=simulated_col, out_col="Simulated_Share" + ) + + # AO summary from the results/target + target_df = create_summary(household_df, key=target_col, out_col="Target_Share") + + merged_df = pd.merge( + target_df, simulated_df, left_on=target_col, right_on=simulated_col, how="outer" + ) + merged_df = merged_df.fillna(0) + + # compare simulated and target results by computing the cosine similarity between them + similarity_value = cosine_similarity( + merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist() + ) + + # save the results to disk + merged_df.to_csv( + os.path.join("test", "auto_ownership", "output", "ao_test_results.csv"), + index=False, + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + +@pytest.mark.skip +def test_auto_ownership_variation(reconnect_pipeline: pipeline.Pipeline, caplog): + + caplog.set_level(logging.INFO) + + output_file = os.path.join( + "test", "auto_ownership", "output", "ao_results_variation.csv" + ) + + if os.path.isfile(output_file): + out_df = pd.read_csv(output_file) + + pipeline.open_pipeline(resume_after="initialize_households") + + household_df = pipeline.get_table("households") + + else: + target_col = "pre_autos" + choice_col = "auto_ownership" + simulated_col = "autos_model" + + ao_alternatives_df = pd.DataFrame.from_dict( + { + choice_col: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], + "auto_choice_label": [ + "0_CARS", + "1_CAR_1CV", + "1_CAR_1AV", + "2_CARS_2CV", + "2_CARS_2AV", + "2_CARS_1CV1AV", + "3_CARS_3CV", + "3_CARS_3AV", + "3_CARS_2CV1AV", + "3_CARS_1CV2AV", + "4_CARS_4CV", + ], + simulated_col: [0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4], + } + ) + + NUM_SEEDS = 100 + + for i in range(1, NUM_SEEDS + 1): + base_seed = randint(1, 99999) + orca.add_injectable("rng_base_seed", base_seed) + + # run model step + pipeline.run( + models=["auto_ownership_simulate"], resume_after="initialize_households" + ) + + # get the updated pipeline data + household_df = pipeline.get_table("households") + + household_df = pd.merge( + household_df, ao_alternatives_df, how="left", on=choice_col + ) + + # AO summary from the model + simulated_share_col_name = "simulation_" + str(i) + simulated_df = create_summary( + household_df, key=simulated_col, out_col=simulated_share_col_name + ) + + if i == 1: + out_df = create_summary(household_df, key=target_col, out_col="target") + + out_df = pd.concat( + [out_df, simulated_df[[simulated_share_col_name]]], axis=1 + ) + + # since model_name is used as checkpoint name, the same model can not be run more than once. + # have to close the pipeline before running the same model again. + pipeline.close_pipeline() + + out_df["simulation_min"] = out_df.filter(like="simulation_").min(axis=1) + out_df["simulation_max"] = out_df.filter(like="simulation_").max(axis=1) + out_df["simulation_mean"] = out_df.filter(like="simulation_").mean(axis=1) + + # reorder columns + cols = [ + "pre_autos", + "target", + "simulation_mean", + "simulation_min", + "simulation_max", + ] + cols = cols + [x for x in out_df.columns if x not in cols] + out_df = out_df[cols] + + out_df.to_csv(output_file, index=False) + + # chi-square test + alpha = 0.05 + observed_prob = out_df["simulation_mean"] + expected_prob = out_df["target"] + + num_hh = len(household_df) + + observed = [p * num_hh for p in observed_prob] + expected = [p * num_hh for p in expected_prob] + + observed_sum = sum(observed) + expected_sum = sum(expected) + + # the sum of the observed and expected frequencies must be the same for the test + observed = [f * expected_sum / observed_sum for f in observed] + + chi_square, prob = stats.chisquare(observed, expected) + p_value = 1 - prob + + # Ho: Target and Simulated results are from same distribution and there is no significant difference b/w the two. + # Ha: Target and Simulated results are statistically different. + + conclusion = "Failed to reject the null hypothesis." + if p_value >= alpha: + conclusion = "Null Hypothesis is rejected. Obersved and Simulated results are statistically different." + + logger.info(conclusion) + + # if p-value is less than alpha, then difference between simulated and target results + # are statistically greater than the random variation of the model results + assert p_value < alpha + + +# fetch/prepare existing files for model inputs +# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim +@pytest.fixture(scope="module") +def prepare_module_inputs() -> None: + """ + copy input files from sharepoint into test folder + + create unique person id in person file + + :return: None + """ + # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/ + # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1 + accessibility_file = os.path.join( + "test", "auto_ownership", "data", "accessibilities.csv" + ) + household_file = os.path.join( + "test", "auto_ownership", "data", "popsyn", "households.csv" + ) + person_file = os.path.join( + "test", "auto_ownership", "data", "popsyn", "persons.csv" + ) + landuse_file = os.path.join( + "test", "auto_ownership", "data", "landuse", "maz_data_withDensity.csv" + ) + + test_dir = os.path.join("test", "auto_ownership", "data") + + shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv")) + shutil.copy(household_file, os.path.join(test_dir, "households.csv")) + shutil.copy(person_file, os.path.join(test_dir, "persons.csv")) + shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv")) + + # add original maz id to accessibility table + land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv")) + + accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv")) + + accessibility_df = pd.merge( + accessibility_df, + land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}), + how="left", + on="mgra", + ) + + accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False) + + # currently household file has to have these two columns, even before annotation + # because annotate person happens before household and uses these two columns + # TODO find a way to get around this + #### + + # household file from populationsim + household_df = pd.read_csv(os.path.join(test_dir, "households.csv")) + + household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"} + + household_df.rename(columns=household_columns_dict, inplace=True) + + # get columns from ctramp output + tm2_simulated_household_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "householdData_3.csv") + ) + tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + household_df = pd.merge( + household_df, + tm2_simulated_household_df[ + [ + "household_id", + "autos", + "automated_vehicles", + "transponder", + "cdap_pattern", + "jtf_choice", + ] + ], + how="inner", # tm2 is not 100% sample run + on="household_id", + ) + + tm2_pre_ao_results_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "aoResults_pre.csv") + ) + tm2_pre_ao_results_df.rename( + columns={"HHID": "household_id", "AO": "pre_autos"}, inplace=True + ) + + household_df = pd.merge( + household_df, tm2_pre_ao_results_df, how="inner", on="household_id" + ) + + household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False) + + # person file from populationsim + person_df = pd.read_csv(os.path.join(test_dir, "persons.csv")) + + person_columns_dict = {"HHID": "household_id", "PERID": "person_id"} + + person_df.rename(columns=person_columns_dict, inplace=True) + + # get columns from ctramp result + tm2_simulated_person_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "personData_3.csv") + ) + tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + person_df = pd.merge( + person_df, + tm2_simulated_person_df[ + [ + "household_id", + "person_id", + "type", + "value_of_time", + "activity_pattern", + "imf_choice", + "inmf_choice", + "fp_choice", + "reimb_pct", + "workDCLogsum", + "schoolDCLogsum", + ] + ], + how="inner", # ctramp might not be 100% sample run + on=["household_id", "person_id"], + ) + + person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False) + + +def create_summary(input_df, key, out_col="Share") -> pd.DataFrame: + """ + Create summary for the input data. + 1. group input data by the "key" column + 2. calculate the percent of input data records in each "key" category. + + :return: pd.DataFrame + """ + + out_df = input_df.groupby(key).size().reset_index(name="Count") + out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4) + + return out_df[[key, out_col]] + + +def cosine_similarity(a, b): + """ + Computes cosine similarity between two vectors. + + Cosine similarity is used here as a metric to measure similarity between two sequence of numbers. + Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them + i.e., dot products of the vectors divided by the product of their lengths. + + :return: + """ + + return dot(a, b) / (norm(a) * norm(b)) diff --git a/test/cdap/.gitkeep b/test/cdap/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/cdap/configs/annotate_households.csv b/test/cdap/configs/annotate_households.csv new file mode 100644 index 000000000..bd6dc6fef --- /dev/null +++ b/test/cdap/configs/annotate_households.csv @@ -0,0 +1,18 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of non_workers,num_non_workers,households.hhsize - households.num_workers +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)" +num_children,num_children,"_PERSON_COUNT('~adult', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_6_to_15,num_children_6_to_15,"_PERSON_COUNT('6 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +# get definition from travel model two +num_young_adults,num_young_adults,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_retirees,num_young_retirees,"_PERSON_COUNT('65 <= age <= 79', persons, households)" +num_old_retirees,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)" +num_persons_18_to_35,num_persons_18_to_35,"_PERSON_COUNT('18 <= age <= 35', persons, households)" +num_persons_65_plus,num_persons_65_plus,"_PERSON_COUNT('65 <= age', persons, households)" +num_highschool_graduates,num_highschool_graduates,"_PERSON_COUNT('9 <= education_attainment', persons, households)" \ No newline at end of file diff --git a/test/cdap/configs/annotate_households_cdap.csv b/test/cdap/configs/annotate_households_cdap.csv new file mode 100644 index 000000000..cd66b1c42 --- /dev/null +++ b/test/cdap/configs/annotate_households_cdap.csv @@ -0,0 +1,11 @@ +Description,Target,Expression +#,, annotate households table after cdap model has run +num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +#participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) +participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) +cdap_activity,cdap_activity,"persons.cdap_activity.groupby(persons.household_id).apply(''.join).reindex(households.index).fillna('')" \ No newline at end of file diff --git a/test/cdap/configs/annotate_landuse.csv b/test/cdap/configs/annotate_landuse.csv new file mode 100644 index 000000000..65237c0ad --- /dev/null +++ b/test/cdap/configs/annotate_landuse.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / land_use.TOTACRE +employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE +population_density,population_density,land_use.TOTPOP / land_use.TOTACRE +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) \ No newline at end of file diff --git a/test/cdap/configs/annotate_persons.csv b/test/cdap/configs/annotate_persons.csv new file mode 100644 index 000000000..0aea67814 --- /dev/null +++ b/test/cdap/configs/annotate_persons.csv @@ -0,0 +1,54 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +#,, annotate employment category +#employment category - under employment age,empcat, +#employment category - over employment age and not employed,empcat, +#employment category - over employment age and working at least 35 hours a week and 27+ weeks a year,empcat, +#employment category - over employment age and not working at least 35 hours a week and 27+ weeks a year,empcat, +#,,TODO add ptype annotation +,ptype,0 +,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)" +,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)" +,ptype,"np.where(persons.type == 'University student', 3, ptype)" +,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)" +,ptype,"np.where(persons.type == 'Retired', 5, ptype)" +,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)" +,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)" +,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)" +#full time worker,ptype +#presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" +#presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" +#presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" +#presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" +#presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" +#presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" +#presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" +#presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" +#student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" +#nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" +#,, +#,, FIXME - if person is a university student but has school age student category value then reset student category value +#,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" +#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student +#,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" +#,, FIXME if student category is non-student and employment is student then reset student category value to student +#,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" +#,, +#is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +#preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" +#preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" +#is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) +#is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +#is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +#school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +#school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +#school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +#is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" diff --git a/test/cdap/configs/annotate_persons_after_hh.csv b/test/cdap/configs/annotate_persons_after_hh.csv new file mode 100644 index 000000000..0dfa16be6 --- /dev/null +++ b/test/cdap/configs/annotate_persons_after_hh.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" \ No newline at end of file diff --git a/test/cdap/configs/annotate_persons_cdap.csv b/test/cdap/configs/annotate_persons_cdap.csv new file mode 100644 index 000000000..2ad5e56a6 --- /dev/null +++ b/test/cdap/configs/annotate_persons_cdap.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" +has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" +has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" diff --git a/test/cdap/configs/cdap.yaml b/test/cdap/configs/cdap.yaml new file mode 100644 index 000000000..5758444c2 --- /dev/null +++ b/test/cdap/configs/cdap.yaml @@ -0,0 +1,49 @@ + +INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv +COEFFICIENTS: cdap_coefficients.csv +INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv + +FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv + +# set to True if want to introduce joint tour utility in cdap +# otherwise set to False or comment out, defaulted to False +ADD_JOINT_TOUR_UTILITY: True +JOINT_TOUR_COEFFICIENTS: cdap_joint_tour_coefficients.csv +JOINT_TOUR_USEFUL_COLUMNS: + - autos + - income + - num_workers + - nonmandatory_hov0_accessibility + - nonmandatory_hov1_accessibility + - nonmandatory_hov2_accessibility + - WorkLocationLogsum + +CONSTANTS: + FULL: 1 + PART: 2 + UNIVERSITY: 3 + NONWORK: 4 + RETIRED: 5 + DRIVING: 6 + SCHOOL: 7 + PRESCHOOL: 8 + +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + +annotate_persons: + SPEC: annotate_persons_cdap + DF: persons + + +annotate_households: + SPEC: annotate_households_cdap + DF: households + TABLES: + - persons diff --git a/test/cdap/configs/cdap_coefficients.csv b/test/cdap/configs/cdap_coefficients.csv new file mode 100644 index 000000000..110ee2a44 --- /dev/null +++ b/test/cdap/configs/cdap_coefficients.csv @@ -0,0 +1,235 @@ +coefficient_name,value,constrain +coef_UNAVAILABLE,-999,T +coef_full_time_worker_asc_M,2.9114,F +coef_full_time_worker_asc_N,-0.7695,F +coef_part_time_worker_asc_M,2.9274,F +coef_part_time_worker_asc_N,1.3675,F +coef_university_student_asc_M,1.7642,F +coef_university_student_asc_N,-0.3138,F +coef_non_working_adult_asc_M,-3.1521,F +coef_non_working_adult_asc_N,0.513,F +coef_retired_asc_M,-2.7055,F +coef_retired_asc_N,0.9234,F +coef_driving_age_child_who_is_in_school_asc_M,3.2036,F +coef_driving_age_child_who_is_in_school_asc_N,-3.4315,F +coef_pre_driving_age_child_who_is_in_school_asc_M,7.0644,F +coef_pre_driving_age_child_who_is_in_school_asc_N,2.574,F +coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,1.1,F +coef_pre_driving_age_child_who_is_too_young_for_school_asc_N,0.6017,F +#,, +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,-1.5151,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_N,0.3702,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,3.2965,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_N,1.1392,F +coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,-0.8582,F +coef_full_time_worker_interaction_with_age_less_than_35_N,-0.145,F +coef_part_time_worker_interaction_with_age_less_than_35_M,-0.7095,F +coef_part_time_worker_interaction_with_age_less_than_35_N,-1.4213,F +#,, +coef_full_time_worker_interaction_with_female_gender_M,0.3032,F +coef_full_time_worker_interaction_with_female_gender_N,0.7718,F +coef_part_time_worker_interaction_with_female_gender_M,0.061,F +coef_part_time_worker_interaction_with_female_gender_N,0.4176,F +coef_university_student_interaction_with_female_gender_M,1.2429,F +coef_university_student_interaction_with_female_gender_N,2.2549,F +coef_non_working_adult_interaction_with_female_gender_N,0.1475,F +coef_retired_interaction_with_female_M,-0.7751,F +coef_retired_interaction_with_female_N,-0.3729,F +coef_driving_age_child_who_is_in_school_interaction_with_female_N,0.7991,F +#,, +coef_full_time_worker_interaction_with_more_cars_than_workers_N,-0.087,F +coef_non_working_adult_interaction_with_more_cars_than_workers_N,0.2122,F +coef_retired_interaction_with_more_cars_than_workers_N,0.8642,F +coef_driving_age_child_who_is_in_school_interaction_with_more_cars_than_workers_M,0.0988,F +coef_full_time_worker_interaction_with_zero_cars_M,-0.3377,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_M,-0.5917,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_N,-1.4389,F +coef_full_time_worker_interaction_with_fewer_cars_than_workers_M,-0.3377,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_M,-0.4778,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_N,-0.5259,F +#,, +coef_full_time_worker_interaction_with_income_less_than_30k_M,-0.7201,F +coef_full_time_worker_interaction_with_income_less_than_30k_H,-0.5331,F +coef_part_time_worker_interaction_with_income_less_than_30k_M,0.1285,F +coef_university_student_interaction_with_income_less_than_30k_M,0.4359,F +coef_university_student_interaction_with_income_between_60k_and_100k_H,0.6352,F +coef_university_student_interaction_with_income_more_than_100k_H,0.6352,F +coef_non_working_adult_interaction_with_income_more_than_100k_H,-0.2468,F +coef_retired_interaction_with_income_more_than_100k_M,-0.1418,F +coef_retired_interaction_with_income_more_than_100k_H,-0.2388,F +coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_M,1.2007,F +coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_H,1.9783,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_60k_and_100k_M,0.2952,F +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_M,0.2952,F +#,, +coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,0.1212,F +coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,0.2004,F +coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,0.2314,F +coef_retired_interaction_with_peak_accessibility_to_all_employment_M,0.2792,F +coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N,0.0721,F +coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,0.0823,F +#,, +coef_university_student_interaction_with_school_location_mc_logsum_M,0.0243,F +coef_full_time_worker_interaction_with_usual_work_location_is_home_M,-2.4147,F +coef_part_time_worker_interaction_with_usual_work_location_is_home_M,-2.8801,F +coef_full_time_worker_interaction_with_no_usual_work_location_M,-0.3777,F +coef_part_time_worker_interaction_with_no_usual_work_location_M,-0.6869,F +coef_full_time_worker_asc_adjustment_M,-0.2301,F +coef_part_time_worker_asc_adjustment_M,-0.471,F +coef_university_student_asc_adjustment_M,-0.3361,F +coef_driving_age_child_who_is_in_school_asc_adjustment_M,0.1434,F +coef_pre_driving_age_child_who_is_in_school_asc_adjustment_M,0.4725,F +coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_M,0.5554,F +coef_part_time_worker_asc_adjustment_tm2_calib_M,-0.3499,F +coef_university_student_asc_adjustment_tm2_calib_M,-0.9471,F +coef_non_working_adult_asc_adjustment_tm2_calib_M,0,F +coef_retired_asc_adjustment_tm2_calib_M,0,F +coef_full_time_worker_interaction_with_retail_accessibility_N,0.0445,F +coef_non_working_adult_interaction_with_retail_accessibility_N,0.0069,F +coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N,0.157,F +coef_full_time_worker_interaction_with_usual_work_location_is_home_N,0.87616,F +coef_full_time_worker_asc_adjustment_N,-0.1846,F +coef_part_time_worker_asc_adjustment_N,-0.4229,F +coef_university_student_asc_adjustment_N,0.0325,F +coef_non_working_adult_asc_adjustment_N,0.077,F +coef_retired_asc_adjustment_N,-0.0012,F +coef_driving_age_child_who_is_in_school_asc_adjustment_N,-0.8075,F +coef_pre_driving_age_child_who_is_in_school_asc_adjustment_N,-0.1462,F +coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_N,0.134,F +coef_part_time_worker_asc_adjustment_tm2_calib_N,0.5217,F +coef_university_student_asc_adjustment_tm2_calib_N,-0.6678,F +coef_non_working_adult_asc_adjustment_tm2_calib_N,0.0466,F +coef_retired_asc_adjustment_tm2_calib_N,0.1313,F +coef_full_time_worker_interaction_with_detached_home_H,0.1538,F +coef_part_time_worker_interaction_with_detached_home_H,0.0862,F +coef_retired_interaction_with_detached_home_H,0.7415,F +coef_driving_age_child_who_is_in_school_interaction_with_detached_home_H,2.023,F +#,, +# 2-way interactions,, +coef_H_11,0.7511,F +coef_H_12,0,F +coef_H_13,0,F +coef_H_14,0,F +coef_H_15,0.6692,F +coef_H_16,1.3472,F +coef_H_17,0,F +coef_H_18,0.7797,F +coef_H_22,0.7897,F +coef_H_23,1.617,F +coef_H_24,1.1606,F +coef_H_25,0.7915,F +coef_H_26,0,F +coef_H_27,1.8203,F +coef_H_28,1.7547,F +coef_H_33,0,F +coef_H_34,0.637,F +coef_H_35,0.1955,F +coef_H_36,2.2375,F +coef_H_37,0,F +coef_H_38,1.7118,F +coef_H_44,1.2214,F +coef_H_45,0.8544,F +coef_H_46,1.116,F +coef_H_47,1.974,F +coef_H_48,2.1615,F +coef_H_55,1.0484,F +coef_H_56_57,0,F +coef_H_58,1.9117,F +coef_H_66,3.192,F +coef_H_67,0,F +coef_H_68,0,F +coef_H_77,5.6222,F +coef_H_78,2.8078,F +coef_H_88,3.2327,F +coef_M_11,0,F +coef_M_12,0,F +coef_M_13,0.0627,F +coef_M_16,0,F +coef_M_17,0.1434,F +coef_M_18,0.3851,F +coef_M_22,0,F +coef_M_23,0.5967,F +coef_M_26,0,F +coef_M_27,0.4024,F +coef_M_28,0.4453,F +coef_M_33,0.3881,F +coef_M_36,0,F +coef_M_37,0.2755,F +coef_M_38,0.4148,F +coef_M_66,0.6854,F +coef_M_67,0.3692,F +coef_M_68,0.5467,F +coef_M_77,0.7729,F +coef_M_78,0,F +coef_M_88,0,F +coef_N_11,0.15,F +coef_N_12,0,F +coef_N_13,0,F +coef_N_14,0,F +coef_N_15,0,F +coef_N_16,1.0053,F +coef_N_17,0.3041,F +coef_N_18,0,F +coef_N_22,0,F +coef_N_23,0,F +coef_N_24,0,F +coef_N_25,0,F +coef_N_26,0,F +coef_N_27,0.3248,F +coef_N_28,0.9231,F +coef_N_33,0,F +coef_N_34,0,F +coef_N_35,0.8526,F +coef_N_36,0.9678,F +coef_N_37,0,F +coef_N_38,0.9241,F +coef_N_44,0,F +coef_N_45,0,F +coef_N_46,0.7134,F +coef_N_47,0.8509,F +coef_N_48,1.1721,F +coef_N_55,0,F +coef_N_56_57_58,0,F +coef_N_66,0,F +coef_N_67,1.8265,F +coef_N_68,1.1744,F +coef_N_77,2.5719,F +coef_N_78,0.7036,F +coef_N_88,0.4338,F +# 3-way interactions,, +coef_H_114,1.5072,F +coef_H_117_118,0.8382,F +coef_H_127_128,0.4246,F +coef_H_147_148,0.1548,F +coef_H_177_178_187_188,-0.7547,F +coef_H_277_278_287_288,-2.2535,F +coef_H_447_448,-0.9024,F +coef_H_477_478_487_488,-1.3723,F +coef_H_777_778_788_888,-2.4703,F +coef_M_111,0.298,F +coef_M_112,0.2032,F +coef_M_122,-0.6279,F +coef_M_127_128,-0.0432,F +coef_M_177_178_187_188,-0.1301,F +coef_M_227_228,-0.1524,F +coef_M_277_278_287_288,-0.1259,F +coef_M_777_778_788_888,-0.0112,F +coef_N_117_118,-0.5454,F +coef_N_144,-0.9496,F +coef_N_127_128,-1.7459,F +coef_N_147_148,-0.1659,F +coef_N_177_178_187_188,0.4687,F +coef_N_222,1.8781,F +coef_N_277_278_287_288,-0.6913,F +coef_N_477_478_487_488,-0.4894,F +coef_N_777_778_788_888,-0.0582,F +# cdap_all_people,, +coef_M_xxx,-0.114,F +coef_N_xxx,-0.4673,F +coef_H_xxx,-0.1538,F +coef_M_xxxx,0.4569,F +coef_N_xxxx,-0.4669,F +coef_H_xxxx,-0.4645,F +coef_M_xxxxx,-0.2607,F +coef_N_xxxxx,-1.4859,F +coef_H_xxxxx,-9,F diff --git a/test/cdap/configs/cdap_fixed_relative_proportions.csv b/test/cdap/configs/cdap_fixed_relative_proportions.csv new file mode 100644 index 000000000..788f398b6 --- /dev/null +++ b/test/cdap/configs/cdap_fixed_relative_proportions.csv @@ -0,0 +1,9 @@ +Description,Expression,M,N,H +Full-time worker,ptype == 1,0.79647,0.09368,0.10985 +Part-time worker,ptype == 2,0.61678,0.25757,0.12565 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 diff --git a/test/cdap/configs/cdap_indiv_and_hhsize1.csv b/test/cdap/configs/cdap_indiv_and_hhsize1.csv new file mode 100644 index 000000000..a5cb0d660 --- /dev/null +++ b/test/cdap/configs/cdap_indiv_and_hhsize1.csv @@ -0,0 +1,81 @@ +Description,Expression,M,N,H +Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult alternative-specific constants,ptype == 4,coef_non_working_adult_asc_M,coef_non_working_adult_asc_N, +Retired alternative-specific constants,ptype == 5,coef_retired_asc_M,coef_retired_asc_N, +Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N, +#,,,, +Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_N, +Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_N, +Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,, +Full-time worker interaction with age less than 35,(ptype == 1) & (age < 35),,coef_full_time_worker_interaction_with_age_less_than_35_N, +Part-time worker interaction with age less than 35,(ptype == 2) & (age < 35),coef_part_time_worker_interaction_with_age_less_than_35_N,coef_part_time_worker_interaction_with_age_less_than_35_N, +Retired interaction with age more than 80,(ptype == 5) & (age > 80),,, +#,,,, +Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,coef_full_time_worker_interaction_with_female_gender_N, +Part-time worker interaction with female gender,(ptype == 2) & (sex == 2),coef_part_time_worker_interaction_with_female_gender_M,coef_part_time_worker_interaction_with_female_gender_N, +University student interaction with female gender,(ptype == 3) & (sex == 2),coef_university_student_interaction_with_female_gender_M,coef_university_student_interaction_with_female_gender_N, +Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),,coef_non_working_adult_interaction_with_female_gender_N, +Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,coef_retired_interaction_with_female_N, +Driving-age child interaction with female,(ptype == 6) & (sex == 2),,coef_driving_age_child_who_is_in_school_interaction_with_female_N, +#,,,, +Full-time worker interaction with more cars than workers,(ptype == 1) & (autos > num_workers),,coef_full_time_worker_interaction_with_more_cars_than_workers_N, +Non-working adult interaction with more cars than workers,(ptype == 4) & (autos > num_workers),,coef_non_working_adult_interaction_with_more_cars_than_workers_N, +Retired interaction with more cars than workers,(ptype == 5) & (autos > num_workers),,coef_retired_interaction_with_more_cars_than_workers_N, +Driving-age child who is in school interaction with more cars than workers,(ptype == 6) & (autos > num_workers),coef_driving_age_child_who_is_in_school_interaction_with_more_cars_than_workers_M,, +Full-time worker interaction with zero cars,(ptype == 1) & (autos==0),coef_full_time_worker_interaction_with_zero_cars_M,, +Pre-driving-age child who is too young for school interaction with zero cars,(ptype == 8) & (autos==0),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_zero_cars_N, +Full-time worker interaction with fewer cars than workers,(ptype == 1) & (autos < num_workers),coef_full_time_worker_interaction_with_fewer_cars_than_workers_M,, +Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (autos < num_workers),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_M,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_N, +#,,,, +Full-time worker interaction with income less than $30k,(ptype == 1) & (income < 30000),coef_full_time_worker_interaction_with_income_less_than_30k_M,,coef_full_time_worker_interaction_with_income_less_than_30k_H +Part-time worker interaction with income less than $30k,(ptype == 2) & (income < 30000),coef_part_time_worker_interaction_with_income_less_than_30k_M,, +University student interaction with income less than $30k,(ptype == 3) & (income < 30000),coef_university_student_interaction_with_income_less_than_30k_M,, +University student interaction with income between $60k and $100k,(ptype == 3) & (income >= 60000) & (income <= 100000),,,coef_university_student_interaction_with_income_between_60k_and_100k_H +University student interaction with income more than $100k,(ptype == 3) & (income >= 100000),,,coef_university_student_interaction_with_income_more_than_100k_H +Non-working adult interaction with income more than $100k,(ptype == 4) & (income >= 100000),,,coef_non_working_adult_interaction_with_income_more_than_100k_H +Retired interaction with income between $60k and $100k,(ptype == 5) & (income >= 60000) & (income <= 100000),,, +Retired interaction with income more than $100k,(ptype == 5) & (income >= 100000),coef_retired_interaction_with_income_more_than_100k_M,,coef_retired_interaction_with_income_more_than_100k_H +Pre-driving-age child who is in school interaction with income less than $30k,(ptype == 7) & (income < 30000),coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_M,,coef_pre_driving_age_child_who_is_in_school_interaction_with_income_less_than_30k_H +Pre-driving-age child who is too young for school interaction with income between $60k and $100k,(ptype == 8) & (income >= 60000) & (income <= 100000),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_60k_and_100k_M,, +Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income >= 100000),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_M,, +#,,,, +Uinversity Student interaction with peak accessibility to all employment,(ptype == 3) * SchoolLocationLogsum,coef_university_student_interaction_with_school_location_mc_logsum_M,, +Full-time Worker interaction with accessibility to retail,"@np.where((df.ptype == 1)*(df.autosdf.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_full_time_worker_interaction_with_retail_accessibility_N, +Non-working adult interaction with accessibility to retail,"@np.where((df.ptype == 4)*(df.autosdf.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_non_working_adult_interaction_with_retail_accessibility_N, +Driving-age child who is in school interaction with accessibility to retail,"@np.where((df.ptype == 6)*(df.autosdf.num_workers),df.nonmandatory_hov2_accessibility,0)",,coef_driving_age_child_who_is_in_school_interaction_with_retail_accessibility_N, +#,,,, +Full-time worker interaction with usual work location is home,(ptype==1)&(WorkLocation==99999),coef_full_time_worker_interaction_with_usual_work_location_is_home_M,coef_full_time_worker_interaction_with_usual_work_location_is_home_N, +Part-time worker interaction with usual work location is home,(ptype==2)&(WorkLocation==99999),coef_part_time_worker_interaction_with_usual_work_location_is_home_M,, +Full-time worker interaction with no usual work location,(ptype==1)&((WorkLocation==99999)|(WorkLocation<=0)),coef_full_time_worker_interaction_with_no_usual_work_location_M,, +Part-time worker interaction with no usual work location,(ptype==2)&((WorkLocation==99999)|(WorkLocation<=0)),coef_part_time_worker_interaction_with_no_usual_work_location_M,, +#,,,, +Full-time worker interaction with detached house,(ptype==1)&(building_size<3),,,coef_full_time_worker_interaction_with_detached_home_H +Part-time worker interaction with detached house,(ptype==2)&(building_size<3),,,coef_part_time_worker_interaction_with_detached_home_H +Retired interaction with detached house,(ptype==5)&(building_size<3),,,coef_retired_interaction_with_detached_home_H +Pre-driving-age child in School interaction with detached house,(ptype==6)&(building_size<3),,,coef_driving_age_child_who_is_in_school_interaction_with_detached_home_H +#,,,, +Mandatory pattern unavailable for retired person,ptype==5,coef_UNAVAILABLE,, +Mandatory pattern unavailable for nonworking adult person type,ptype==4,coef_UNAVAILABLE,, +#,,,, +Alternative Specific Constant Adjustment for Full-time worker,ptype == 1,coef_full_time_worker_asc_adjustment_M,coef_full_time_worker_asc_adjustment_N, +Alternative Specific Constant Adjustment for Part-time worker,ptype == 2,coef_part_time_worker_asc_adjustment_M,coef_part_time_worker_asc_adjustment_N, +Alternative Specific Constant Adjustment for University student,ptype == 3,coef_university_student_asc_adjustment_M,coef_university_student_asc_adjustment_N, +Alternative Specific Constant Adjustment for Non-working adult,ptype == 4,,coef_non_working_adult_asc_adjustment_N, +Alternative Specific Constant Adjustment for Retired,ptype == 5,,coef_retired_asc_adjustment_N, +Alternative Specific Constant Adjustment for Driving-age child who is in school,ptype == 6,coef_driving_age_child_who_is_in_school_asc_adjustment_M,coef_driving_age_child_who_is_in_school_asc_adjustment_N, +Alternative Specific Constant Adjustment for Pre-driving-age child who is in school,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_adjustment_M,coef_pre_driving_age_child_who_is_in_school_asc_adjustment_N, +Alternative Specific Constant Adjustment for Pre-driving-age child who is too young for school,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_adjustment_N, +ASC Adjustment for Part-time worker -- TM2 calibration,ptype == 2,coef_part_time_worker_asc_adjustment_tm2_calib_M,coef_part_time_worker_asc_adjustment_tm2_calib_N, +ASC Adjustment for University student -- TM2 calibration,ptype == 3,coef_university_student_asc_adjustment_tm2_calib_M,coef_university_student_asc_adjustment_tm2_calib_N, +ASC Adjustment for Non-working adult -- TM2 calibration,ptype == 4,coef_non_working_adult_asc_adjustment_tm2_calib_M,coef_non_working_adult_asc_adjustment_tm2_calib_N, +ASC Adjustment for Retired --TM2 calibration,ptype == 5,coef_retired_asc_adjustment_tm2_calib_M,coef_retired_asc_adjustment_tm2_calib_N, diff --git a/test/cdap/configs/cdap_interaction_coefficients.csv b/test/cdap/configs/cdap_interaction_coefficients.csv new file mode 100644 index 000000000..f94fa1723 --- /dev/null +++ b/test/cdap/configs/cdap_interaction_coefficients.csv @@ -0,0 +1,177 @@ +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57 +H,57,coef_H_56_57 +H,58,coef_H_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,114,coef_H_114 +H,117,coef_H_117_118 +H,118,coef_H_117_118 +H,127,coef_H_127_128 +H,128,coef_H_127_128 +H,147,coef_H_147_148 +H,148,coef_H_147_148 +H,177,coef_H_177_178_187_188 +H,178,coef_H_177_178_187_188 +H,187,coef_H_177_178_187_188 +H,188,coef_H_177_178_187_188 +H,277,coef_H_277_278_287_288 +H,278,coef_H_277_278_287_288 +H,287,coef_H_277_278_287_288 +H,288,coef_H_277_278_287_288 +H,447,coef_H_447_448 +H,448,coef_H_447_448 +H,477,coef_H_477_478_487_488 +H,478,coef_H_477_478_487_488 +H,487,coef_H_477_478_487_488 +H,488,coef_H_477_478_487_488 +H,777,coef_H_777_778_788_888 +H,778,coef_H_777_778_788_888 +H,788,coef_H_777_778_788_888 +H,888,coef_H_777_778_788_888 +M,111,coef_M_111 +M,112,coef_M_112 +M,122,coef_M_122 +M,127,coef_M_127_128 +M,128,coef_M_127_128 +M,177,coef_M_177_178_187_188 +M,178,coef_M_177_178_187_188 +M,187,coef_M_177_178_187_188 +M,188,coef_M_177_178_187_188 +M,227,coef_M_227_228 +M,228,coef_M_227_228 +M,277,coef_M_277_278_287_288 +M,278,coef_M_277_278_287_288 +M,287,coef_M_277_278_287_288 +M,288,coef_M_277_278_287_288 +M,777,coef_M_777_778_788_888 +M,778,coef_M_777_778_788_888 +M,788,coef_M_777_778_788_888 +M,888,coef_M_777_778_788_888 +N,117,coef_N_117_118 +N,118,coef_N_117_118 +N,144,coef_N_144 +N,127,coef_N_127_128 +N,128,coef_N_127_128 +N,147,coef_N_147_148 +N,148,coef_N_147_148 +N,177,coef_N_177_178_187_188 +N,178,coef_N_177_178_187_188 +N,187,coef_N_177_178_187_188 +N,188,coef_N_177_178_187_188 +N,222,coef_N_222 +N,277,coef_N_277_278_287_288 +N,278,coef_N_277_278_287_288 +N,287,coef_N_277_278_287_288 +N,288,coef_N_277_278_287_288 +N,477,coef_N_477_478_487_488 +N,478,coef_N_477_478_487_488 +N,487,coef_N_477_478_487_488 +N,488,coef_N_477_478_487_488 +N,777,coef_N_777_778_788_888 +N,778,coef_N_777_778_788_888 +N,788,coef_N_777_778_788_888 +N,888,coef_N_777_778_788_888 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/test/cdap/configs/cdap_joint_tour_coefficients.csv b/test/cdap/configs/cdap_joint_tour_coefficients.csv new file mode 100644 index 000000000..14ee3fee3 --- /dev/null +++ b/test/cdap/configs/cdap_joint_tour_coefficients.csv @@ -0,0 +1,22 @@ +Label,description,Expression,dependency,coefficient +,constant - joint tour,1,,-3.1506 +,joint tour for hhsize 2,hhsize==2,,0.331420519 +,joint tour for hhsize 3,hhsize==3,,0.360848095 +,joint tour for hhsize 4,hhsize==4,,-0.118640717 +,joint tour for hhsize 5+,hhsize>=5,,-0.053509306 +,person x is adult and DAP is M,ptype_px<7,M_px,0.008 +,person x is adult and DAP is N,ptype_px<7,N_px,1.2557 +,person x is kid and DAP is M,ptype_px>6,M_px,0.1088 +,person x is kid and DAP is N,ptype_px>6,N_px,1.6898 +,Accessibility to retail employment/Non-Mandatory Attractions for Joint Tour,(autos_p1num_workers_p1)*nonmandatory_hov2_accessibility_p1,,0.055031985 +,Income less than $30k,income_p1<30000,,-0.192506367 +,Income between $60k and $100k,(income_p1>=60000)&(income_p1<=100000),,0.104325349 +,Income more than $100k,income_p1>100000,,0.104325349 +,No Car Households,autos_p1==0,,0 +,Cars Less than Workers,autos_p1num_workers_p1,,-0.005896499 +,Constant - Joint Tour - MTC TM2 Calibration,1,,-0.5918 +,WorkAccessForMandatoryDap,WorkLocationLogsum_px,M_px,0.17217579 +,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,ptype_pxprod<7|ptype_pxprod>6,H_px,-0.988838929 diff --git a/test/cdap/configs/constants.yaml b/test/cdap/configs/constants.yaml new file mode 100644 index 000000000..b0bd5a1f3 --- /dev/null +++ b/test/cdap/configs/constants.yaml @@ -0,0 +1,68 @@ +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Correction for transit skim expressions +# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and +# therefore need to be divided by the scale factor if used in expressions +TRANSIT_SCALE_FACTOR: 100 diff --git a/test/cdap/configs/initialize_households.yaml b/test/cdap/configs/initialize_households.yaml new file mode 100644 index 000000000..5d98a703d --- /dev/null +++ b/test/cdap/configs/initialize_households.yaml @@ -0,0 +1,37 @@ +annotate_tables: + - tablename: persons + column_map: + HHID: household_id + PERID: person_id + AGEP: age + SEX: sex + SCHL: education_attainment + OCCP: occupation + WKHP: hours_worked + WKW: weeks_worked + EMPLOYED: employment_status + ESR: esr + SCHG: grade_attending + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + column_map: + HHID: household_id + MAZ: home_zone_id + HHINCADJ: income + NWRKRS_ESR: num_workers + VEH: auto_ownership + NP: hhsize + HHT: hh_type + BLD: building_size + TYPE: hh_unit_type + MTCCountyID: county_id + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons + - land_use \ No newline at end of file diff --git a/test/cdap/configs/initialize_landuse.yaml b/test/cdap/configs/initialize_landuse.yaml new file mode 100644 index 000000000..7cc1d7e34 --- /dev/null +++ b/test/cdap/configs/initialize_landuse.yaml @@ -0,0 +1,54 @@ +annotate_tables: + - tablename: land_use + column_map: + MAZ_ORIGINAL: zone_id + CountyID: county_id + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + annotate: + SPEC: annotate_landuse + DF: land_use + - tablename: accessibility + column_map: + column_1: nonmandatory_auto_accessibility + column_2: nonmandatory_transit_accessibility + column_3: nonmandatory_nm_accessibility + column_4: nonmandatory_sov0_accessibility + column_5: nonmandatory_sov1_accessibility + column_6: nonmandatory_sov2_accessibility + column_7: nonmandatory_hov0_accessibility + column_8: nonmandatory_hov1_accessibility + column_9: nonmandatory_hov2_accessibility + column_10: shop_hov_insufficient_accessibility + column_11: shop_hov_sufficient_accessibility + column_12: shop_hov_oversufficient_accessibility + column_13: maint_hov_insufficient_accessibility + column_14: maint_hov_sufficient_accessibility + column_15: maint_hov_oversufficient_accessibility + column_16: eat_hov_insufficient_accessibility + column_17: eat_hov_sufficient_accessibility + column_18: eat_hov_oversufficient_accessibility + column_19: visit_hov_insufficient_accessibility + column_20: visit_hov_sufficient_accessibility + column_21: visit_hov_oversufficient_accessibility + column_22: discr_hov_insufficient_accessibility + column_23: discr_hov_sufficient_accessibility + column_24: discr_hov_oversufficient_accessibility + column_25: escort_hov_insufficient_accessibility + column_26: escort_hov_sufficient_accessibility + column_27: escort_hov_oversufficient_accessibility + column_28: shop_sov_insufficient_accessibility + column_29: shop_sov_sufficient_accessibility + column_30: shop_sov_oversufficient_accessibility + column_31: maint_sov_insufficient_accessibility + column_32: maint_sov_sufficient_accessibility + column_33: maint_sov_oversufficient_accessibility + column_40: discr_sov_insufficient_accessibility + column_41: discr_sov_sufficient_accessibility + column_42: discr_sov_oversufficient_accessibility + column_45: total_emp_accessibility + column_47: hh_walktransit_accessibility + mgra : zone_id \ No newline at end of file diff --git a/test/cdap/configs/network_los.yaml b/test/cdap/configs/network_los.yaml new file mode 100644 index 000000000..391125a38 --- /dev/null +++ b/test/cdap/configs/network_los.yaml @@ -0,0 +1,14 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/test/cdap/configs/settings.yaml b/test/cdap/configs/settings.yaml new file mode 100644 index 000000000..bd0f4acc9 --- /dev/null +++ b/test/cdap/configs/settings.yaml @@ -0,0 +1,60 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + unique_hh_id: household_id + NP: hhsize + hh_workers_from_esr: num_workers + VEH: auto_ownership + MAZ: home_zone_id + HINCP: income + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + unique_hh_id: household_id + AGEP: age + SPORDER: PNUM + SEX: sex + employed: pemploy + student_status: pstudent + person_type: ptype + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ_ORIGINAL: zone_id + CountyID: county_id + TAZ_ORIGINAL: TAZ + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + +trace_hh_id: 2 \ No newline at end of file diff --git a/test/cdap/configs/tour_departure_and_duration_alternatives.csv b/test/cdap/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 000000000..bddab06b9 --- /dev/null +++ b/test/cdap/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/test/cdap/data/.gitkeep b/test/cdap/data/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/cdap/test_cdap.py b/test/cdap/test_cdap.py new file mode 100644 index 000000000..df74740b4 --- /dev/null +++ b/test/cdap/test_cdap.py @@ -0,0 +1,413 @@ +import logging +import pytest +import os +import shutil +import pandas as pd +import numpy as np +from numpy import dot +from numpy.linalg import norm + +# import models is necessary to initalize the model steps with orca +from activitysim.abm import models +from activitysim.core import pipeline, config +from activitysim.core import tracing + +logger = logging.getLogger(__name__) + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return "cdap" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables(prepare_module_inputs) -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "MAZ_ORIGINAL", + "persons": "person_id", + "households": "household_id", + "accessibility": "MAZ_ORIGINAL", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return False + + +@pytest.fixture(scope="module") +def load_checkpoint() -> bool: + """ + checkpoint to be loaded from the pipeline when reconnecting. + """ + return "initialize_households" + + +# make a reconnect_pipeline internal to cdap +@pytest.mark.skipif( + os.path.isfile("test/cdap/output/pipeline.h5"), + reason="no need to recreate pipeline store if alreayd exist", +) +def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["initialize_landuse", "initialize_households"]) + + pipeline.close_pipeline() + + +def test_cdap_from_pipeline(reconnect_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["cdap_simulate"], resume_after="initialize_households") + + # get the updated pipeline data + person_df = pipeline.get_table("persons") + + # get the updated pipeline data + household_df = pipeline.get_table("households") + + ############################ + # person cdap pattern validation + ############################ + # cdap person result from the model + logger.info("person cdap pattern validation") + + target_key = "activity_pattern" + simulated_key = "cdap_activity" + similarity_threshold = 0.99 + + simulated_df = create_summary( + person_df, key=simulated_key, out_col="Simulated_Share" + ) + + # cdap person result from the TM2 run + target_df = create_summary(person_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + ############################ + # household joint pattern validation + ############################ + # cdap household result from the model + logger.info("household joint vs non-joint tour validation") + + target_key = "has_joint_tour_tm2" + simulated_key = "has_joint_tour" + similarity_threshold = 0.99 + + simulated_df = create_summary( + household_df, key=simulated_key, out_col="Simulated_Share" + ) + + # cdap household result from the TM2 run + household_df["has_joint_tour_tm2"] = np.where( + household_df["cdap_pattern"].str.endswith("j"), 1, 0 + ) + target_df = create_summary(household_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + ############################ + # one person household cdap pattern validation + ############################ + # cdap household result from the model + logger.info("one-person household cdap pattern validation") + + target_key = "cdap_pattern" + simulated_key = "cdap_activity" + similarity_threshold = 0.99 + + household_df["cdap_pattern"] = household_df["cdap_pattern"].apply( + lambda x: x[:-1].upper() if x.endswith("0") else x.upper() + ) + household_df["cdap_activity"] = household_df.apply( + lambda x: x["cdap_activity"] + "J" + if x["has_joint_tour"] == 1 + else x["cdap_activity"], + axis=1, + ) + + hh1_df = household_df[household_df.hhsize == 1].copy() + + simulated_df = create_summary(hh1_df, key=simulated_key, out_col="Simulated_Share") + target_df = create_summary(hh1_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + ############################ + # two person household cdap pattern validation + ############################ + # cdap household result from the model + logger.info("two-person household cdap pattern validation") + + target_key = "cdap_pattern" + simulated_key = "cdap_activity" + similarity_threshold = 0.99 + + hh2_df = household_df[household_df.hhsize == 2].copy() + + simulated_df = create_summary(hh2_df, key=simulated_key, out_col="Simulated_Share") + target_df = create_summary(hh2_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + ############################ + # three person household cdap pattern validation + ############################ + # cdap household result from the model + logger.info("three-person household cdap pattern validation") + + target_key = "cdap_pattern" + simulated_key = "cdap_activity" + similarity_threshold = 0.99 + + hh3_df = household_df[household_df.hhsize == 3].copy() + + simulated_df = create_summary(hh3_df, key=simulated_key, out_col="Simulated_Share") + target_df = create_summary(hh3_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + +# fetch/prepare existing files for model inputs +# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim +@pytest.fixture(scope="module") +def prepare_module_inputs() -> None: + """ + copy input files from sharepoint into test folder + + create unique person id in person file + + :return: None + """ + # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/ + # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1 + accessibility_file = os.path.join( + "test", "cdap", "data", "tm2_outputs", "accessibilities.csv" + ) + household_file = os.path.join("test", "cdap", "data", "popsyn", "households.csv") + person_file = os.path.join("test", "cdap", "data", "popsyn", "persons.csv") + landuse_file = os.path.join( + "test", "cdap", "data", "landuse", "maz_data_withDensity.csv" + ) + + test_dir = os.path.join("test", "cdap", "data") + + shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv")) + shutil.copy(household_file, os.path.join(test_dir, "households.csv")) + shutil.copy(person_file, os.path.join(test_dir, "persons.csv")) + shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv")) + + # add original maz id to accessibility table + land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv")) + accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv")) + + accessibility_df = pd.merge( + accessibility_df, + land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}), + how="left", + on="mgra", + ) + + accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False) + + # currently household file has to have these two columns, even before annotation + # because annotate person happens before household and uses these two columns + # TODO find a way to get around this + #### + household_df = pd.read_csv(os.path.join(test_dir, "households.csv")) + + household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"} + + household_df.rename(columns=household_columns_dict, inplace=True) + + tm2_simulated_household_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "householdData_3.csv") + ) + tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + household_df = pd.merge( + household_df, + tm2_simulated_household_df[ + [ + "household_id", + "autos", + "automated_vehicles", + "transponder", + "cdap_pattern", + "jtf_choice", + ] + ], + how="inner", # tm2 is not 100% sample run + on="household_id", + ) + + household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False) + + person_df = pd.read_csv(os.path.join(test_dir, "persons.csv")) + + person_columns_dict = {"HHID": "household_id", "PERID": "person_id"} + + person_df.rename(columns=person_columns_dict, inplace=True) + + tm2_simulated_person_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "personData_3.csv") + ) + tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + person_df = pd.merge( + person_df, + tm2_simulated_person_df[ + [ + "household_id", + "person_id", + "type", + "value_of_time", + "activity_pattern", + "imf_choice", + "inmf_choice", + "fp_choice", + "reimb_pct", + "workDCLogsum", + "schoolDCLogsum", + ] + ], + how="inner", # tm2 is not 100% sample run + on=["household_id", "person_id"], + ) + + # get tm2 simulated workplace and school location results + tm2_simulated_wsloc_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "wsLocResults_3.csv") + ) + tm2_simulated_wsloc_df.rename( + columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True + ) + + person_df = pd.merge( + person_df, + tm2_simulated_wsloc_df[ + [ + "household_id", + "person_id", + "WorkLocation", + "WorkLocationLogsum", # this is the same as `workDCLogsum` in tm2 person output + "SchoolLocation", + "SchoolLocationLogsum", # this is the same as `schoolDCLogsum` in tm2 person output + ] + ], + how="inner", # ctramp might not be 100% sample run + on=["household_id", "person_id"], + ) + + person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False) + #### + + +def create_summary(input_df, key, out_col="Share") -> pd.DataFrame: + """ + Create summary for the input data. + 1. group input data by the "key" column + 2. calculate the percent of input data records in each "key" category. + + :return: pd.DataFrame + """ + + out_df = input_df.groupby(key).size().reset_index(name="Count") + out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4) + + return out_df[[key, out_col]] + + +def cosine_similarity(a, b): + """ + Computes cosine similarity between two vectors. + + Cosine similarity is used here as a metric to measure similarity between two sequence of numbers. + Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them + i.e., dot products of the vectors divided by the product of their lengths. + + :return: + """ + + return dot(a, b) / (norm(a) * norm(b)) + + +def compare_simulated_against_target( + target_df: pd.DataFrame, + simulated_df: pd.DataFrame, + target_key: str, + simulated_key: str, +) -> bool: + """ + compares the simulated and target results by computing the cosine similarity between them. + + :return: + """ + + merged_df = pd.merge( + target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer" + ) + merged_df = merged_df.fillna(0) + + logger.info("simulated vs target share:\n%s" % merged_df) + + similarity_value = cosine_similarity( + merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist() + ) + + logger.info("cosine similarity:\n%s" % similarity_value) + + return similarity_value diff --git a/test/conftest.py b/test/conftest.py new file mode 100644 index 000000000..7d8b33203 --- /dev/null +++ b/test/conftest.py @@ -0,0 +1,105 @@ +import os + +import orca +import pandas as pd +import pytest +from activitysim.core import pipeline +from activitysim.core.los import Network_LOS as los + + +@pytest.fixture(scope="module") +def initialize_pipeline( + module: str, tables: dict[str, str], initialize_network_los: bool +) -> pipeline.Pipeline: + test_dir = os.path.join("test", module) + configs_dir = os.path.join(test_dir, "configs") + data_dir = os.path.join(test_dir, "data") + output_dir = os.path.join(test_dir, "output") + + if os.path.isdir(configs_dir): + orca.add_injectable("configs_dir", configs_dir) + + if os.path.isdir(data_dir): + orca.add_injectable("data_dir", data_dir) + + if os.path.isdir(test_dir): + if not os.path.isdir(output_dir): + os.mkdir(output_dir) + orca.add_injectable("output_dir", output_dir) + + # Read in the input test dataframes + for dataframe_name, idx_name in tables.items(): + df = pd.read_csv( + os.path.join("test", module, "data", f"{dataframe_name}.csv"), + index_col=idx_name, + ) + orca.add_table(dataframe_name, df) + + if initialize_network_los: + net_los = los() + net_los.load_data() + orca.add_injectable("network_los", net_los) + + # Add an output directory in current working directory if it's not already there + try: + os.makedirs("output") + except FileExistsError: + # directory already exists + pass + + # Add the dataframes to the pipeline + pipeline.open_pipeline() + pipeline.add_checkpoint(module) + pipeline.close_pipeline() + + # By convention, this method needs to yield something + yield pipeline._PIPELINE + + if pipeline.is_open(): + pipeline.close_pipeline() + + +@pytest.fixture(scope="module") +def reconnect_pipeline( + module: str, initialize_network_los: bool, load_checkpoint: str +) -> pipeline.Pipeline: + test_dir = os.path.join("test", module) + configs_dir = os.path.join(test_dir, "configs") + data_dir = os.path.join(test_dir, "data") + output_dir = os.path.join(test_dir, "output") + + if os.path.isdir(configs_dir): + orca.add_injectable("configs_dir", configs_dir) + + if os.path.isdir(data_dir): + orca.add_injectable("data_dir", data_dir) + + if os.path.isdir(test_dir): + if not os.path.isdir(output_dir): + os.mkdir(output_dir) + orca.add_injectable("output_dir", output_dir) + + # Read in the existing pipeline + orca.add_injectable("pipeline_file_path", output_dir) + + if initialize_network_los: + net_los = los() + net_los.load_data() + orca.add_injectable("network_los", net_los) + + # Add an output directory in current working directory if it's not already there + try: + os.makedirs("output") + except FileExistsError: + # directory already exists + pass + + pipeline.open_pipeline(resume_after=load_checkpoint) + pipeline.add_checkpoint(module) + pipeline.close_pipeline() + # By convention, this method needs to yield something + yield pipeline._PIPELINE + + # pytest teardown code + if pipeline.is_open(): + pipeline.close_pipeline() diff --git a/test/joint_tour_frequency/.gitkeep b/test/joint_tour_frequency/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/joint_tours/.gitkeep b/test/joint_tours/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/joint_tours/configs/JointTourFrequency.xls b/test/joint_tours/configs/JointTourFrequency.xls new file mode 100644 index 000000000..2eb7699bb Binary files /dev/null and b/test/joint_tours/configs/JointTourFrequency.xls differ diff --git a/test/joint_tours/configs/annotate_households.csv b/test/joint_tours/configs/annotate_households.csv new file mode 100644 index 000000000..eead2858c --- /dev/null +++ b/test/joint_tours/configs/annotate_households.csv @@ -0,0 +1,19 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)" +num_children,num_children,"_PERSON_COUNT('~adult', persons, households)" +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) +number of travel active fulltime workers,num_travel_active_full_time_workers,"_PERSON_COUNT('(ptype == 1) & (cdap_activity != \'H\')', persons, households)" +number of travel active parttime workers,num_travel_active_part_time_workers,"_PERSON_COUNT('(ptype == 2) & (cdap_activity != \'H\')', persons, households)" +number of travel active university student,num_travel_active_university_students,"_PERSON_COUNT('(ptype == 3) & (cdap_activity != \'H\')', persons, households)" +number of travel active non-workers,num_travel_active_non_workers,"_PERSON_COUNT('(ptype == 4) & (cdap_activity != \'H\')', persons, households)" +number of travel active retirees,num_travel_active_retirees,"_PERSON_COUNT('(ptype == 5) & (cdap_activity != \'H\')', persons, households)" +number of travel active driving age students,num_travel_active_driving_age_students,"_PERSON_COUNT('(ptype == 6) & (cdap_activity != \'H\')', persons, households)" +number of travel active pre-driving age school kids,num_travel_active_pre_driving_age_school_kids,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)" +number of travel active pre-school kids,num_travel_active_pre_school_kids,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)" +number of travel active adults,num_travel_active_adults,"_PERSON_COUNT('adult & (cdap_activity != \'H\')', persons, households)" +number of travel active chilren,num_travel_active_children,"_PERSON_COUNT('~adult & (cdap_activity != \'H\')', persons, households)" \ No newline at end of file diff --git a/test/joint_tours/configs/annotate_landuse.csv b/test/joint_tours/configs/annotate_landuse.csv new file mode 100644 index 000000000..65237c0ad --- /dev/null +++ b/test/joint_tours/configs/annotate_landuse.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / land_use.TOTACRE +employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE +population_density,population_density,land_use.TOTPOP / land_use.TOTACRE +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) \ No newline at end of file diff --git a/test/joint_tours/configs/annotate_persons.csv b/test/joint_tours/configs/annotate_persons.csv new file mode 100644 index 000000000..6af8a61d7 --- /dev/null +++ b/test/joint_tours/configs/annotate_persons.csv @@ -0,0 +1,16 @@ +Description,Target,Expression +#,, annotate persons table after import +,ptype,0 +,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)" +,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)" +,ptype,"np.where(persons.type == 'University student', 3, ptype)" +,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)" +,ptype,"np.where(persons.type == 'Retired', 5, ptype)" +,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)" +,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)" +,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)" +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +# adult is needed as it's harded coded in activitysim core overlap.py +adult,adult,ptype<=6 +child,child,ptype>6 \ No newline at end of file diff --git a/test/joint_tours/configs/annotate_persons_jtp.csv b/test/joint_tours/configs/annotate_persons_jtp.csv new file mode 100644 index 000000000..a72c86605 --- /dev/null +++ b/test/joint_tours/configs/annotate_persons_jtp.csv @@ -0,0 +1,3 @@ +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/test/joint_tours/configs/annotate_tours.csv b/test/joint_tours/configs/annotate_tours.csv new file mode 100644 index 000000000..225abe7f8 --- /dev/null +++ b/test/joint_tours/configs/annotate_tours.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +,tour_category,tours.tour_category.str.lower() +,tour_purpose,tours.tour_purpose.str.lower() +,tour_type,tours.tour_purpose.str.lower() +,origin,tours.orig_mgra +,destination,tours.dest_mgra diff --git a/test/joint_tours/configs/constants.yaml b/test/joint_tours/configs/constants.yaml new file mode 100644 index 000000000..b0bd5a1f3 --- /dev/null +++ b/test/joint_tours/configs/constants.yaml @@ -0,0 +1,68 @@ +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Correction for transit skim expressions +# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and +# therefore need to be divided by the scale factor if used in expressions +TRANSIT_SCALE_FACTOR: 100 diff --git a/test/joint_tours/configs/initialize_households.yaml b/test/joint_tours/configs/initialize_households.yaml new file mode 100644 index 000000000..a5cabc3df --- /dev/null +++ b/test/joint_tours/configs/initialize_households.yaml @@ -0,0 +1,38 @@ +annotate_tables: + - tablename: persons + column_map: + HHID: household_id + PERID: person_id + AGEP: age + SEX: sex + SCHL: education_attainment + OCCP: occupation + WKHP: hours_worked + WKW: weeks_worked + EMPLOYED: employment_status + ESR: esr + SCHG: grade_attending + activity_pattern: cdap_activity + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + column_map: + HHID: household_id + MAZ: home_zone_id + HHINCADJ: income + NWRKRS_ESR: num_workers + VEH: auto_ownership + NP: hhsize + HHT: hh_type + BLD: building_size + TYPE: hh_unit_type + MTCCountyID: county_id + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons + - land_use \ No newline at end of file diff --git a/test/joint_tours/configs/initialize_landuse.yaml b/test/joint_tours/configs/initialize_landuse.yaml new file mode 100644 index 000000000..7cc1d7e34 --- /dev/null +++ b/test/joint_tours/configs/initialize_landuse.yaml @@ -0,0 +1,54 @@ +annotate_tables: + - tablename: land_use + column_map: + MAZ_ORIGINAL: zone_id + CountyID: county_id + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + annotate: + SPEC: annotate_landuse + DF: land_use + - tablename: accessibility + column_map: + column_1: nonmandatory_auto_accessibility + column_2: nonmandatory_transit_accessibility + column_3: nonmandatory_nm_accessibility + column_4: nonmandatory_sov0_accessibility + column_5: nonmandatory_sov1_accessibility + column_6: nonmandatory_sov2_accessibility + column_7: nonmandatory_hov0_accessibility + column_8: nonmandatory_hov1_accessibility + column_9: nonmandatory_hov2_accessibility + column_10: shop_hov_insufficient_accessibility + column_11: shop_hov_sufficient_accessibility + column_12: shop_hov_oversufficient_accessibility + column_13: maint_hov_insufficient_accessibility + column_14: maint_hov_sufficient_accessibility + column_15: maint_hov_oversufficient_accessibility + column_16: eat_hov_insufficient_accessibility + column_17: eat_hov_sufficient_accessibility + column_18: eat_hov_oversufficient_accessibility + column_19: visit_hov_insufficient_accessibility + column_20: visit_hov_sufficient_accessibility + column_21: visit_hov_oversufficient_accessibility + column_22: discr_hov_insufficient_accessibility + column_23: discr_hov_sufficient_accessibility + column_24: discr_hov_oversufficient_accessibility + column_25: escort_hov_insufficient_accessibility + column_26: escort_hov_sufficient_accessibility + column_27: escort_hov_oversufficient_accessibility + column_28: shop_sov_insufficient_accessibility + column_29: shop_sov_sufficient_accessibility + column_30: shop_sov_oversufficient_accessibility + column_31: maint_sov_insufficient_accessibility + column_32: maint_sov_sufficient_accessibility + column_33: maint_sov_oversufficient_accessibility + column_40: discr_sov_insufficient_accessibility + column_41: discr_sov_sufficient_accessibility + column_42: discr_sov_oversufficient_accessibility + column_45: total_emp_accessibility + column_47: hh_walktransit_accessibility + mgra : zone_id \ No newline at end of file diff --git a/test/joint_tours/configs/initialize_tours.yaml b/test/joint_tours/configs/initialize_tours.yaml new file mode 100644 index 000000000..999382c62 --- /dev/null +++ b/test/joint_tours/configs/initialize_tours.yaml @@ -0,0 +1,6 @@ +# +annotate_tours: + SPEC: annotate_tours + DF: tours + TABLES: + - land_use \ No newline at end of file diff --git a/test/joint_tours/configs/joint_tour_frequency_composition.csv b/test/joint_tours/configs/joint_tour_frequency_composition.csv new file mode 100644 index 000000000..2cace7b25 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition.csv @@ -0,0 +1,99 @@ +Label,Description,Expression,Coefficient +,Constant for shopping tour,@df.shopping,coef_constant_for_shopping_tour +,Constant for Maintenance tour,@df.othmaint,coef_constant_for_maintenance_tour +,Constant for eating out tour,@df.eatout,coef_constant_for_eating_out_tour +,Constant for visiting tour,@df.social,coef_constant_for_visiting_tour +,Constant for discretionary tour,@df.othdiscr,coef_constant_for_discretionary_tour +,Constant for 2 shopping tour,@df.shopping==2,coef_constant_for_2_shopping_tour +,1 Shopping and 1 Maintenance Tour,@((df.shopping==1) & (df.othmaint==1)),coef_1_shopping_and_1_maintenance_tour +,1 Shopping and 1 Eating Out Tour,@((df.shopping==1) & (df.eatout==1)),coef_1_shopping_and_1_eating_out_tour +,1 Shopping and 1 Visiting Tour,@((df.shopping==1) & (df.social==1)),coef_1_shopping_and_1_visiting_tour +,1 Shopping and 1 Discretionary Tour,@((df.shopping==1) & (df.othdiscr==1)),coef_1_shopping_and_1_discretionary_tour +,Constant for 2 Maintenance tour,@df.othmaint==2,coef_constant_for_2_maintenance_tour +,1 Maintenance and 1 Eating Out Tour,@((df.othmaint==1) & (df.eatout==1)),coef_1_maintenance_and_1_eating_out_tour +,1 Maintenance and 1 Visiting Tour,@((df.othmaint==1) & (df.social==1)),coef_1_maintenance_and_1_visiting_tour +,1 Maintenance and 1 Discretionary Tour,@((df.othmaint==1) & (df.othdiscr==1)),coef_1_maintenance_and_1_discretionary_tour +,Constant for 2 eating out tour,@df.eatout==2,coef_constant_for_2_eating_out_tour +,1 Eating Out and 1 Visiting Tour,@((df.eatout==1) & (df.social==1)),coef_1_eating_out_and_1_visiting_tour +,1 Eating Out and 1 Discretionary Tour,@((df.eatout==1) & (df.othdiscr==1)),coef_1_eating_out_and_1_discretionary_tour +,Constant for 2 visiting tour,@df.social==2,coef_constant_for_2_visiting_tour +,1 Visiting and 1 Discretionary Tour,@((df.social==1) & (df.othdiscr==1)),coef_1_visiting_and_1_discretionary_tour +,Constant for 2 discretionary tour,othdiscr==2,coef_constant_for_2_discretionary_tour +,Number of Active Full time workers /Shopping,num_travel_active_full_time_workers * shopping,coef_number_of_active_full_time_workers_shopping +,Number of Active Non-workers /Shopping,num_travel_active_non_workers * shopping,coef_number_of_active_nonworkers_shopping +,Number of Active Pre- Driving Age School Children /Shopping,num_travel_active_pre_driving_age_school_kids * shopping,coef_number_of_active_pre_driving_age_school_children_shopping +,Number of Active Preschool Children /Shopping,num_travel_active_pre_school_kids * shopping,coef_number_of_active_preschool_children_shopping +,Number of Active Non-workers /Maintenance,num_travel_active_non_workers * othmaint,coef_number_of_active_nonworkers_maintenance +,Number of Active Retirees /Maintenance,num_travel_active_retirees * othmaint,coef_number_of_active_retirees_maintenance +,Number of Active Driving Age School Children /Maintenance,num_travel_active_driving_age_students * othmaint,coef_number_of_active_driving_age_school_children_maintenance +,Number of Active Preschool Children /Maintenance,num_travel_active_pre_school_kids * othmaint,coef_number_of_active_preschool_children_maintenance +,Number of Active Full time workers /Eating Out,num_travel_active_full_time_workers * eatout,coef_number_of_active_full_time_workers_eating_out +,Number of Active University Students /Eating Out,num_travel_active_university_students * eatout,coef_number_of_active_university_students_eating_out +,Number of Active Retirees /Eating Out,num_travel_active_retirees * eatout,coef_number_of_active_retirees_eating_out +,Number of Active Pre- Driving Age School Children /Eating Out,num_travel_active_pre_driving_age_school_kids * eatout,coef_number_of_active_pre_driving_age_school_children_eating_out +,Number of Active Preschool Children /Eating Out,num_travel_active_pre_school_kids * eatout,coef_number_of_active_preschool_children_eating_out +,Number of Active Pre- Driving Age School Children /Visiting,num_travel_active_pre_driving_age_school_kids * social,coef_number_of_active_pre_driving_age_school_children_visiting +,Number of Active Preschool Children /Visiting,num_travel_active_pre_school_kids * social,coef_number_of_active_preschool_children_visiting +,Number of Active Part time workers /Discretionary,num_travel_active_part_time_workers * othdiscr,coef_number_of_active_part_time_workers_discretionary +,Number of Active University Students /Discretionary,num_travel_active_university_students * othdiscr,coef_number_of_active_university_students_discretionary +,Number of Active Driving Age School Children /Discretionary,num_travel_active_driving_age_students * othdiscr,coef_number_of_active_driving_age_school_children_discretionary +,Number of Active Preschool Children /Discretionary,num_travel_active_pre_school_kids * othdiscr,coef_number_of_active_preschool_children_discretionary +,HH has more autos than workers/ Maintenance,(auto_ownership > num_workers) * othmaint,coef_hh_has_more_autos_than_workers_maintenance +,Income > $100k /Maintenance,(income>100000) * othmaint,coef_income_gtr_100k_maintenance +,"Income Less than $29,999 / Eating Out",(income<30000) * eatout,coef_income_less_than_29999__eating_out +,"Income Between $30,000 to $59,999 / Eating Out",((income>=30000) & (income<60000)) * othmaint,coef_income_between_30000_to_59999__eating_out +,"Income Less than $29,999 / Discretionary",(income<30000) * othdiscr,coef_income_less_than_29999__discretionary +,"Income Between $30,000 to $59,999 / Discretionary",((income>=30000) & (income<60000)) * othdiscr,coef_income_between_30000_to_59999__discretionary +,Shopping HOV accessibility for 2 Tours,((autosnum_workers)*shop_hov_oversufficient_accessibility)*(num_joint_tours==2)*shopping,coef_shopping_hov_accessibility_for_2_tours +,Maintenance HOV Accessibility,((autosnum_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_maintenance_hov_accessibility +,Discretionary HOV Accessibility,((autosnum_workers)*discr_hov_oversufficient_accessibility)*othdiscr,coef_discretionary_hov_accessibility +,Constant for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_children_party_shopping_tour +,Constant for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_constant_for_children_party_maintenance_tour +,Constant for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_constant_for_children_party_eating_out_tour +,Constant for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_constant_for_children_party_visiting_tour +,Constant for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_constant_for_children_party_discretionary_tour +,Constant for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_constant_for_mixed_party_shopping_tour +,Constant for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_constant_for_mixed_party_maintenance_tour +,Constant for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_constant_for_mixed_party_eating_out_tour +,Constant for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_constant_for_mixed_party_visiting_tour +,Constant for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_constant_for_mixed_party_discretionary_tour +,Number of Active Full time workers /Adult Party,num_travel_active_full_time_workers * (party1==1) + num_travel_active_full_time_workers * (party2==1),coef_number_of_active_full_time_workers_adult_party +,Number of Active Part time workers /Adult Party,num_travel_active_part_time_workers * (party1==1) + num_travel_active_part_time_workers * (party2==1),coef_number_of_active_part_time_workers_adult_party +,Number of Active University Students /Adult Party,num_travel_active_university_students * (party1==1) + num_travel_active_university_students * (party2==1),coef_number_of_active_university_students_adult_party +,Number of Active Non-workers /Adult Party,num_travel_active_non_workers * (party1==1) + num_travel_active_non_workers * (party2==1),coef_number_of_active_nonworkers_adult_party +,Number of Active Retirees /Adult Party,num_travel_active_retirees * (party1==1) + num_travel_active_retirees * (party2==1),coef_number_of_active_retirees_adult_party +,Number of Active Driving Age School Children /Children Party,num_travel_active_driving_age_students * (party1==1) + num_travel_active_driving_age_students * (party2==1),coef_number_of_active_driving_age_school_children_children_party +,Number of Active Pre- Driving Age School Children /Children Party,num_travel_active_pre_driving_age_school_kids * (party1==2) + num_travel_active_pre_driving_age_school_kids * (party2==2),coef_number_of_active_pre_driving_age_school_children_children_party +,Number of Active Part time workers /Mixed Party,num_travel_active_part_time_workers * (party1==2) + num_travel_active_part_time_workers * (party2==2),coef_number_of_active_part_time_workers_mixed_party +,Number of Active Driving Age School Children /Mixed Party,num_travel_active_driving_age_students * (party1==3) + num_travel_active_driving_age_students * (party2==3),coef_number_of_active_driving_age_school_children_mixed_party +,Number of Active Pre- Driving Age School Children /Mixed Party,num_travel_active_pre_driving_age_school_kids * (party1==3) + num_travel_active_pre_driving_age_school_kids * (party2==3),coef_number_of_active_pre_driving_age_school_children_mixed_party +,Number of Active Preschool Children /Mixed Party,num_travel_active_pre_school_kids * (party1==3) + num_travel_active_pre_school_kids * (party2==3),coef_number_of_active_preschool_children_mixed_party +,HH has no autos / Mixed Party,@(df.autos==0)*(df.party1==3)+(df.autos==0)*(df.party2==3),coef_hh_has_no_autos__mixed_party +,HH has less autos than workers/ Mixed Party,@(df.autos100000)*(df.party1==2)+(df.income>100000)*(df.party2==2),coef_income_more_than_100k_child_party +,Income more than $100k /Mixed Party,@(df.income>100000)*(df.party1==3)+(df.income>100000)*(df.party2==3),coef_income_more_than_100k_mixed_party +,Log of max window overlaps between adults,@df.log_time_window_overlap_adult*((df.party1==1)+(df.party2==1)),coef_log_of_max_window_overlaps_between_adults +,Log of max window overlaps between children,@df.log_time_window_overlap_child*((df.party1==2)+(df.party2==2)),coef_log_of_max_window_overlaps_between_children +,Log of max window overlaps between adult & child,@df.log_time_window_overlap_adult_child*((df.party1==3)+(df.party2==3)),coef_log_of_max_window_overlaps_between_adult_child +,Not more than 1 travel active adult in HH,@(df.num_travel_active_adults < 2)*(((df.party1==1)+(df.party2==1))>0),coef_unavailable +,Not more than 1 travel active child in HH,@(df.num_travel_active_children < 2)*(((df.party1==2)+(df.party2==2))>0),coef_unavailable +,No travel-active pair adult-child in HH ,@((df.num_travel_active_adults*df.num_travel_active_children) ==0)*(((df.party1==3)+(df.party2==3))>0),coef_unavailable +,Adjustment for Children Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_children_party_shopping_tour +,Adjustment for Children Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==2)+(df.purpose2==6)*(df.party2==2),coef_adjustment_for_children_party_maintenance_tour +,Adjustment for Children Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==2)+(df.purpose2==7)*(df.party2==2),coef_adjustment_for_children_party_eating_out_tour +,Adjustment for Children Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==2)+(df.purpose2==8)*(df.party2==2),coef_adjustment_for_children_party_visiting_tour +,Adjustment for Children Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==2)+(df.purpose2==9)*(df.party2==2),coef_adjustment_for_children_party_discretionary_tour +,Adjustment for Mixed Party/ Shopping Tour,@(df.purpose1==5)*(df.party1==2)+(df.purpose2==5)*(df.party2==2),coef_adjustment_for_mixed_party_shopping_tour +,Adjustment for Mixed Party/ Maintenance Tour,@(df.purpose1==6)*(df.party1==3)+(df.purpose2==6)*(df.party2==3),coef_adjustment_for_mixed_party_maintenance_tour +,Adjustment for Mixed Party/ Eating Out Tour,@(df.purpose1==7)*(df.party1==3)+(df.purpose2==7)*(df.party2==3),coef_adjustment_for_mixed_party_eating_out_tour +,Adjustment for Mixed Party/ Visiting Tour,@(df.purpose1==8)*(df.party1==3)+(df.purpose2==8)*(df.party2==3),coef_adjustment_for_mixed_party_visiting_tour +,Adjustment for Mixed Party/ Discretionary Tour,@(df.purpose1==9)*(df.party1==3)+(df.purpose2==9)*(df.party2==3),coef_adjustment_for_mixed_party_discretionary_tour +,Adjustment for shopping tour,shopping,coef_adjustment_for_shopping_tour +,Adjustment for Maintenance tour,othmaint,coef_adjustment_for_maintenance_tour +,Adjustment for eating out tour,eatout,coef_adjustment_for_eating_out_tour +,Adjustment for visiting tour,social,coef_adjustment_for_visiting_tour +,Adjustment for discretionary tour,othdiscr,coef_adjustment_for_discretionary_tour +,Adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_adjustment_for_share_of_2_joint_tours +,TM2 Adjustment for Children Party,@(df.party1==2)+(df.party2==2),coef_tm2_adjustment_for_children_party +,TM2 Adjustment for Mixed Party,@(df.party1==3)+(df.party2==3),coef_tm2_adjustment_for_mixed_party +,TM2 adjustment for share of 2 Joint Tours,num_joint_tours==2,coef_tm2_adjustment_for_share_of_2_joint_tours diff --git a/test/joint_tours/configs/joint_tour_frequency_composition.yaml b/test/joint_tours/configs/joint_tour_frequency_composition.yaml new file mode 100644 index 000000000..ce31ea8df --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition.yaml @@ -0,0 +1,35 @@ +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency_composition.csv +COEFFICIENTS: joint_tour_frequency_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_composition_annotate_households_preprocessor.csv + DF: households + TABLES: + - persons + +ALTS_PREPROCESSOR: + SPEC: joint_tour_frequency_composition_annotate_alt_preprocessor.csv + DF: alt_tdd + +# define the structure of alternative table +ALTS_TABLE_STRUCTURE: + PURPOSE: + COLUMNS: + - purpose1 + - purpose2 + VALUE_MAP: + 5: shopping + 6: othmaint + 7: eatout + 8: social + 9: othdiscr + COMPOSITION: + COLUMNS: + - party1 + - party2 + VALUE_MAP: + 1: adults + 2: children + 3: mixed \ No newline at end of file diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv b/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv new file mode 100644 index 000000000..657f98741 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv @@ -0,0 +1,151 @@ +alt,purpose1,purpose2,party1,party2 +1,5,0,1,0 +2,5,0,2,0 +3,5,0,3,0 +4,6,0,1,0 +5,6,0,2,0 +6,6,0,3,0 +7,7,0,1,0 +8,7,0,2,0 +9,7,0,3,0 +10,8,0,1,0 +11,8,0,2,0 +12,8,0,3,0 +13,9,0,1,0 +14,9,0,2,0 +15,9,0,3,0 +16,5,5,1,1 +17,5,5,1,2 +18,5,5,1,3 +19,5,5,2,1 +20,5,5,2,2 +21,5,5,2,3 +22,5,5,3,1 +23,5,5,3,2 +24,5,5,3,3 +25,5,6,1,1 +26,5,6,1,2 +27,5,6,1,3 +28,5,6,2,1 +29,5,6,2,2 +30,5,6,2,3 +31,5,6,3,1 +32,5,6,3,2 +33,5,6,3,3 +34,5,7,1,1 +35,5,7,1,2 +36,5,7,1,3 +37,5,7,2,1 +38,5,7,2,2 +39,5,7,2,3 +40,5,7,3,1 +41,5,7,3,2 +42,5,7,3,3 +43,5,8,1,1 +44,5,8,1,2 +45,5,8,1,3 +46,5,8,2,1 +47,5,8,2,2 +48,5,8,2,3 +49,5,8,3,1 +50,5,8,3,2 +51,5,8,3,3 +52,5,9,1,1 +53,5,9,1,2 +54,5,9,1,3 +55,5,9,2,1 +56,5,9,2,2 +57,5,9,2,3 +58,5,9,3,1 +59,5,9,3,2 +60,5,9,3,3 +61,6,6,1,1 +62,6,6,1,2 +63,6,6,1,3 +64,6,6,2,1 +65,6,6,2,2 +66,6,6,2,3 +67,6,6,3,1 +68,6,6,3,2 +69,6,6,3,3 +70,6,7,1,1 +71,6,7,1,2 +72,6,7,1,3 +73,6,7,2,1 +74,6,7,2,2 +75,6,7,2,3 +76,6,7,3,1 +77,6,7,3,2 +78,6,7,3,3 +79,6,8,1,1 +80,6,8,1,2 +81,6,8,1,3 +82,6,8,2,1 +83,6,8,2,2 +84,6,8,2,3 +85,6,8,3,1 +86,6,8,3,2 +87,6,8,3,3 +88,6,9,1,1 +89,6,9,1,2 +90,6,9,1,3 +91,6,9,2,1 +92,6,9,2,2 +93,6,9,2,3 +94,6,9,3,1 +95,6,9,3,2 +96,6,9,3,3 +97,7,7,1,1 +98,7,7,1,2 +99,7,7,1,3 +100,7,7,2,1 +101,7,7,2,2 +102,7,7,2,3 +103,7,7,3,1 +104,7,7,3,2 +105,7,7,3,3 +106,7,8,1,1 +107,7,8,1,2 +108,7,8,1,3 +109,7,8,2,1 +110,7,8,2,2 +111,7,8,2,3 +112,7,8,3,1 +113,7,8,3,2 +114,7,8,3,3 +115,7,9,1,1 +116,7,9,1,2 +117,7,9,1,3 +118,7,9,2,1 +119,7,9,2,2 +120,7,9,2,3 +121,7,9,3,1 +122,7,9,3,2 +123,7,9,3,3 +124,8,8,1,1 +125,8,8,1,2 +126,8,8,1,3 +127,8,8,2,1 +128,8,8,2,2 +129,8,8,2,3 +130,8,8,3,1 +131,8,8,3,2 +132,8,8,3,3 +133,8,9,1,1 +134,8,9,1,2 +135,8,9,1,3 +136,8,9,2,1 +137,8,9,2,2 +138,8,9,2,3 +139,8,9,3,1 +140,8,9,3,2 +141,8,9,3,3 +142,9,9,1,1 +143,9,9,1,2 +144,9,9,1,3 +145,9,9,2,1 +146,9,9,2,2 +147,9,9,2,3 +148,9,9,3,1 +149,9,9,3,2 +150,9,9,3,3 diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv new file mode 100644 index 000000000..389d28a13 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +,shopping,"np.where(alt_tdd.purpose1 == 5, 1, 0) + np.where(alt_tdd.purpose2 == 5, 1, 0)" +,othmaint,"np.where(alt_tdd.purpose1 == 6, 1, 0) + np.where(alt_tdd.purpose2 == 6, 1, 0)" +,eatout,"np.where(alt_tdd.purpose1 == 7, 1, 0) + np.where(alt_tdd.purpose2 == 7, 1, 0)" +,social,"np.where(alt_tdd.purpose1 == 8, 1, 0) + np.where(alt_tdd.purpose2 == 8, 1, 0)" +,othdiscr,"np.where(alt_tdd.purpose1 == 9, 1, 0) + np.where(alt_tdd.purpose2 == 9, 1, 0)" +,num_joint_tours,shopping+othmaint+eatout+social+othdiscr \ No newline at end of file diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv new file mode 100644 index 000000000..1b4a5c91d --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) diff --git a/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv b/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv new file mode 100644 index 000000000..da82e8fa9 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv @@ -0,0 +1,97 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_constant_for_shopping_tour,0,T +coef_constant_for_maintenance_tour,-1.477,F +coef_constant_for_eating_out_tour,0.5796,F +coef_constant_for_visiting_tour,-1.0037,F +coef_constant_for_discretionary_tour,-1.1195,F +coef_constant_for_2_shopping_tour,-13.70946409,F +coef_1_shopping_and_1_maintenance_tour,-12.13684801,F +coef_1_shopping_and_1_eating_out_tour,-12.79892713,F +coef_1_shopping_and_1_visiting_tour,-12.22077221,F +coef_1_shopping_and_1_discretionary_tour,-12.57867869,F +coef_constant_for_2_maintenance_tour,-13.43572169,F +coef_1_maintenance_and_1_eating_out_tour,-12.18915033,F +coef_1_maintenance_and_1_visiting_tour,-11.81267905,F +coef_1_maintenance_and_1_discretionary_tour,-12.28867197,F +coef_constant_for_2_eating_out_tour,-13.15388273,F +coef_1_eating_out_and_1_visiting_tour,-13.15388273,F +coef_1_eating_out_and_1_discretionary_tour,-12.56102569,F +coef_constant_for_2_visiting_tour,-13.15388273,F +coef_1_visiting_and_1_discretionary_tour,-12.37222202,F +coef_constant_for_2_discretionary_tour,-13.23532342,F +coef_number_of_active_full_time_workers_shopping,0.09864381,F +coef_number_of_active_nonworkers_shopping,0.393630718,F +coef_number_of_active_pre_driving_age_school_children_shopping,-0.313021042,F +coef_number_of_active_preschool_children_shopping,-1.213950718,F +coef_number_of_active_nonworkers_maintenance,0.322738327,F +coef_number_of_active_retirees_maintenance,0.298632515,F +coef_number_of_active_driving_age_school_children_maintenance,0.503901856,F +coef_number_of_active_preschool_children_maintenance,-1.160523204,F +coef_number_of_active_full_time_workers_eating_out,-0.305934663,F +coef_number_of_active_university_students_eating_out,-0.65706029,F +coef_number_of_active_retirees_eating_out,-0.391710731,F +coef_number_of_active_pre_driving_age_school_children_eating_out,-0.25140082,F +coef_number_of_active_preschool_children_eating_out,-1.700731169,F +coef_number_of_active_pre_driving_age_school_children_visiting,0.162335324,F +coef_number_of_active_preschool_children_visiting,-0.96955678,F +coef_number_of_active_part_time_workers_discretionary,0.217898126,F +coef_number_of_active_university_students_discretionary,-0.610578234,F +coef_number_of_active_driving_age_school_children_discretionary,0.359485499,F +coef_number_of_active_preschool_children_discretionary,-1.244458661,F +coef_hh_has_more_autos_than_workers_maintenance,-0.336188392,F +coef_income_gtr_100k_maintenance,-0.475730683,F +coef_income_less_than_29999__eating_out,-1.282190776,F +coef_income_between_30000_to_59999__eating_out,-0.275046208,F +coef_income_less_than_29999__discretionary,-0.352579013,F +coef_income_between_30000_to_59999__discretionary,-0.191735343,F +coef_shopping_hov_accessibility_for_2_tours,0.039513822,F +coef_maintenance_hov_accessibility,0.128132691,F +coef_discretionary_hov_accessibility,0.089590553,F +coef_constant_for_children_party_shopping_tour,-5.374907972,F +coef_constant_for_children_party_maintenance_tour,-5.144798184,F +coef_constant_for_children_party_eating_out_tour,-4.09806907,F +coef_constant_for_children_party_visiting_tour,-4.09806907,F +coef_constant_for_children_party_discretionary_tour,-4.09806907,F +coef_constant_for_mixed_party_shopping_tour,0.575879495,F +coef_constant_for_mixed_party_maintenance_tour,0.515873866,F +coef_constant_for_mixed_party_eating_out_tour,0.168592084,F +coef_constant_for_mixed_party_visiting_tour,0.078060666,F +coef_constant_for_mixed_party_discretionary_tour,0.856042068,F +coef_number_of_active_full_time_workers_adult_party,0.599335502,F +coef_number_of_active_part_time_workers_adult_party,1.113944272,F +coef_number_of_active_university_students_adult_party,0.231138167,F +coef_number_of_active_nonworkers_adult_party,0.341446999,F +coef_number_of_active_retirees_adult_party,0.657220801,F +coef_number_of_active_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_pre_driving_age_school_children_children_party,0.580109231,F +coef_number_of_active_part_time_workers_mixed_party,0.522327137,F +coef_number_of_active_driving_age_school_children_mixed_party,0.216908669,F +coef_number_of_active_pre_driving_age_school_children_mixed_party,0.31440104,F +coef_number_of_active_preschool_children_mixed_party,0.897670235,F +coef_hh_has_no_autos__mixed_party,-2.920728233,F +coef_hh_has_less_autos_than_workers_mixed_party,-0.546339245,F +coef_income_more_than_100k_child_party,-1.189112151,F +coef_income_more_than_100k_mixed_party,-0.303217156,F +coef_log_of_max_window_overlaps_between_adults,2.96902119,F +coef_log_of_max_window_overlaps_between_children,4.673601828,F +coef_log_of_max_window_overlaps_between_adult_child,3.523795377,F +coef_adjustment_for_children_party_shopping_tour,0.407745781,F +coef_adjustment_for_children_party_maintenance_tour,0.284925252,F +coef_adjustment_for_children_party_eating_out_tour,0,T +coef_adjustment_for_children_party_visiting_tour,0.966264444,F +coef_adjustment_for_children_party_discretionary_tour,0.144740532,F +coef_adjustment_for_mixed_party_shopping_tour,0.681178558,F +coef_adjustment_for_mixed_party_maintenance_tour,0.476324741,F +coef_adjustment_for_mixed_party_eating_out_tour,0.827297043,F +coef_adjustment_for_mixed_party_visiting_tour,0.691826384,F +coef_adjustment_for_mixed_party_discretionary_tour,0.697808415,F +coef_adjustment_for_shopping_tour,0,T +coef_adjustment_for_maintenance_tour,0.06575155,F +coef_adjustment_for_eating_out_tour,0.0643679,F +coef_adjustment_for_visiting_tour,0.00785518,F +coef_adjustment_for_discretionary_tour,0.076075677,F +coef_adjustment_for_share_of_2_joint_tours,-1.214059893,F +coef_tm2_adjustment_for_children_party,0,F +coef_tm2_adjustment_for_mixed_party,0,F +coef_tm2_adjustment_for_share_of_2_joint_tours,-0.5,F diff --git a/test/joint_tours/configs/joint_tour_participation.csv b/test/joint_tours/configs/joint_tour_participation.csv new file mode 100644 index 000000000..9f3151129 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_participation.csv @@ -0,0 +1,67 @@ +Label,Description,Expression,participate,not_participate +,Dummy for full-time worker in adult party,(ptype==1) & (composition=='adults'),coef_full_time_worker_adults_only_party, +,Dummy for part-time worker in adult party,(ptype==2) & (composition=='adults'),coef_part_time_worker_adults_only_party, +,Dummy for university student in adult party,(ptype==3) & (composition=='adults'),coef_university_students_adults_only_party, +,Dummy for nonworker in adult party,(ptype==4) & (composition=='adults'),coef_non_worker_adults_only_party, +,Dummy for retiree in adult party,(ptype==5) & (composition=='adults'),coef_retiree_adults_only_party, +,Dummy for driving school child in children party,(ptype==6) & (composition=='children'),coef_driving_age_student_children_only_party, +,Dummy for pre-driving school child in children party,(ptype==7) & (composition=='children'),coef_pre_driving_age_student_children_only_party, +,Dummy for preschool child in children party,(ptype==8) & (composition=='children'),coef_child_too_young_for_school_children_only_party, +util_full_time_worker_mixed_party,Dummy for full-time worker in mixed party,(ptype==1) & (composition=='mixed'),coef_full_time_worker_mixed_party, +util_part_time_worker_mixed_party,Dummy for part-time worker in mixed party,(ptype==2) & (composition=='mixed'),coef_part_time_worker_mixed_party, +util_university_student_mixed_party,Dummy for university student in mixed party,(ptype==3) & (composition=='mixed'),coef_university_student_mixed_party, +util_non_worker_mixed_party,Dummy for nonworker in mixed party,(ptype==4) & (composition=='mixed'),coef_non_worker_mixed_party, +util_retiree_mixed_party,Dummy for retiree in mixed party,(ptype==5) & (composition=='mixed'),coef_retiree_mixed_party, +util_child_too_young_for_school_mixed_party,Dummy for driving school child in mixed party,(ptype==6) & (composition=='mixed'),coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_mixed_party,Dummy for pre-driving school child in mixed party,(ptype==7) & (composition=='mixed'),coef_pre_driving_age_student_mixed_party, +util_driving_age_student_mixed_party,Dummy for preschool child in mixed party,(ptype==8) & (composition=='mixed'),coef_driving_age_student_mixed_party, +#,,,, +,Dummy for part-time worker on maintenance joint tour ,(ptype==2) & (tour_type=='othmaint'),coef_part_time_worker_specific_to_maintenance_joint_tours, +,Dummy for non-worker on maintenance joint tour ,(ptype==4) & (tour_type=='othmaint'),coef_non_worker_specific_to_maintenance_joint_tours, +,Dummy for preschool child on maintenance joint tour ,(ptype==8) & (tour_type=='othmaint'),coef_pre_school_kid_specific_to_maintenance_joint_tours, +,Dummy for full-time worker on eating out joint tour ,(ptype==1) & (tour_type=='eatout'),coef_full_time_worker_specific_to_eating_out_joint_tours, +,Dummy for part-time worker on eating out joint tour ,(ptype==2) & (tour_type=='eatout'),coef_part_time_worker_specific_to_eating_out_joint_tours, +,Dummy for pre-driving age school child on eating out joint tour ,(ptype==7) & (tour_type=='eatout'),coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +,Dummy for part-time worker on discretionary joint tour ,(ptype==2) & (tour_type=='othdiscr'),coef_part_time_worker_specific_to_discretionary_joint_tours, +,Dummy for retiree on discretionary joint tour ,(ptype==5) & (tour_type=='othdiscr'),coef_retiree_specific_to_discretionary_joint_tours, +,Dummy for driving school child on discretionary joint tour ,(ptype==6) & (tour_type=='othdiscr'),coef_driving_age_student_specific_to_discretionary_joint_tours, +,Dummy for pre-driving school child on discretionary joint tour ,(ptype==7) & (tour_type=='othdiscr'),coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +,Dummy for part-time worker on visiting joint tour ,(ptype==2) & (tour_type=='social'),coef_part_time_worker_specific_to_visiting_joint_tours, +,Dummy for non-worker on visiting joint tour ,(ptype==4) & (tour_type=='social'),coef_non_worker_specific_to_visiting_joint_tours, +,Dummy for retiree on visiting joint tour ,(ptype==5) & (tour_type=='social'),coef_retiree_specific_to_visiting_joint_tours, +,Dummy for driving school child on visiting joint tour ,(ptype==6) & (tour_type=='social'),coef_driving_age_student_specific_to_visiting_joint_tours, +,Dummy for preschool child on visiting joint tour ,(ptype==8) & (tour_type=='social'),coef_pre_school_kid_specific_to_visiting_joint_tours, +#,,,, +,Dummy for low car ownership for adult in adult party,(autosnum_workers)&(composition=='mixed')&adult,coef_adult_more_automobiles_than_workers_mixed_party, +,Dummy for no car for child in mixed party,(autos==0)&(composition=='mixed')&child,coef_child_zero_automobiles_mixed_party, +#,,,, +,Dummy for low income for adult in adult party,(income<30000)&(composition=='adults')&adult,coef_dummy_for_low_income_for_adult_in_adult_party, +,Dummy for high income for adult in mixed party,(income>100000)&(composition=='mixed')&adult,coef_dummy_for_high_income_for_adult_in_mixed_party, +,Dummy for high income for child in mixed party,(income>100000)&(composition=='mixed')&child,coef_dummy_for_high_income_for_child_in_mixed_party, +#,,,, +,No of HH joint tours for adult in adult party,(adult&(composition=='adults'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +,No of HH joint tours for adult in mixed party,(adult&(composition=='mixed'))*num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +,No of HH joint tours for child in children party,(child&(composition=='children'))*num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +,No of HH joint tours for child in mixed party,(child&(composition=='mixed'))*num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +#,,,, +,No of other HH adults for adult in adult party,(adult & (composition=='adults')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +,No of other HH adults for adult in mixed party,(adult & (composition=='mixed')) * (num_adults.clip(upper=4) - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +,No of other HH children for child in children party,(~adult & (composition=='children')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +,No of other HH children for child in mixed party,(~adult & (composition=='mixed')) * (num_children.clip(upper=4) - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & (composition=='adults')) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & (composition=='mixed')) * log_time_window_overlap_adult_child,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult &(composition=='mixed')) * log_time_window_overlap_adult_child,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & (composition=='children')) * log_time_window_overlap_child,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & (composition=='children'),coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & (composition=='adults'),coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +#,,,, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & (composition=='adults') & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & (composition=='mixed') & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & (composition=='children') & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & (composition == 'mixed') & (num_travel_active_children<2),,coef_unavailable +#,,,, +,Global adjustment constant for whether person participated or not,1,coef_global_adj_for_paticipation, diff --git a/test/joint_tours/configs/joint_tour_participation.yaml b/test/joint_tours/configs/joint_tour_participation.yaml new file mode 100644 index 000000000..6e4fe5050 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_participation.yaml @@ -0,0 +1,21 @@ + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coefficients.csv + +LOGIT_TYPE: MNL + +max_participation_choice_iterations: 1000 + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants + TABLES: + - tours +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv new file mode 100644 index 000000000..95dac2f9e --- /dev/null +++ b/test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), participants.household_id)" +#,, +,person_is_preschool,participants.ptype == 8 \ No newline at end of file diff --git a/test/joint_tours/configs/joint_tour_participation_coefficients.csv b/test/joint_tours/configs/joint_tour_participation_coefficients.csv new file mode 100644 index 000000000..8126d3088 --- /dev/null +++ b/test/joint_tours/configs/joint_tour_participation_coefficients.csv @@ -0,0 +1,59 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_adults_only_party,-0.844885174,F +coef_part_time_worker_adults_only_party,-1.837563723,F +coef_university_students_adults_only_party,-0.970251833,F +coef_non_worker_adults_only_party,-0.757939204,F +coef_retiree_adults_only_party,1.196798335,F +coef_driving_age_student_children_only_party,-12.08924396,F +coef_pre_driving_age_student_children_only_party,-16.16999328,F +coef_child_too_young_for_school_children_only_party,-16.16999328,F +coef_full_time_worker_mixed_party,0.453048547,F +coef_part_time_worker_mixed_party,1.263225489,F +coef_university_student_mixed_party,1.561741231,F +coef_non_worker_mixed_party,2.90040237,F +coef_retiree_mixed_party,1.043276837,F +coef_child_too_young_for_school_mixed_party,-1.916267403,F +coef_pre_driving_age_student_mixed_party,-1.91645719,F +coef_driving_age_student_mixed_party,-0.933863842,F +#,, +coef_part_time_worker_specific_to_maintenance_joint_tours,0.766072366,F +coef_non_worker_specific_to_maintenance_joint_tours,0.971450303,F +coef_pre_school_kid_specific_to_maintenance_joint_tours,-0.52817951,F +coef_full_time_worker_specific_to_eating_out_joint_tours,0.535579735,F +coef_part_time_worker_specific_to_eating_out_joint_tours,1.23257663,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.535608884,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.539337302,F +coef_retiree_specific_to_discretionary_joint_tours,1.105118702,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-1.151066894,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.798684189,F +coef_part_time_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_non_worker_specific_to_visiting_joint_tours,1.075535443,F +coef_retiree_specific_to_visiting_joint_tours,-1.930328716,F +coef_driving_age_student_specific_to_visiting_joint_tours,-1.334515344,F +coef_pre_school_kid_specific_to_visiting_joint_tours,1.552698221,F +#,, +coef_adult_fewer_automobiles_than_workers_adult_only_party,1.292641814,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.390671553,F +coef_child_zero_automobiles_mixed_party,-1.546687521,F +#,, +coef_dummy_for_low_income_for_adult_in_adult_party,-0.680688378,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.20257569,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.74160668,F +#,, +coef_adult_number_of_joint_tours_adult_only,-0.599443484,F +coef_adult_number_of_joint_tours_mixed,-0.218541079,F +coef_child_number_of_joint_tours_child_only,-0.313832442,F +coef_child_number_of_joint_tours_mixed,-0.241748337,F +#,, +coef_adult_number_of_other_adults_in_the_household_adults_only_party,-0.747528682,F +coef_adult_number_of_other_adults_in_the_household_mixed_party,-0.285988025,F +coef_child_number_of_other_children_in_the_household_child_only_party,-2.305908575,F +coef_child_number_of_other_children_in_the_household_mixed,-0.471545203,F +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.634378325,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,0.057012356,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.616796615,F +coef_child_log_of_max_window_overlap_with_a_child_child,10.70256432,F +#,, +coef_global_adj_for_paticipation,-1,T diff --git a/test/joint_tours/configs/network_los.yaml b/test/joint_tours/configs/network_los.yaml new file mode 100644 index 000000000..391125a38 --- /dev/null +++ b/test/joint_tours/configs/network_los.yaml @@ -0,0 +1,14 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/test/joint_tours/configs/settings.yaml b/test/joint_tours/configs/settings.yaml new file mode 100644 index 000000000..64584bdf4 --- /dev/null +++ b/test/joint_tours/configs/settings.yaml @@ -0,0 +1,79 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + unique_hh_id: household_id + NP: hhsize + hh_workers_from_esr: num_workers + VEH: auto_ownership + MAZ: home_zone_id + HINCP: income + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + unique_hh_id: household_id + AGEP: age + SPORDER: PNUM + SEX: sex + employed: pemploy + student_status: pstudent + person_type: ptype + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ_ORIGINAL: zone_id + CountyID: county_id + TAZ_ORIGINAL: TAZ + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - tablename: tours + filename: mandatory_tours.csv + # since tours has a canonical index name 'tour_id', we must explicitly indicate that no index should be assigned + # canonical index_col 'tour_id' will be assigned by initialize_tours + index_col: + rename_columns: + hh_id: household_id + start_period: start + end_period: end + keep_columns: + - person_id + - household_id + - person_type + - tour_category + - tour_purpose + - orig_mgra + - dest_mgra + - start + - end + +trace_hh_id: \ No newline at end of file diff --git a/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv b/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 000000000..bddab06b9 --- /dev/null +++ b/test/joint_tours/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/test/joint_tours/test_joint_tours.py b/test/joint_tours/test_joint_tours.py new file mode 100644 index 000000000..2edccb042 --- /dev/null +++ b/test/joint_tours/test_joint_tours.py @@ -0,0 +1,265 @@ +import logging +import pytest +import os +import shutil +import pandas as pd +import numpy as np +from numpy import dot +from numpy.linalg import norm + +# import models is necessary to initalize the model steps with orca +from activitysim.abm import models +from activitysim.core import pipeline, config +from activitysim.core import tracing + +logger = logging.getLogger(__name__) + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return "joint_tours" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +# def tables() -> dict[str, str]: +def tables(prepare_module_inputs) -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "MAZ_ORIGINAL", + "persons": "person_id", + "households": "household_id", + "accessibility": "MAZ_ORIGINAL", + "tours": "tour_id", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return False + + +# Used by conftest.py reconnect_pipeline method +@pytest.fixture(scope="module") +def load_checkpoint() -> bool: + """ + checkpoint to be loaded from the pipeline when reconnecting. + """ + return "initialize_households" + + +@pytest.mark.skipif( + os.path.isfile("test/joint_tours/output/pipeline.h5"), + reason="no need to recreate pipeline store if already exist", +) +def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["initialize_landuse", "initialize_households"]) + person_df = pipeline.get_table("persons") + pipeline.close_pipeline() + + +def test_joint_tours_frequency_composition( + reconnect_pipeline: pipeline.Pipeline, caplog +): + + caplog.set_level(logging.INFO) + + # run model step + pipeline.run( + models=["joint_tour_frequency_composition"], + resume_after="initialize_households", + ) + + pipeline.close_pipeline() + + +def test_joint_tours_participation(reconnect_pipeline: pipeline.Pipeline, caplog): + + caplog.set_level(logging.INFO) + + # run model step + pipeline.run( + models=["joint_tour_participation"], + resume_after="joint_tour_frequency_composition", + ) + + pipeline.close_pipeline() + + +# fetch/prepare existing files for model inputs +# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim +@pytest.fixture(scope="module") +def prepare_module_inputs() -> None: + """ + copy input files from sharepoint into test folder + + create unique person id in person file + + :return: None + """ + # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/ + # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1 + test_dir = os.path.join("test", "joint_tours", "data") + + accessibility_file = os.path.join(test_dir, "tm2_outputs", "accessibilities.csv") + household_file = os.path.join(test_dir, "popsyn", "households.csv") + person_file = os.path.join(test_dir, "popsyn", "persons.csv") + landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv") + + shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv")) + shutil.copy(household_file, os.path.join(test_dir, "households.csv")) + shutil.copy(person_file, os.path.join(test_dir, "persons.csv")) + shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv")) + + # add original maz id to accessibility table + land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv")) + + accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv")) + + accessibility_df = pd.merge( + accessibility_df, + land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}), + how="left", + on="mgra", + ) + + accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False) + + # currently household file has to have these two columns, even before annotation + # because annotate person happens before household and uses these two columns + # TODO find a way to get around this + #### + + # household file from populationsim + household_df = pd.read_csv(os.path.join(test_dir, "households.csv")) + + household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"} + + household_df.rename(columns=household_columns_dict, inplace=True) + + # get columns from ctramp output + tm2_simulated_household_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "householdData_1.csv") + ) + tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + household_df = pd.merge( + household_df, + tm2_simulated_household_df[ + [ + "household_id", + "autos", + "automated_vehicles", + "transponder", + "cdap_pattern", + "jtf_choice", + ] + ], + how="inner", # tm2 is not 100% sample run + on="household_id", + ) + + household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False) + + # person file from populationsim + person_df = pd.read_csv(os.path.join(test_dir, "persons.csv")) + + person_columns_dict = {"HHID": "household_id", "PERID": "person_id"} + + person_df.rename(columns=person_columns_dict, inplace=True) + + # get columns from ctramp result + tm2_simulated_person_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "personData_1.csv") + ) + tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + person_df = pd.merge( + person_df, + tm2_simulated_person_df[ + [ + "household_id", + "person_id", + "type", + "value_of_time", + "activity_pattern", + "imf_choice", + "inmf_choice", + "fp_choice", + "reimb_pct", + "workDCLogsum", + "schoolDCLogsum", + ] + ], + how="inner", # ctramp might not be 100% sample run + on=["household_id", "person_id"], + ) + + person_df["PNUM"] = person_df.groupby("household_id")["person_id"].rank() + + person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False) + + ## get tour data from tm2 output + + tm2_simulated_indiv_tour_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv") + ) + tm2_simulated_indiv_tour_df = tm2_simulated_indiv_tour_df[ + tm2_simulated_indiv_tour_df.tour_category == "MANDATORY" + ] + + tm2_simulated_tour_df = pd.concat( + [tm2_simulated_indiv_tour_df], sort=False, ignore_index=True + ) + + tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}).to_csv( + os.path.join(test_dir, "tours.csv"), index=False + ) + + +def create_summary(input_df, key, out_col="Share") -> pd.DataFrame: + """ + Create summary for the input data. + 1. group input data by the "key" column + 2. calculate the percent of input data records in each "key" category. + + :return: pd.DataFrame + """ + + out_df = input_df.groupby(key).size().reset_index(name="Count") + out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4) + + return out_df[[key, out_col]] + + +def cosine_similarity(a, b): + """ + Computes cosine similarity between two vectors. + + Cosine similarity is used here as a metric to measure similarity between two sequence of numbers. + Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them + i.e., dot products of the vectors divided by the product of their lengths. + + :return: + """ + + return dot(a, b) / (norm(a) * norm(b)) diff --git a/test/non_mandatory_tour_frequency/.gitkeep b/test/non_mandatory_tour_frequency/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls b/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls new file mode 100644 index 000000000..4726c3f73 Binary files /dev/null and b/test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls differ diff --git a/test/non_mandatory_tour_frequency/configs/annotate_households.csv b/test/non_mandatory_tour_frequency/configs/annotate_households.csv new file mode 100644 index 000000000..ac21d3797 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/annotate_households.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of fulltime workers,num_full_time_workers,"_PERSON_COUNT('ptype == 1', persons, households)" +number of parttime workers,num_part_time_workers,"_PERSON_COUNT('ptype == 2', persons, households)" +number of university student,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)" +number of non-workers,num_non_workers,"_PERSON_COUNT('ptype == 4', persons, households)" +number of retirees,num_retirees,"_PERSON_COUNT('ptype == 5', persons, households)" +number of driving age students,num_driving_age_students,"_PERSON_COUNT('ptype == 6', persons, households)" +number of pre-driving age school kids,num_pre_driving_age_school_kids,"_PERSON_COUNT('ptype == 7', persons, households)" +number of pre-school kids,num_pre_school_kids,"_PERSON_COUNT('ptype == 8', persons, households)" +number of pre-driving age school kids who go out,num_pre_driving_age_school_kids_go_out,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)" +number of pre-school kids who go out,num_pre_school_kids_go_out,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)" \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv b/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv new file mode 100644 index 000000000..65237c0ad --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/annotate_landuse.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / land_use.TOTACRE +employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE +population_density,population_density,land_use.TOTPOP / land_use.TOTACRE +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons.csv new file mode 100644 index 000000000..a68eb7e67 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/annotate_persons.csv @@ -0,0 +1,17 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +,ptype,0 +,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)" +,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)" +,ptype,"np.where(persons.type == 'University student', 3, ptype)" +,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)" +,ptype,"np.where(persons.type == 'Retired', 5, ptype)" +,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)" +,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)" +,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)" +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv new file mode 100644 index 000000000..0dfa16be6 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv b/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv new file mode 100644 index 000000000..07890f237 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv @@ -0,0 +1,10 @@ +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours diff --git a/test/non_mandatory_tour_frequency/configs/constants.yaml b/test/non_mandatory_tour_frequency/configs/constants.yaml new file mode 100644 index 000000000..b0bd5a1f3 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/constants.yaml @@ -0,0 +1,68 @@ +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Correction for transit skim expressions +# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and +# therefore need to be divided by the scale factor if used in expressions +TRANSIT_SCALE_FACTOR: 100 diff --git a/test/non_mandatory_tour_frequency/configs/initialize_households.yaml b/test/non_mandatory_tour_frequency/configs/initialize_households.yaml new file mode 100644 index 000000000..07e34e8a4 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/initialize_households.yaml @@ -0,0 +1,48 @@ +annotate_tables: + - tablename: persons + column_map: + HHID: household_id + PERID: person_id + AGEP: age + SEX: sex + SCHL: education_attainment + OCCP: occupation + WKHP: hours_worked + WKW: weeks_worked + EMPLOYED: employment_status + ESR: esr + SCHG: grade_attending + activity_pattern: cdap_activity + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + column_map: + HHID: household_id + MAZ: home_zone_id + HHINCADJ: income + NWRKRS_ESR: num_workers + VEH: auto_ownership + NP: hhsize + HHT: hh_type + BLD: building_size + TYPE: hh_unit_type + MTCCountyID: county_id + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons + - land_use + +CONSTANTS: + PTYPE_FULL: 1 + PTYPE_PART: 2 + PTYPE_UNIVERSITY: 3 + PTYPE_NONWORK: 4 + PTYPE_RETIRED: 5 + PTYPE_DRIVING: 6 + PTYPE_SCHOOL: 7 + PTYPE_PRESCHOOL: 8 \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml b/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml new file mode 100644 index 000000000..7cc1d7e34 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml @@ -0,0 +1,54 @@ +annotate_tables: + - tablename: land_use + column_map: + MAZ_ORIGINAL: zone_id + CountyID: county_id + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + annotate: + SPEC: annotate_landuse + DF: land_use + - tablename: accessibility + column_map: + column_1: nonmandatory_auto_accessibility + column_2: nonmandatory_transit_accessibility + column_3: nonmandatory_nm_accessibility + column_4: nonmandatory_sov0_accessibility + column_5: nonmandatory_sov1_accessibility + column_6: nonmandatory_sov2_accessibility + column_7: nonmandatory_hov0_accessibility + column_8: nonmandatory_hov1_accessibility + column_9: nonmandatory_hov2_accessibility + column_10: shop_hov_insufficient_accessibility + column_11: shop_hov_sufficient_accessibility + column_12: shop_hov_oversufficient_accessibility + column_13: maint_hov_insufficient_accessibility + column_14: maint_hov_sufficient_accessibility + column_15: maint_hov_oversufficient_accessibility + column_16: eat_hov_insufficient_accessibility + column_17: eat_hov_sufficient_accessibility + column_18: eat_hov_oversufficient_accessibility + column_19: visit_hov_insufficient_accessibility + column_20: visit_hov_sufficient_accessibility + column_21: visit_hov_oversufficient_accessibility + column_22: discr_hov_insufficient_accessibility + column_23: discr_hov_sufficient_accessibility + column_24: discr_hov_oversufficient_accessibility + column_25: escort_hov_insufficient_accessibility + column_26: escort_hov_sufficient_accessibility + column_27: escort_hov_oversufficient_accessibility + column_28: shop_sov_insufficient_accessibility + column_29: shop_sov_sufficient_accessibility + column_30: shop_sov_oversufficient_accessibility + column_31: maint_sov_insufficient_accessibility + column_32: maint_sov_sufficient_accessibility + column_33: maint_sov_oversufficient_accessibility + column_40: discr_sov_insufficient_accessibility + column_41: discr_sov_sufficient_accessibility + column_42: discr_sov_oversufficient_accessibility + column_45: total_emp_accessibility + column_47: hh_walktransit_accessibility + mgra : zone_id \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/network_los.yaml b/test/non_mandatory_tour_frequency/configs/network_los.yaml new file mode 100644 index 000000000..391125a38 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/network_los.yaml @@ -0,0 +1,14 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv new file mode 100644 index 000000000..7a3fc8703 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv @@ -0,0 +1,187 @@ +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mandatory_tours == 0) & (num_person_joint_tours == 0),coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable,coef_unavailable +util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mandatory_tours > 0) | (num_person_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours +util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,,,,, +util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,,,,,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 +util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,,coef_total_number_of_tours_is_3,,,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 +util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,,coef_total_number_of_tours_is_4,,,, +util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_unavailable,coef_total_number_of_tours_is_5,,coef_unavailable,coef_unavailable,,, +util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_unavailable,coef_total_number_of_tours_is_6_plus,,coef_unavailable,coef_unavailable,,, +util_total_number_of_tours_is_1+,Total Number of Tours >= 1,tot_tours >= 1,,,,,,coef_total_number_of_tours_is_1_plus,, +util_total_number_of_tours_is_4+,Total Number of Tours >= 4,tot_tours >= 4,,,,,,coef_unavailable,coef_unavailable,coef_unavailable +util_has_mandatory_tours_and_tour_frequency_is_1,One or more Mandatory tour & tour frequency =1,(num_mandatory_tours>0)&(tot_tours == 1),,,,,,,coef_has_mandatory_tours_and_tour_frequency_is_1, +util_has_mandatory_tours_and_tour_frequency_is_2,One or more Mandatory tour & tour frequency =2,(num_mandatory_tours>0)&(tot_tours == 2),coef_has_mandatory_tours_and_tour_frequency_is_2,coef_has_mandatory_tours_and_tour_frequency_is_2,,,,,, +util_has_mandatory_tours_and_tour_frequency_is_2+,One or more Mandatory tour & tour frequency =2+,(num_mandatory_tours>0)&(tot_tours >= 2),,,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,,,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,coef_has_mandatory_tours_and_tour_frequency_is_2_plus,coef_has_mandatory_tours_and_tour_frequency_is_2_plus +util_has_mandatory_tours_and_tour_frequency_is_3,One or more Mandatory tour & tour frequency =3,(num_mandatory_tours>0)&(tot_tours == 3),coef_has_mandatory_tours_and_tour_frequency_is_3,coef_has_mandatory_tours_and_tour_frequency_is_3,,,,,, +util_has_mandatory_tours_and_tour_frequency_is_4+,One or more Mandatory tour & tour frequency =4+,(num_mandatory_tours>0)&(tot_tours >= 4),coef_has_mandatory_tours_and_tour_frequency_is_4_plus,coef_has_mandatory_tours_and_tour_frequency_is_4_plus,,,,,, +util_has_joint_tours_and_tour_frequency_is_1,One or more Joint tour & tour frequency =1,(num_person_joint_tours>0)&(tot_tours == 1),,,coef_has_joint_tours_and_tour_frequency_is_1,coef_has_joint_tours_and_tour_frequency_is_1,coef_has_joint_tours_and_tour_frequency_is_1,,, +util_has_joint_tours_and_tour_frequency_is_2,One or more Joint tour & tour frequency =2,(num_person_joint_tours>0)&(tot_tours == 2),,,,coef_has_joint_tours_and_tour_frequency_is_2,,,, +util_has_joint_tours_and_tour_frequency_is_3+,One or more Joint tour & tour frequency =3+,(num_person_joint_tours>0)&(tot_tours >= 3),coef_has_joint_tours_and_tour_frequency_is_3_plus,,,coef_has_joint_tours_and_tour_frequency_is_3_plus,,,, +util_has_joint_tours_and_tour_frequency_is_4+,One or more Joint tour & tour frequency =4+,(num_person_joint_tours>0)&(tot_tours >= 4),,coef_has_joint_tours_and_tour_frequency_is_4_plus,,,,,, +util_has_joint_tours_and_tour_frequency_is_2+,One or more Joint tour & tour frequency =2+,(num_person_joint_tours>0)&(tot_tours >= 2),,,coef_has_joint_tours_and_tour_frequency_is_2_plus,,coef_has_joint_tours_and_tour_frequency_is_2_plus,,, +util_has_joint_tours_and_tour_frequency_is_1+,One or more Joint tour & tour frequency =1+,(num_person_joint_tours>0)&(tot_tours >= 1),,,,,,,coef_has_joint_tours_and_tour_frequency_is_1_plus, +util_number_of_joint_shopping_tours,Number of Joint Shopping tours,(shopping>0) * num_person_joint_shop_tours,coef_number_of_joint_shopping_tours,,,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,,, +util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,(othmaint>0) * num_person_joint_maint_tours,,,,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,,, +util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,(eatout>0) * num_person_joint_eatout_tours,,,,,coef_number_of_joint_eating_out_tours,,, +util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,(othdiscr>0) * num_person_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,,,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours, +util_more_than_one_escorting_tours,More than one escorting tours,escort > 1,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours,,coef_more_than_one_escorting_tours,coef_more_than_one_escorting_tours +util_more_than_one_shopping_tours,More than one shopping tours,shopping > 1,,coef_more_than_one_shopping_tours,,coef_more_than_one_shopping_tours,coef_more_than_one_shopping_tours,,, +util_more_than_one_maintenance_tours,More than one maintenance tours,othmaint>1,,,,coef_more_than_one_maintenance_tours,coef_more_than_one_maintenance_tours,,, +util_more_than_one_discretionary_tours,More than one discretionary tours,othdiscr > 1,coef_more_than_one_discretionary_tours,coef_more_than_one_discretionary_tours,,coef_more_than_one_discretionary_tours,,coef_more_than_one_discretionary_tours,coef_more_than_one_discretionary_tours, +util_low_income_group_and_escorting_tour,Dummy for low Income group (> 150K) & escorting tour,low_income * escort,,,coef_low_income_group_and_escorting_tour,,,coef_low_income_group_and_escorting_tour,, +util_mediumlow_income_group_and_escorting_tour,Dummy for medium low Income group (> 150K) & escorting tour,medium_low_income * escort,,,coef_medium_low_income_group_and_escorting_tour,,,coef_medium_low_income_group_and_escorting_tour,, +util_mediumhigh_income_group_and_escorting_tour,Dummy for medium high Income group (> 150K) & escorting tour,medium_high_income * escort,,,coef_medium_high_income_group_and_escorting_tour,,,,, +util_high_income_group_and_escorting_tour,Dummy for high Income group (> 150K) & escorting tour,high_income * escort,coef_high_income_group_and_escorting_tour,coef_high_income_group_and_escorting_tour,coef_high_income_group_and_escorting_tour,,,,,coef_high_income_group_and_escorting_tour +util_low_income_group_and_shopping_tour,Dummy for low Income group (> 150K) & shopping tour,low_income * shopping,,coef_low_income_group_and_shopping_tour,,coef_low_income_group_and_shopping_tour,,,, +util_mediumlow_income_group_and_shopping_tour,Dummy for medium low Income group (> 150K) & shopping tour,medium_low_income * shopping,,coef_medium_low_income_group_and_shopping_tour,,coef_medium_low_income_group_and_shopping_tour,,,, +util_mediumhigh_income_group_and_shopping_tour,Dummy for medium high Income group (> 150K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_medium_high_income_group_and_shopping_tour,coef_medium_high_income_group_and_shopping_tour,,coef_mediumhigh_income_group_and_shopping_tour,,, +util_high_income_group_and_shopping_tour,Dummy for high Income group (> 150K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,,coef_high_income_group_and_shopping_tour,,coef_high_income_group_and_shopping_tour, +util_low_income_group_and_maintenance_tour,Dummy for low Income group (<30k) & maintenance tour,low_income * othmaint,coef_low_income_group_and_maintenance_tour,,,,coef_low_income_group_and_maintenance_tour,,, +util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (30K-60K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,,,,coef_mediumlow_income_group_and_maintenance_tour,,, +util_mediumhigh_income_group_and_maintenance_tour,Dummy for Medium high Income group (30K-60K) & maintenance tour,medium_high_income * othmaint,,,coef_mediumhigh_income_group_and_maintenance_tour,,,,coef_mediumhigh_income_group_and_maintenance_tour, +util_high_income_group_and_maintenance_tour,Dummy for High Income group (>150K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,,coef_high_income_group_and_maintenance_tour,,,,coef_high_income_group_and_maintenance_tour, +util_low_income_group_and_eating_out_tour,Dummy for Low Income group (<30K) & Eating out tour,low_income * eatout,coef_low_income_group_and_eating_out_tour,coef_low_income_group_and_eating_out_tour,,coef_low_income_group_and_eating_out_tour,coef_low_income_group_and_eating_out_tour,,, +util_medium_low_income_group_and_eating_out_tour,Dummy for medium low Income group (>150K) & Eating out tour,medium_low_income * eatout,,coef_medium_low_income_group_and_eating_out_tour,,,coef_medium_low_income_group_and_eating_out_tour,,, +util_medium_high_income_group_and_eating_out_tour,Dummy for medium high Income group (>150K) & Eating out tour,medium_high_income * eatout,,,coef_medium_high_income_group_and_eating_out_tour,,,,, +util_high_income_group_and_eating_out_tour,Dummy for High Income group (>150K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,,,, +util_low_income_group_and_discretionary_tour,Dummy for Low Income group (<30K) & Discretionary tour,low_income * othdiscr,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour,coef_low_income_group_and_discretionary_tour, +util_medium_low_income_group_and_discretionary_tour,Dummy for Medium Low Income group (<30K) & Discretionary tour,medium_low_income * othdiscr,,,,coef_medium_low_income_group_and_discretionary_tour,,,coef_medium_low_income_group_and_discretionary_tour, +util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (100k-150K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,, +util_high_income_group_and_discretionary_tour,Dummy for High Income group (>150K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour, +util_low_income_group_and_visiting_tour,Dummy for Low Income group (<30k) & Visiting tour,low_income * social,coef_low_income_group_and_visiting_tour,,coef_low_income_group_and_visiting_tour,,,,coef_low_income_group_and_visiting_tour, +util_mediumlow_income_group_and_visiting_tour,Dummy for medium Low Income group (<30k) & Visiting tour,medium_low_income * social,,,coef_medium_low_income_group_and_visiting_tour,,,,coef_medium_low_income_group_and_visiting_tour, +util_high_income_group_and_visiting_tour,Dummy for high Income group (<30k) & Visiting tour,high_income * social,,,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour, +util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,,,,coef_mediumhigh_income_group_and_visiting_tour,,coef_mediumhigh_income_group_and_visiting_tour,, +util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,,,, +util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,, +util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,,,coef_female_and_maintenance_tour,,coef_female_and_maintenance_tour,,, +util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,,coef_female_and_eatingout_tour,,,coef_female_and_eatingout_tour,,, +util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,,,,,coef_female_and_discretionary_tour,,, +util_female_and_discretionary_tour,Dummy for Female & Visiting Tour,female * social,,,coef_female_and_visit_tour,coef_female_and_visit_tour,,,, +util_zero_car_ownership_and_tour_frequency_is_2+,Dummy for zero car ownership & tour frequency >=2,no_cars & (tot_tours >= 2),coef_zero_car_ownership_and_tour_frequency_is_2_plus,coef_zero_car_ownership_and_tour_frequency_is_2_plus,,coef_zero_car_ownership_and_tour_frequency_is_2_plus,,,, +util_car_shortage_vs_workers_and_tour_frequency_is_2+,Dummy for Car Shortage vs Workers & tour frequency >=2,~no_cars & (car_sufficiency < 0) & (tot_tours >= 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,,,,,, +util_car_surplus_vs_workers_and_tour_frequency_is_1+,Dummy for Car Surplus vs Workers & tour frequency >=1,~no_cars & (car_sufficiency > 0) & (tot_tours >= 1),,,coef_car_surplus_vs_workers_and_tour_frequency_is_1_plus,,,,, +util_car_surplus_vs_workers_and_tour_frequency_is_3+,Dummy for Car Surplus vs Workers & tour frequency >=3,~no_cars & (car_sufficiency > 0) & (tot_tours >= 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3_plus,,,,,,, +util_zero_car_ownership_and_escorting_tour,Dummy for zero car ownership & number of escorting tour,no_cars * escort,coef_zero_car_ownership_and_escorting_tour,,,coef_zero_car_ownership_and_escorting_tour,,,,coef_zero_car_ownership_and_escorting_tour +util_car_shortage_vs_workers_and_escorting_tour,Dummy for Car Shortage vs Workers & number of escorting tours,(~no_cars & (car_sufficiency < 0)) * escort,coef_car_shortage_vs_workers_and_number_of_escorting_tours,coef_car_shortage_vs_workers_and_number_of_escorting_tours,,,,,,coef_car_shortage_vs_workers_and_number_of_escorting_tours +util_car_surplus_vs_workers_and_escorting_tour,Dummy for Car Surplus vs Workers & number of escorting tours,(~no_cars & (car_sufficiency > 0)) * escort,,coef_car_surplus_vs_workers_and_number_of_escorting_tours,,coef_car_surplus_vs_workers_and_number_of_escorting_tours,,,, +util_car_shortage_vs_workers_and_shopping_tour,Dummy for Car Shortage vs Workers & number of shopping tours,(~no_cars & (car_sufficiency < 0)) * shopping,,coef_car_shortage_vs_workers_and_number_of_shopping_tours,,,,,, +util_car_surplus_vs_workers_and_shopping_tour,Dummy for Car Surplus vs Workers & number of shopping tours,(~no_cars & (car_sufficiency > 0)) * shopping,,coef_car_surplus_vs_workers_and_number_of_shopping_tours,,,,,, +util_car_surplus_vs_workers_and_maintenance_tour,Dummy for Car Surplus vs Workers & number of maintenance tours,(~no_cars & (car_sufficiency > 0)) * othmaint,,,,,,,coef_car_surplus_vs_workers_and_number_of_maintenance_tours, +util_car_surplus_vs_workers_and_discretionary_tour,Dummy for Car Surplus vs Workers & number of discretionary tours,(~no_cars & (car_sufficiency > 0)) * othdiscr,,,,,,,coef_car_surplus_vs_workers_and_number_of_discretionary_tours, +util_number_of_full_time_worker_and_escorting_tour,Dummy for number of Full time Worker (other than modeled person) & Escorting tour ,num_full_time_workers_not_self * escort,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,coef_number_of_full_time_worker_and_escorting_tour,,, +util_number_of_part_time_worker_and_escorting_tour,Dummy for number of Part time Worker (other than modeled person) & Escorting tour ,num_part_time_workers_not_self * escort,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,coef_number_of_part_time_worker_and_escorting_tour,,,coef_number_of_part_time_worker_and_escorting_tour +util_number_of_non_worker_and_escorting_tour,Dummy for number of Non-Worker (other than modeled person) & Escorting tour ,num_non_workers_not_self * escort,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,coef_number_of_non_worker_and_escorting_tour,,,coef_number_of_non_worker_and_escorting_tour +util_number_of_retiree_and_escorting_tour,Dummy for number of Retiree (other than modeled person) & Escorting tour ,num_retirees_not_self * escort,coef_number_of_retiree_and_escorting_tour,,coef_number_of_retiree_and_escorting_tour,coef_number_of_retiree_and_escorting_tour,,,, +util_number_of_university_student_and_escorting_tour,Dummy for number of University Student (other than modeled person) & Escorting tour ,num_university_students_not_self * escort,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,coef_number_of_university_student_and_escorting_tour,,,,coef_number_of_university_student_and_escorting_tour +util_number_of_driving_school_kid_and_escorting_tour,Dummy for number of Driving School Kid (other than modeled person) & Escorting tour ,num_driving_age_students_not_self * escort,coef_number_of_driving_school_kid_and_escorting_tour,coef_number_of_driving_school_kid_and_escorting_tour,,coef_number_of_driving_school_kid_and_escorting_tour,coef_number_of_driving_school_kid_and_escorting_tour,,, +util_go_out_pre_driving_school_kid_and_escorting_tour,Dummy for number of Pre-Driving School Kid go out & Escorting tour ,num_pre_driving_age_school_kids_go_out * escort,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour,coef_go_out_pre_driving_school_kid_and_escorting_tour +util_go_out_pre_school_kid_and_escorting_tour,Dummy for Pre-School Kid going out & Escorting tour ,num_pre_school_kids_go_out * escort,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,coef_go_out_school_kid_and_escorting_tour,,,,coef_go_out_school_kid_and_escorting_tour +util_number_of_full_time_worker_and_shopping_tour,Dummy for number of Full time Worker (other than modeled person) & Shopping tour ,num_full_time_workers_not_self * shopping,,coef_number_of_full_time_worker_and_shopping_tour,coef_number_of_full_time_worker_and_shopping_tour,,,coef_number_of_full_time_worker_and_shopping_tour,, +util_number_of_part_time_worker_and_shopping_tour,Dummy for number of Part time Worker (other than modeled person) & Shopping tour ,num_part_time_workers_not_self * shopping,,coef_number_of_part_time_worker_and_shopping_tour,coef_number_of_part_time_worker_and_shopping_tour,,,coef_number_of_part_time_worker_and_shopping_tour,, +util_number_of_non_worker_and_shopping_tour,Dummy for number of Non-Worker (other than modeled person) & Shopping tour ,num_non_workers_not_self * shopping,,coef_number_of_non_worker_and_shopping_tour,,coef_number_of_non_worker_and_shopping_tour,,coef_number_of_non_worker_and_shopping_tour,, +util_number_of_retiree_and_shopping_tour,Dummy for number of Retiree (other than modeled person) & Shopping tour ,num_retirees_not_self * shopping,,,,,coef_number_of_retiree_and_shopping_tour,,, +util_number_of_university_student_and_shopping_tour,Dummy for number of University Student (other than modeled person) & Shopping tour ,num_university_students_not_self * shopping,,,coef_number_of_university_student_and_shopping_tour,,,,, +util_number_of_driving_school_kid_and_shopping_tour,Dummy for number of Driving School Kid (other than modeled person) & Shopping tour ,num_driving_age_students_not_self * shopping,coef_number_of_driving_school_kid_and_shopping_tour,,,,,,coef_number_of_driving_school_kid_and_shopping_tour, +util_number_of_pre_driving_school_kid_and_shopping_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Shopping tour ,num_pre_driving_age_school_kids_not_self * shopping,coef_number_of_pre_driving_school_kid_and_shopping_tour,,,,,coef_number_of_pre_driving_school_kid_and_shopping_tour,coef_number_of_pre_driving_school_kid_and_shopping_tour, +util_number_of_pre_school_kid_and_shopping_tour,Dummy for number of Pre-School Kid (other than modeled person) & Shopping tour ,num_pre_school_kids_not_self * shopping,coef_number_of_pre_school_kid_and_shopping_tour,,,,,coef_number_of_pre_school_kid_and_shopping_tour,coef_number_of_pre_school_kid_and_shopping_tour, +util_number_of_full_time_worker_and_maintenance_tour,Dummy for number of Full time Worker (other than modeled person) & Maintenance tour ,num_full_time_workers_not_self * othmaint,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,coef_number_of_full_time_worker_and_maintenance_tour,,,, +util_number_of_part_time_worker_and_maintenance_tour,Dummy for number of Part time Worker (other than modeled person) & Maintenance tour ,num_part_time_workers_not_self * othmaint,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,coef_number_of_part_time_worker_and_maintenance_tour,,,, +util_number_of_non_worker_and_maintenance_tour,Dummy for number of Non-Worker(other than modeled person) & Maintenance tour ,num_non_workers_not_self * othmaint,coef_number_of_non_worker_and_maintenance_tour,coef_number_of_non_worker_and_maintenance_tour,coef_number_of_non_worker_and_maintenance_tour,,,,, +util_number_of_retiree_and_maintenance_tour,Dummy for number of Retiree (other than modeled person) & Maintenance tour ,num_retirees_not_self * othmaint,,,,,coef_number_of_retiree_and_maintenance_tour,,, +util_number_of_university_student_and_maintenance_tour,Dummy for number of University Student (other than modeled person) & Maintenance tour ,num_university_students_not_self * othmaint,coef_number_of_university_student_and_maintenance_tour,,coef_number_of_university_student_and_maintenance_tour,,,,, +util_number_of_full_time_worker_and_eating_out_tour,Dummy for number of Full time Worker (other than modeled person) & Eating Out tour ,num_full_time_workers_not_self * eatout,coef_number_of_full_time_worker_and_eating_out_tour,coef_number_of_full_time_worker_and_eating_out_tour,,,,,, +util_number_of_part_time_worker_and_eating_out_tour,Dummy for number of Part time Worker (other than modeled person) & Eating Out tour ,num_part_time_workers_not_self * eatout,coef_number_of_part_time_worker_and_eating_out_tour,,,,,,, +util_number_of_non_worker_and_eating_out_tour,Dummy for number of Non-Worker (other than modeled person) & Eating Out tour ,num_non_workers_not_self * eatout,coef_number_of_non_worker_and_eating_out_tour,,,,,,, +util_number_of_retiree_and_eating_out_tour,Dummy for number of Retiree (other than modeled person) & Eating Out tour ,num_retirees_not_self * eatout,coef_number_of_retiree_and_eating_out_tour,,,,,,, +util_number_of_university_student_and_eating_out_tour,Dummy for number of University Student (other than modeled person) & Eating Out tour ,num_university_students_not_self * eatout,coef_number_of_university_student_and_eating_out_tour,,,,,,, +util_number_of_pre_driving_school_kid_and_eating_out_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,num_pre_driving_age_school_kids_not_self * eatout,coef_number_of_pre_driving_school_kid_and_eating_out_tour,,,coef_number_of_pre_driving_school_kid_and_eating_out_tour,,,, +util_number_of_pre_school_kid_and_eating_out_tour,Dummy for number of Pre-School Kid (other than modeled person) & Eating Out tour ,num_pre_school_kids_not_self * eatout,coef_number_of_pre_school_kid_and_eating_out_tour,,,coef_number_of_pre_school_kid_and_eating_out_tour,,,, +util_number_of_part_time_worker_and_visiting_tour,Dummy for number of Part time Worker (other than modeled person) & Visiting tour ,num_part_time_workers_not_self * social,,coef_number_of_part_time_worker_and_visiting_tour,,,,,, +util_number_of_non_worker_and_visiting_tour,Dummy for number of Non-Worker (other than modeled person) & Visiting tour ,num_non_workers_not_self * social,coef_number_of_non_worker_and_visiting_tour,,,,,,, +util_number_of_retiree_and_visiting_tour,Dummy for number of Retiree (other than modeled person) & Visiting tour ,num_retirees_not_self * social,coef_number_of_retiree_and_visiting_tour,coef_number_of_retiree_and_visiting_tour,,,coef_number_of_retiree_and_visiting_tour,,, +util_number_of_university_student_and_visiting_tour,Dummy for number of University Student (other than modeled person) & Visiting tour ,num_university_students_not_self * social,coef_number_of_university_student_and_visiting_tour,,,,,,, +util_number_of_driving_school_kid_and_visiting_tour,Dummy for number of Driving School Kid (other than modeled person) & visiting tour ,num_driving_age_students_not_self * social,,coef_number_of_driving_school_kid_and_visiting_tour,,,,,coef_number_of_driving_school_kid_and_visiting_tour, +util_number_of_pre_driving_school_kid_and_visiting_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & visiting tour ,num_pre_driving_age_school_kids_not_self * social,,coef_number_of_pre_driving_school_kid_and_visiting_tour,,coef_number_of_pre_driving_school_kid_and_visiting_tour,,,coef_number_of_pre_driving_school_kid_and_visiting_tour, +util_number_of_pre_school_kid_and_visiting_tour,Dummy for number of Pre-School Kid (other than modeled person) & visiting tour ,num_pre_school_kids_not_self * social,,coef_number_of_pre_school_kid_and_visiting_tour,,coef_number_of_pre_school_kid_and_visiting_tour,,,coef_number_of_pre_school_kid_and_visiting_tour, +util_number_of_part_time_worker_and_discretionary_tour,Dummy for number of Part time Worker (other than modeled person) & Discretionary tour ,num_part_time_workers_not_self * othdiscr,,,,,,coef_number_of_part_time_worker_and_discretionary_tour,, +util_number_of_non_worker_and_discretionary_tour,Dummy for number of Non-Worker (other than modeled person) & Discretionary tour ,num_non_workers_not_self * othdiscr,,,,,,coef_number_of_non_worker_and_discretionary_tour,, +util_number_of_university_student_and_discretionary_tour,Dummy for number of University Student (other than modeled person) & Discretionary tour ,num_university_students_not_self * othdiscr,,,coef_number_of_university_student_and_discretionary_tour,,,coef_number_of_university_student_and_discretionary_tour,, +util_number_of_driving_school_kid_and_discretionary_tour,Dummy for number of Driving School Kid (other than modeled person) & Discretionary tour ,num_driving_age_students_not_self * othdiscr,coef_number_of_driving_school_kid_and_discretionary_tour,coef_number_of_driving_school_kid_and_discretionary_tour,,coef_number_of_driving_school_kid_and_discretionary_tour,,,coef_number_of_driving_school_kid_and_discretionary_tour, +util_number_of_pre_driving_school_kid_and_discretionary_tour,Dummy for number of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,num_pre_driving_age_school_kids_not_self * othdiscr,coef_number_of_pre_driving_school_kid_and_discretionary_tour,coef_number_of_pre_driving_school_kid_and_discretionary_tour,,coef_number_of_pre_driving_school_kid_and_discretionary_tour,,,coef_number_of_pre_driving_school_kid_and_discretionary_tour, +util_number_of_pre_school_kid_and_discretionary_tour,Dummy for number of Pre-School Kid (other than modeled person) & Discretionary tour ,num_pre_school_kids_not_self * othdiscr,coef_number_of_pre_school_kid_and_discretionary_tour,coef_number_of_pre_school_kid_and_discretionary_tour,,coef_number_of_pre_school_kid_and_discretionary_tour,,,coef_number_of_pre_school_kid_and_discretionary_tour, +util_retired_hh_and_escorting,"Household Type (All Retirees and Non-workers only), Escorting",retiredHh * escort,,,,,coef_retired_hh_and_escorting_tour,,, +util_retired_hh_and_shopping,"Household Type (All Retirees and Non-workers only), Shopping",retiredHh * shopping,,,,,coef_retired_hh_and_shopping_tour,,, +util_retired_hh_and_eating_out,"Household Type (All Retirees and Non-workers only), Eating out",retiredHh * eatout,,,,,coef_retired_hh_and_eating_out_tour,,, +util_retired_hh_and_discretionary,"Household Type (All Retirees and Non-workers only), Discretionary",retiredHh * othdiscr,,,,,coef_retired_hh_and_discretionary_tour,,, +util_work_accessibility_and_tour_frequency_is_1,Work Accessibility & Tour Frequency =1,WorkLocationLogsum * ((tot_tours == 1) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_1,coef_work_accessibility_and_tour_frequency_is_1,,,,,, +util_work_accessibility_and_tour_frequency_is_2,Work Accessibility & Tour Frequency =2,WorkLocationLogsum * ((tot_tours == 2) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_2,coef_work_accessibility_and_tour_frequency_is_2,,,,,, +util_work_accessibility_and_tour_frequency_is_3,Work Accessibility & Tour Frequency =3,WorkLocationLogsum * ((tot_tours == 3) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_3,,,,,,, +util_work_accessibility_and_tour_frequency_is_3_plus,Work Accessibility & Tour Frequency =3+,WorkLocationLogsum * ((tot_tours >= 3) & (cdap_activity == 'M')),,coef_work_accessibility_and_tour_frequency_is_3_plus,,,,,, +util_work_accessibility_and_tour_frequency_is_4,Work Accessibility & Tour Frequency =4,WorkLocationLogsum * ((tot_tours == 4) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_4,,,,,,, +util_work_accessibility_and_tour_frequency_is_5_plus,Work Accessibility & Tour Frequency =5+,WorkLocationLogsum * ((tot_tours > 5) & (cdap_activity == 'M')),coef_work_accessibility_and_tour_frequency_is_5_plus,,,,,,, +util_school_accessibility_and_tour_frequency_is_1,School Accessibility & Tour Frequency =1,SchoolLocationLogsum * ((tot_tours == 1) & (cdap_activity == 'M')),,,,,,,coef_school_accessibility_and_tour_frequency_is_1, +util_school_accessibility_and_tour_frequency_is_1_plus,School Accessibility & Tour Frequency =1+,SchoolLocationLogsum * ((tot_tours >= 1) & (cdap_activity == 'M')),,,coef_school_accessibility_and_tour_frequency_is_1_plus,,,,, +util_school_accessibility_and_tour_frequency_is_2_plus,School Accessibility & Tour Frequency =2+,SchoolLocationLogsum * ((tot_tours >= 2) & (cdap_activity == 'M')),,,,,,coef_school_accessibility_and_tour_frequency_is_2_plus,coef_school_accessibility_and_tour_frequency_is_2_plus, +util_work_from_home_and_tour_frequency_is_1,Work From Home & Tour Frequency =1,(WorkLocation==99999) & (tot_tours == 1),coef_work_from_home_and_tour_frequency_is_1,,,,,,, +util_work_from_home_and_tour_frequency_is_2,Work From Home & Tour Frequency =2,(WorkLocation==99999) & (tot_tours == 2),coef_work_from_home_and_tour_frequency_is_2,,,,,,, +util_work_from_home_and_tour_frequency_is_3,Work From Home & Tour Frequency =3,(WorkLocation==99999) & (tot_tours == 3),coef_work_from_home_and_tour_frequency_is_3,,,,,,, +util_work_from_home_and_tour_frequency_is_4,Work From Home & Tour Frequency =4,(WorkLocation==99999) & (tot_tours == 4),coef_work_from_home_and_tour_frequency_is_4,,,,,,, +util_work_from_home_and_tour_frequency_is_5_plus,Work From Home & Tour Frequency =5+,(WorkLocation==99999) & (tot_tours > 5),coef_work_from_home_and_tour_frequency_is_5_plus,,,,,,, +util_retail_accessibility_for_escorting,Retail Accessibility for Escorting,((autosnum_workers)*escort_hov_oversufficient_accessibility)*escort,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting,,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting,,coef_retail_accessibility_for_escorting,coef_retail_accessibility_for_escorting +util_retail_accessibility_for_shopping,Retail Accessibility for Shopping,((autosnum_workers)*shop_hov_oversufficient_accessibility)*shopping,coef_retail_accessibility_for_shopping,,,,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping,coef_retail_accessibility_for_shopping +util_retail_accessibility_for_maintenance,Retail Accessibility for Maintenance,((autosnum_workers)*maint_hov_oversufficient_accessibility)*othmaint,coef_retail_accessibility_for_maintenance,coef_retail_accessibility_for_maintenance,,coef_retail_accessibility_for_maintenance,coef_retail_accessibility_for_maintenance,,coef_retail_accessibility_for_maintenance, +util_retail_accessibility_for_eating_out,Retail Accessibility for Eating Out,((autosnum_workers)*eat_hov_oversufficient_accessibility)*eatout,coef_retail_accessibility_for_eating_out,coef_retail_accessibility_for_eating_out,,,,coef_retail_accessibility_for_eating_out,coef_retail_accessibility_for_eating_out, +util_retail_accessibility_for_discretionary,Retail Accessibility for Discretionary,((autosnum_workers)*discr_hov_oversufficient_accessibility)*othdiscr,,,coef_retail_accessibility_for_discretionary,coef_retail_accessibility_for_discretionary,coef_retail_accessibility_for_discretionary,,coef_retail_accessibility_for_discretionary, +util_retail_accessibility_for_visiting,Retail Accessibility for Visiting,((autosnum_workers)*visit_hov_oversufficient_accessibility)*othdiscr,,,,coef_retail_accessibility_for_visiting,coef_retail_accessibility_for_visiting,,, +util_walk_accessibility_for_discretionary,Walk Accessibility for Discretionary,nonmandatory_nm_accessibility *othdiscr,coef_walk_accessibility_for_discretionary,coef_walk_accessibility_for_discretionary,,,,,, +util_walk_accessibility_for_eat_out,Walk Accessibility for Eating Out,nonmandatory_nm_accessibility *eatout,,,coef_walk_accessibility_for_eatout,coef_walk_accessibility_for_eatout,,,, +util_origin_population_density_for_visiting,Population Density & Visiting tour,PopDen * social,,coef_origin_population_density_for_visiting,,,,,, +util_college_education_and_escorting,College Education & Escorting tour,(education_attainment>=13) * escort,,,,,coef_college_education_for_escorting,,, +util_college_education_and_visiting,College Education & Visiting tour,(education_attainment>=13) * social,coef_college_education_for_visiting,,,,coef_college_education_for_visiting,,, +util_college_education_and_discretionary,College Education & Discretionary tour,(education_attainment>=13) * othdiscr,coef_college_education_for_discretionary,coef_college_education_for_discretionary,,coef_college_education_for_discretionary,coef_college_education_for_discretionary,,, +util_college_education_and_shopping,College Education & Shopping tour,(education_attainment>=13) * shopping,,,,coef_college_education_for_shopping,coef_college_education_for_shopping,,, +util_college_education_and_maintenance,College Education & Maintenance tour,(education_attainment>=13) * othmaint,,,,coef_college_education_for_maintenance,,,, +util_college_education_and_eating_out,College Education & Eating out tour,(education_attainment>=13) * eatout,,,,coef_college_education_for_eatout,,,, +util_low_education_and_visiting,Less than High School Education & Visiting tour,(education_attainment<9) * social,coef_low_education_for_visiting,,,coef_low_education_for_visiting,,,, +util_low_education_and_discretionary,Less than High School Education & Discretionary tour,(education_attainment<9) * othdiscr,,coef_low_education_for_discretionary,,coef_low_education_for_discretionary,coef_low_education_for_discretionary,,, +util_low_education_and_escorting,Less than High School Education & Escorting tour,(education_attainment<9) * escort,,,,coef_low_education_for_escorting,coef_low_education_for_escorting,,, +util_low_education_and_shopping,Less than High School Education & Shopping tour,(education_attainment<9) * shopping,,,,coef_low_education_for_shopping,coef_low_education_for_shopping,,, +util_low_education_and_eating_out,Less than High School Education & Eating out tour,(education_attainment<9) * eatout,,,,coef_low_education_for_eatout,,,, +util_low_education_and_maintenance,Less than High School Education & Maintenance tour,(education_attainment<9) * othmaint,,,,,coef_low_education_for_maintenance,,, +util_detached_household_and_escorting_tour,Detached Household & Escorting tour,(building_size==1) * escort,coef_detached_household_and_escorting,coef_detached_household_and_escorting,,,coef_detached_household_and_escorting,,, +util_detached_household_and_eat_out,Detached Household & Eating out tour,(building_size==1) * eatout,,coef_detached_household_and_eating_out,,,coef_detached_household_and_eating_out,,, +util_detached_household_and_discretionary,Detached Household & discretionary tour,(building_size==1) * othdiscr,,,coef_detached_household_and_discretionary,,coef_detached_household_and_discretionary,,, +util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant +util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant +util_1_shopping_tours_constant,1 Shopping Tours Constant,shopping == 1,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant,coef_1_shopping_tours_constant +util_2_plus_shopping_tours_constant,2+ Shopping Tours Constant,shopping >= 2,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant,coef_2_plus_shopping_tours_constant +util_1_maintenance_tours_constant,1 Maintenance Tours Constant,othmaint == 1,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant,coef_1_maintenance_tours_constant +util_2_plus_maintenance_tours_constant,2+ Maintenance Tours Constant,othmaint >= 2,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant,coef_2_plus_maintenance_tours_constant +util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,(eatout >= 1) * eatout,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant +util_1_plus_visting_tours_constant,1+ Visting Tours Constant,(social >= 1) * social,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant +util_1_other_discretionary_tours_constant,1 Other Discretionary Tours Constant,othdiscr == 1,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant,coef_1_other_discretionary_tours_constant +util_2_plus_other_discretionary_tours_constant,2+ Other Discretionary Tours Constant,othdiscr >= 2,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant,coef_2_plus_other_discretionary_tours_constant +util_1_escort_tour_constant_tm2,1 Escort Tour constant_tm2,escort == 1,,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2,coef_1_escort_tour_constant_tm2 +util_2_plus_escort_tours_constant_tm2,2+ Escort Tours constant_tm2,escort >= 2,,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2,coef_2_plus_escort_tours_constant_tm2 +util_1_shopping_tours_constant_tm2,1 Shopping Tours constant_tm2,shopping == 1,,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2,coef_1_shopping_tours_constant_tm2 +util_2_plus_shopping_tours_constant_tm2,2+ Shopping Tours constant_tm2,shopping >= 2,,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2,coef_2_plus_shopping_tours_constant_tm2 +util_1_maintenance_tours_constant_tm2,1 Maintenance Tours constant_tm2,othmaint == 1,,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2,coef_1_maintenance_tours_constant_tm2 +util_2_plus_maintenance_tours_constant_tm2,2+ Maintenance Tours constant_tm2,othmaint >= 2,,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2,coef_2_plus_maintenance_tours_constant_tm2 +util_1_plus_eating_out_tours_constant_tm2,1+ Eating Out Tours constant_tm2,(eatout >= 1) * eatout,,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2,coef_1_plus_eating_out_tours_constant_tm2 +util_1_plus_visting_tours_constant_tm2,1+ Visting Tours constant_tm2,(social >= 1) * social,,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2,coef_1_plus_visting_tours_constant_tm2 +util_1_other_discretionary_tours_constant_tm2,1 Other Discretionary Tours constant_tm2,othdiscr == 1,,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2,coef_1_other_discretionary_tours_constant_tm2 +util_2_plus_other_discretionary_tours_constant_tm2,2+ Other Discretionary Tours constant_tm2,othdiscr >= 2,,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2,coef_2_plus_other_discretionary_tours_constant_tm2 +util_mandatory_dap_and_1_non_mand_tour_ABM_2_calib,Mandatory and 1 Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours==1),coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour,,,coef_mandatory_dap_and_1_non_mand_tour,coef_mandatory_dap_and_1_non_mand_tour, +util_mandatory_dap_and_2_non_mand_tour_ABM_2_calib,Mandatory and 2 Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours==2),coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour,,,coef_mandatory_dap_and_2_non_mand_tour,coef_mandatory_dap_and_2_non_mand_tour, +util_mandatory_dap_and_3_plus_non_mand_tour_ABM_2_calib,Mandatory and 3+ Non-Mandatory - ABM2 Calibration,(cdap_activity=='M') & (tot_tours>=3),coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour,,,coef_mandatory_dap_and_3_plus_non_mand_tour,coef_mandatory_dap_and_3_plus_non_mand_tour, +util_non_mandatory_dap_and_1_non_mand_tour_ABM_2_calib,0 Mandatory and 1 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==1),coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour,coef_non_mandatory_dap_and_1_non_mand_tour +util_non_mandatory_dap_and_2_non_mand_tour_ABM_2_calib,0 Mandatory and 2 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==2),coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour,coef_non_mandatory_dap_and_2_non_mand_tour +util_non_mandatory_dap_and_3_non_mand_tour_ABM_2_calib,0 Mandatory and 3 Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours==3),coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour,coef_non_mandatory_dap_and_3_non_mand_tour +util_non_mandatory_dap_and_4_plus_non_mand_tour_ABM_2_calib,0 Mandatory and 4+ Non-Mandatory - ABM2 Calibration,(cdap_activity=='N') & (tot_tours>=4),coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour,coef_non_mandatory_dap_and_4_plus_non_mand_tour diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml new file mode 100644 index 000000000..53f56d613 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml @@ -0,0 +1,52 @@ + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv + - NAME: PTYPE_DRIVING + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv + - NAME: PTYPE_SCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 8 + COEFFICIENTS: non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours +# - accessibility + +CONSTANTS: + PTYPE_FULL: 1 + PTYPE_PART: 2 + PTYPE_UNIVERSITY: 3 + PTYPE_NONWORK: 4 + PTYPE_RETIRED: 5 + PTYPE_DRIVING: 6 + PTYPE_SCHOOL: 7 + PTYPE_PRESCHOOL: 8 diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv new file mode 100644 index 000000000..e4d4f2e4a --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv @@ -0,0 +1,198 @@ +escort,shopping,othmaint,eatout,social,othdiscr +0,0,0,0,0,0 +0,0,0,0,0,1 +0,0,0,0,0,2 +0,0,0,0,1,0 +0,0,0,0,1,1 +0,0,0,0,1,2 +0,0,0,1,0,0 +0,0,0,1,0,1 +0,0,0,1,0,2 +0,0,0,1,1,0 +0,0,0,1,1,1 +0,0,0,1,1,2 +0,0,1,0,0,0 +0,0,1,0,0,1 +0,0,1,0,0,2 +0,0,1,0,1,0 +0,0,1,0,1,1 +0,0,1,0,1,2 +0,0,1,1,0,0 +0,0,1,1,0,1 +0,0,1,1,0,2 +0,0,1,1,1,0 +0,0,1,1,1,1 +0,0,1,1,1,2 +0,0,2,0,0,0 +0,0,2,0,0,1 +0,0,2,0,0,2 +0,0,2,0,1,0 +0,0,2,0,1,1 +0,0,2,0,1,2 +0,0,2,1,0,0 +0,0,2,1,0,1 +0,0,2,1,0,2 +0,0,2,1,1,0 +0,0,2,1,1,1 +0,1,0,0,0,0 +0,1,0,0,0,1 +0,1,0,0,0,2 +0,1,0,0,1,0 +0,1,0,0,1,1 +0,1,0,0,1,2 +0,1,0,1,0,0 +0,1,0,1,0,1 +0,1,0,1,0,2 +0,1,0,1,1,0 +0,1,0,1,1,1 +0,1,0,1,1,2 +0,1,1,0,0,0 +0,1,1,0,0,1 +0,1,1,0,0,2 +0,1,1,0,1,0 +0,1,1,0,1,1 +0,1,1,0,1,2 +0,1,1,1,0,0 +0,1,1,1,0,1 +0,1,1,1,0,2 +0,1,1,1,1,0 +0,1,1,1,1,1 +0,1,2,0,0,0 +0,1,2,0,0,1 +0,1,2,0,0,2 +0,1,2,0,1,0 +0,1,2,0,1,1 +0,1,2,1,0,0 +0,1,2,1,0,1 +0,1,2,1,1,0 +0,2,0,0,0,0 +0,2,0,0,0,1 +0,2,0,0,0,2 +0,2,0,0,1,0 +0,2,0,0,1,1 +0,2,0,0,1,2 +0,2,0,1,0,0 +0,2,0,1,0,1 +0,2,0,1,0,2 +0,2,0,1,1,0 +0,2,0,1,1,1 +0,2,1,0,0,0 +0,2,1,0,0,1 +0,2,1,0,0,2 +0,2,1,0,1,0 +0,2,1,0,1,1 +0,2,1,1,0,0 +0,2,1,1,0,1 +0,2,1,1,1,0 +0,2,2,0,0,0 +0,2,2,0,0,1 +0,2,2,0,1,0 +0,2,2,1,0,0 +1,0,0,0,0,0 +1,0,0,0,0,1 +1,0,0,0,0,2 +1,0,0,0,1,0 +1,0,0,0,1,1 +1,0,0,0,1,2 +1,0,0,1,0,0 +1,0,0,1,0,1 +1,0,0,1,0,2 +1,0,0,1,1,0 +1,0,0,1,1,1 +1,0,0,1,1,2 +1,0,1,0,0,0 +1,0,1,0,0,1 +1,0,1,0,0,2 +1,0,1,0,1,0 +1,0,1,0,1,1 +1,0,1,0,1,2 +1,0,1,1,0,0 +1,0,1,1,0,1 +1,0,1,1,0,2 +1,0,1,1,1,0 +1,0,1,1,1,1 +1,0,2,0,0,0 +1,0,2,0,0,1 +1,0,2,0,0,2 +1,0,2,0,1,0 +1,0,2,0,1,1 +1,0,2,1,0,0 +1,0,2,1,0,1 +1,0,2,1,1,0 +1,1,0,0,0,0 +1,1,0,0,0,1 +1,1,0,0,0,2 +1,1,0,0,1,0 +1,1,0,0,1,1 +1,1,0,0,1,2 +1,1,0,1,0,0 +1,1,0,1,0,1 +1,1,0,1,0,2 +1,1,0,1,1,0 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,0,0,1 +1,1,1,0,0,2 +1,1,1,0,1,0 +1,1,1,0,1,1 +1,1,1,1,0,0 +1,1,1,1,0,1 +1,1,1,1,1,0 +1,1,2,0,0,0 +1,1,2,0,0,1 +1,1,2,0,1,0 +1,1,2,1,0,0 +1,2,0,0,0,0 +1,2,0,0,0,1 +1,2,0,0,0,2 +1,2,0,0,1,0 +1,2,0,0,1,1 +1,2,0,1,0,0 +1,2,0,1,0,1 +1,2,0,1,1,0 +1,2,1,0,0,0 +1,2,1,0,0,1 +1,2,1,0,1,0 +1,2,1,1,0,0 +1,2,2,0,0,0 +2,0,0,0,0,0 +2,0,0,0,0,1 +2,0,0,0,0,2 +2,0,0,0,1,0 +2,0,0,0,1,1 +2,0,0,0,1,2 +2,0,0,1,0,0 +2,0,0,1,0,1 +2,0,0,1,0,2 +2,0,0,1,1,0 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,0,0,1 +2,0,1,0,0,2 +2,0,1,0,1,0 +2,0,1,0,1,1 +2,0,1,1,0,0 +2,0,1,1,0,1 +2,0,1,1,1,0 +2,0,2,0,0,0 +2,0,2,0,0,1 +2,0,2,0,1,0 +2,0,2,1,0,0 +2,1,0,0,0,0 +2,1,0,0,0,1 +2,1,0,0,0,2 +2,1,0,0,1,0 +2,1,0,0,1,1 +2,1,0,1,0,0 +2,1,0,1,0,1 +2,1,0,1,1,0 +2,1,1,0,0,0 +2,1,1,0,0,1 +2,1,1,0,1,0 +2,1,1,1,0,0 +2,1,2,0,0,0 +2,2,0,0,0,0 +2,2,0,0,0,1 +2,2,0,0,1,0 +2,2,0,1,0,0 +2,2,1,0,0,0 diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv new file mode 100644 index 000000000..9b63dbc42 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -0,0 +1,36 @@ +Description,Target,Expression +#,, +,low_income,persons.income<=30000 +,medium_low_income,(persons.income > 30000) & (persons.income <= 60000) +,medium_income,(persons.income > 60000) & (persons.income <= 100000) +,medium_high_income,(persons.income > 100000) & (persons.income <= 150000) +,high_income,(persons.income > 150000) +,no_cars,(persons.auto_ownership == 0) +,car_sufficiency,persons.autos-persons.num_workers +#,, +# joint tour counts per persons,, +get joint tours,_JOINT_TOURS,tours[tours.tour_category=='JOINT_NON_MANDATORY'] +create participant list in joint tours,_JOINT_TOURS,"_JOINT_TOURS.assign(person_num=_JOINT_TOURS.apply(lambda x: [int(p) for p in x.tour_participants.split(' ')],axis=1))" +explode joint tour participants,_JOINT_TOURS,_JOINT_TOURS.explode('person_num') +join joint tour info with persons,_JOINT_TOURS_PERSONS,"pd.merge(_JOINT_TOURS[['household_id','person_num','tour_purpose']],persons.reset_index()[['household_id','person_num','person_id']], on=['household_id','person_num'], how='left')" +,num_person_joint_tours,"reindex_i(_JOINT_TOURS_PERSONS.groupby(['person_id']).size(), persons.index)" +,num_person_joint_shop_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Shop'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_maint_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Maintenance'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_eatout_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Eating Out'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_visit_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Visiting'].groupby(['person_id']).size(), persons.index)" +,num_person_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS_PERSONS[_JOINT_TOURS_PERSONS.tour_purpose=='Discretionary'].groupby(['person_id']).size(), persons.index)" +# non_mandatory tour frequency extension,, +,_INDIV_TOURS,tours[tours.tour_category!='JOINT_NON_MANDATORY'] +,num_mandatory_tours,"reindex_i(_INDIV_TOURS[_INDIV_TOURS.tour_purpose=='MANDATORY'].groupby('person_id').size(), persons.index)" +,has_mandatory_tour,(num_mandatory_tours > 0) * 1 +,has_joint_tour,(num_person_joint_tours > 0) * 1 +# number of person types in household in addition to self,, +,num_full_time_workers_not_self,"np.where(df.ptype == PTYPE_FULL, df.num_full_time_workers-1, df.num_full_time_workers)" +,num_part_time_workers_not_self,"np.where(df.ptype == PTYPE_PART, df.num_part_time_workers-1, df.num_part_time_workers)" +,num_university_students_not_self,"np.where(df.ptype == PTYPE_UNIVERSITY, df.num_university_students-1, df.num_university_students)" +,num_non_workers_not_self,"np.where(df.ptype == PTYPE_NONWORK, df.num_non_workers-1, df.num_non_workers)" +,num_retirees_not_self,"np.where(df.ptype == PTYPE_RETIRED, df.num_retirees-1, df.num_retirees)" +,num_driving_age_students_not_self,"np.where(df.ptype == PTYPE_DRIVING, df.num_driving_age_students-1, df.num_driving_age_students)" +,num_pre_driving_age_school_kids_not_self,"np.where(df.ptype == PTYPE_SCHOOL, df.num_pre_driving_age_school_kids-1, df.num_pre_driving_age_school_kids)" +,num_pre_school_kids_not_self,"np.where(df.ptype == PTYPE_PRESCHOOL, df.num_pre_school_kids-1, df.num_pre_school_kids)" +,retiredHh,"np.where(df.num_full_time_workers+df.num_part_time_workers+df.num_university_students+df.num_driving_age_students+df.num_pre_driving_age_school_kids+df.num_pre_school_kids == 0,1,0)" \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv new file mode 100644 index 000000000..7d443b83a --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv @@ -0,0 +1,60 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-5.0011,F +coef_discretionary_tour,-2.0687,F +coef_shopping_tour,-16.7785,F +coef_maintenance_tour,-3.2583,F +coef_visiting_or_social_tour,-2.6366,F +coef_eating_out_tour,-18.1598,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1_plus,-0.4825,F +coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-2.5161,F +coef_number_of_joint_discretionary_tours,-0.2308,F +coef_more_than_one_discretionary_tours,-1.1938,F +coef_low_income_group_and_escorting_tour,1.1208,F +coef_medium_low_income_group_and_escorting_tour,1.1208,F +coef_low_income_group_and_discretionary_tour,-0.8053,F +coef_mediumhigh_income_group_and_discretionary_tour,0.5225,F +coef_high_income_group_and_discretionary_tour,0.8604,F +coef_high_income_group_and_visiting_tour,0.5513,F +coef_mediumhigh_income_group_and_visiting_tour,0.5513,F +coef_female_and_shopping_tour,0.9646,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.4901,F +coef_number_of_full_time_worker_and_shopping_tour,0.3173,F +coef_number_of_part_time_worker_and_shopping_tour,0.3173,F +coef_number_of_non_worker_and_shopping_tour,0.3173,F +coef_number_of_pre_driving_school_kid_and_shopping_tour,0.3133,F +coef_number_of_pre_school_kid_and_shopping_tour,0.3133,F +coef_number_of_part_time_worker_and_discretionary_tour,0.3202,F +coef_number_of_non_worker_and_discretionary_tour,0.3202,F +coef_number_of_university_student_and_discretionary_tour,0.5522,F +coef_school_accessibility_and_tour_frequency_is_2_plus,1.9398,F +coef_retail_accessibility_for_shopping,0.7809,F +coef_retail_accessibility_for_eating_out,1.0093,F +coef_1_escort_tour_constant,-0.052,F +coef_2_plus_escort_tours_constant,0,T +coef_1_shopping_tours_constant,1.025,F +coef_2_plus_shopping_tours_constant,0,T +coef_1_maintenance_tours_constant,-0.294,F +coef_2_plus_maintenance_tours_constant,0,T +coef_1_plus_eating_out_tours_constant,-1.079,F +coef_1_plus_visting_tours_constant,-0.107,F +coef_1_other_discretionary_tours_constant,-0.393,F +coef_2_plus_other_discretionary_tours_constant,-1.086,F +coef_1_escort_tour_constant_tm2,-1.007,F +coef_2_plus_escort_tours_constant_tm2,0,T +coef_1_shopping_tours_constant_tm2,1.027,F +coef_2_plus_shopping_tours_constant_tm2,1.027,F +coef_1_maintenance_tours_constant_tm2,-0.196,F +coef_2_plus_maintenance_tours_constant_tm2,-0.196,F +coef_1_plus_eating_out_tours_constant_tm2,2.909,F +coef_1_plus_visting_tours_constant_tm2,-1.417,F +coef_1_other_discretionary_tours_constant_tm2,-1.519,F +coef_2_plus_other_discretionary_tours_constant_tm2,-1.519,F +coef_mandatory_dap_and_1_non_mand_tour,1.222628436,F +coef_mandatory_dap_and_2_non_mand_tour,0.593278832,F +coef_mandatory_dap_and_3_plus_non_mand_tour,0.070965231,F +coef_non_mandatory_dap_and_1_non_mand_tour,0.756307862,F +coef_non_mandatory_dap_and_2_non_mand_tour,1.396284118,F +coef_non_mandatory_dap_and_3_non_mand_tour,3.551847757,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,-0.058600545,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv new file mode 100644 index 000000000..bfe82bc6b --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv @@ -0,0 +1,103 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-8.005637824,F +coef_discretionary_tour,-4.191895252,F +coef_shopping_tour,-6.093236864,F +coef_maintenance_tour,-4.450826306,F +coef_visiting_or_social_tour,-3.869879887,F +coef_eating_out_tour,-12.62493048,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,0,T +coef_total_number_of_tours_is_2,1.328670418,F +coef_total_number_of_tours_is_3,3.027432477,F +coef_total_number_of_tours_is_4,3.547389909,F +coef_has_mandatory_tours_and_tour_frequency_is_2,-1.122394705,F +coef_has_mandatory_tours_and_tour_frequency_is_3,-2.057588493,F +coef_has_mandatory_tours_and_tour_frequency_is_4_plus,-2.057588493,F +coef_has_joint_tours_and_tour_frequency_is_3_plus,-0.477609877,F +coef_number_of_joint_shopping_tours,-0.170864341,F +coef_number_of_joint_discretionary_tours,-1.163664908,F +coef_more_than_one_escorting_tours,0.613851731,F +coef_more_than_one_discretionary_tours,-0.236694132,F +coef_high_income_group_and_escorting_tour,-0.066520907,F +coef_mediumhigh_income_group_and_shopping_tour,0.06372943,F +coef_high_income_group_and_shopping_tour,0.06372943,F +coef_low_income_group_and_maintenance_tour,0.251347332,F +coef_mediumlow_income_group_and_maintenance_tour,0.188910438,F +coef_high_income_group_and_maintenance_tour,-0.231879121,F +coef_low_income_group_and_eating_out_tour,-1.810193362,F +coef_high_income_group_and_eating_out_tour,0.31465606,F +coef_low_income_group_and_discretionary_tour,-0.565623738,F +coef_mediumhigh_income_group_and_discretionary_tour,-0.231043198,F +coef_high_income_group_and_discretionary_tour,0.107170058,F +coef_low_income_group_and_visiting_tour,0.178971977,F +coef_female_and_escorting_tour,0.137391737,F +coef_zero_car_ownership_and_tour_frequency_is_2_plus,-0.935804805,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,-0.361005701,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3_plus,0.072613168,F +coef_zero_car_ownership_and_escorting_tour,-0.873271814,F +coef_car_shortage_vs_workers_and_number_of_escorting_tours,0.412574716,F +coef_number_of_full_time_worker_and_escorting_tour,0.212097217,F +coef_number_of_part_time_worker_and_escorting_tour,-0.073067961,F +coef_number_of_non_worker_and_escorting_tour,-0.323356663,F +coef_number_of_retiree_and_escorting_tour,-0.558947959,F +coef_number_of_university_student_and_escorting_tour,0.275136763,F +coef_number_of_driving_school_kid_and_escorting_tour,0.579434039,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.639740336,F +coef_go_out_school_kid_and_escorting_tour,0.384950782,F +coef_number_of_driving_school_kid_and_shopping_tour,-0.218745414,F +coef_number_of_pre_driving_school_kid_and_shopping_tour,-0.218745414,F +coef_number_of_pre_school_kid_and_shopping_tour,-0.218745414,F +coef_number_of_full_time_worker_and_maintenance_tour,-0.219097089,F +coef_number_of_part_time_worker_and_maintenance_tour,-0.219097089,F +coef_number_of_non_worker_and_maintenance_tour,-0.219097089,F +coef_number_of_university_student_and_maintenance_tour,-0.219097089,F +coef_number_of_full_time_worker_and_eating_out_tour,-0.764033574,F +coef_number_of_part_time_worker_and_eating_out_tour,-0.764033574,F +coef_number_of_non_worker_and_eating_out_tour,-0.764033574,F +coef_number_of_retiree_and_eating_out_tour,-0.764033574,F +coef_number_of_university_student_and_eating_out_tour,-0.764033574,F +coef_number_of_pre_driving_school_kid_and_eating_out_tour,-0.355618746,F +coef_number_of_pre_school_kid_and_eating_out_tour,-0.355618746,F +coef_number_of_non_worker_and_visiting_tour,-0.444931696,F +coef_number_of_retiree_and_visiting_tour,-0.444931696,F +coef_number_of_university_student_and_visiting_tour,-0.444931696,F +coef_number_of_driving_school_kid_and_discretionary_tour,-0.021518881,F +coef_number_of_pre_driving_school_kid_and_discretionary_tour,-0.021518881,F +coef_number_of_pre_school_kid_and_discretionary_tour,-0.157036558,F +coef_work_accessibility_and_tour_frequency_is_1,0.3286661,F +coef_work_accessibility_and_tour_frequency_is_2,0.71855271,F +coef_work_accessibility_and_tour_frequency_is_3,0.71855271,F +coef_work_accessibility_and_tour_frequency_is_4,0.71855271,F +coef_work_accessibility_and_tour_frequency_is_5_plus,0.71855271,F +coef_work_from_home_and_tour_frequency_is_1,0.915860359,F +coef_work_from_home_and_tour_frequency_is_2,1.100337176,F +coef_work_from_home_and_tour_frequency_is_3,1.100337176,F +coef_work_from_home_and_tour_frequency_is_4,1.100337176,F +coef_work_from_home_and_tour_frequency_is_5_plus,1.100337176,F +coef_retail_accessibility_for_escorting,0.291279719,F +coef_retail_accessibility_for_shopping,0.205237982,F +coef_retail_accessibility_for_maintenance,0.097381625,F +coef_retail_accessibility_for_eating_out,0.678030129,F +coef_walk_accessibility_for_discretionary,0.045591079,F +coef_college_education_for_visiting,-0.671955208,F +coef_college_education_for_discretionary,0.621905223,F +coef_low_education_for_visiting,0.454831607,F +coef_detached_household_and_escorting,0.22019233,F +coef_1_escort_tour_constant,0.390268916,F +coef_2_plus_escort_tours_constant,0.779302172,F +coef_1_shopping_tours_constant,0.264,F +coef_2_plus_shopping_tours_constant,0.383672128,F +coef_1_maintenance_tours_constant,-0.097866802,F +coef_2_plus_maintenance_tours_constant,-0.090890202,F +coef_1_plus_eating_out_tours_constant,-0.974733991,F +coef_1_plus_visting_tours_constant,-0.147867593,F +coef_1_other_discretionary_tours_constant,0.037252469,F +coef_2_plus_other_discretionary_tours_constant,0.221679699,F +coef_mandatory_dap_and_1_non_mand_tour,0.613102892,F +coef_mandatory_dap_and_2_non_mand_tour,1.298340523,F +coef_mandatory_dap_and_3_plus_non_mand_tour,0.294678801,F +coef_non_mandatory_dap_and_1_non_mand_tour,0.40551385,F +coef_non_mandatory_dap_and_2_non_mand_tour,-0.058684635,F +coef_non_mandatory_dap_and_3_non_mand_tour,0.181724483,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,3.456313043,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv new file mode 100644 index 000000000..4d022be71 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv @@ -0,0 +1,90 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-5.5709,F +coef_discretionary_tour,-3.1828,F +coef_shopping_tour,-1.5833,F +coef_maintenance_tour,-2.0214,F +coef_visiting_or_social_tour,-6.6293,F +coef_eating_out_tour,-8.9856,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_3,-0.793922128,F +coef_total_number_of_tours_is_4,-1.117938236,F +coef_has_joint_tours_and_tour_frequency_is_1,-0.968579459,F +coef_has_joint_tours_and_tour_frequency_is_2,-1.808495529,F +coef_has_joint_tours_and_tour_frequency_is_3_plus,-2.59200638,F +coef_number_of_joint_shopping_tours,-0.466843362,F +coef_number_of_joint_maintenance_tours,-0.575337406,F +coef_more_than_one_escorting_tours,1.3605,F +coef_more_than_one_shopping_tours,-0.7454,F +coef_more_than_one_maintenance_tours,-0.1194,F +coef_more_than_one_discretionary_tours,0.1253,F +coef_low_income_group_and_shopping_tour,-0.2599,F +coef_medium_low_income_group_and_shopping_tour,-0.2599,F +coef_low_income_group_and_eating_out_tour,-0.2857,F +coef_high_income_group_and_eating_out_tour,2.223,F +coef_low_income_group_and_discretionary_tour,-0.5775,F +coef_medium_low_income_group_and_discretionary_tour,-0.5775,F +coef_high_income_group_and_visiting_tour,0.2695,F +coef_mediumhigh_income_group_and_visiting_tour,0.2695,F +coef_female_and_escorting_tour,0.6769,F +coef_female_and_shopping_tour,0.5108,F +coef_female_and_visit_tour,0.7494,F +coef_zero_car_ownership_and_tour_frequency_is_2_plus,-1.0038,F +coef_zero_car_ownership_and_escorting_tour,-0.8907,F +coef_car_surplus_vs_workers_and_number_of_escorting_tours,0.1802,F +coef_number_of_full_time_worker_and_escorting_tour,0.2514,F +coef_number_of_part_time_worker_and_escorting_tour,0.2512,F +coef_number_of_non_worker_and_escorting_tour,-0.3489,F +coef_number_of_retiree_and_escorting_tour,0.2841,F +coef_number_of_university_student_and_escorting_tour,0.4583,F +coef_number_of_driving_school_kid_and_escorting_tour,0.7483,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.9378,F +coef_go_out_school_kid_and_escorting_tour,0.9374,F +coef_number_of_non_worker_and_shopping_tour,-0.478,F +coef_number_of_full_time_worker_and_maintenance_tour,0.0904,F +coef_number_of_part_time_worker_and_maintenance_tour,0.0904,F +coef_number_of_pre_driving_school_kid_and_eating_out_tour,-0.6629,F +coef_number_of_pre_school_kid_and_eating_out_tour,-0.6629,F +coef_number_of_pre_driving_school_kid_and_visiting_tour,-0.4021,F +coef_number_of_pre_school_kid_and_visiting_tour,-0.4021,F +coef_number_of_driving_school_kid_and_discretionary_tour,-0.2039,F +coef_number_of_pre_driving_school_kid_and_discretionary_tour,-0.2039,F +coef_number_of_pre_school_kid_and_discretionary_tour,-0.2039,F +coef_retail_accessibility_for_escorting,0.1609,F +coef_retail_accessibility_for_maintenance,0.0194,F +coef_retail_accessibility_for_discretionary,0.0632,F +coef_retail_accessibility_for_visiting,0.2883,F +coef_walk_accessibility_for_eatout,0.6073,F +coef_college_education_for_discretionary,0.981,F +coef_college_education_for_shopping,0.3677,F +coef_college_education_for_maintenance,0.5982,F +coef_college_education_for_eatout,0.7979,F +coef_low_education_for_visiting,-1.6201,F +coef_low_education_for_discretionary,-0.837,F +coef_low_education_for_escorting,0.1748,F +coef_low_education_for_shopping,-0.3525,F +coef_low_education_for_eatout,-0.4838,F +coef_1_escort_tour_constant,-2.501,F +coef_2_plus_escort_tours_constant,-1.958,F +coef_1_shopping_tours_constant,-2.666,F +coef_2_plus_shopping_tours_constant,-2.884,F +coef_1_maintenance_tours_constant,-2.908,F +coef_2_plus_maintenance_tours_constant,-2.97,F +coef_1_plus_eating_out_tours_constant,-3.022,F +coef_1_plus_visting_tours_constant,-2.736,F +coef_1_other_discretionary_tours_constant,-2.612,F +coef_2_plus_other_discretionary_tours_constant,-2.663,F +coef_1_escort_tour_constant_tm2,-1.52187411,F +coef_2_plus_escort_tours_constant_tm2,-1.489501032,F +coef_1_shopping_tours_constant_tm2,-1.853328287,F +coef_2_plus_shopping_tours_constant_tm2,-1.853328287,F +coef_1_maintenance_tours_constant_tm2,-0.808408856,F +coef_2_plus_maintenance_tours_constant_tm2,-0.808408856,F +coef_1_plus_eating_out_tours_constant_tm2,0.600524339,F +coef_1_plus_visting_tours_constant_tm2,-0.284937967,F +coef_1_other_discretionary_tours_constant_tm2,-0.504897539,F +coef_2_plus_other_discretionary_tours_constant_tm2,-0.504897539,F +coef_non_mandatory_dap_and_1_non_mand_tour,0.563996011,F +coef_non_mandatory_dap_and_2_non_mand_tour,1.1354493,F +coef_non_mandatory_dap_and_3_non_mand_tour,1.647365455,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,6.448243189,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv new file mode 100644 index 000000000..2d32768ef --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv @@ -0,0 +1,103 @@ +coefficient_name,value,Constrain +coef_unavailable,-999,T +coef_escorting_tour,-6.825870475,F +coef_discretionary_tour,-3.103671535,F +coef_shopping_tour,-2.585699928,F +coef_maintenance_tour,-4.876815454,F +coef_visiting_or_social_tour,-4.787916747,F +coef_eating_out_tour,-8.091973006,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,0,T +coef_total_number_of_tours_is_2,1.347662088,F +coef_total_number_of_tours_is_3,1.958684542,F +coef_total_number_of_tours_is_4,3.406917203,F +coef_total_number_of_tours_is_5,3.406917203,F +coef_total_number_of_tours_is_6_plus,3.406917203,F +coef_has_mandatory_tours_and_tour_frequency_is_2,-1.383795025,F +coef_has_mandatory_tours_and_tour_frequency_is_3,-1.383795025,F +coef_has_mandatory_tours_and_tour_frequency_is_4_plus,-2.308089044,F +coef_has_joint_tours_and_tour_frequency_is_4_plus,-0.850963319,F +coef_number_of_joint_discretionary_tours,-0.13985943,F +coef_more_than_one_escorting_tours,0.688722128,F +coef_more_than_one_shopping_tours,-0.806980084,F +coef_more_than_one_discretionary_tours,-1.123565611,F +coef_high_income_group_and_escorting_tour,-0.188741999,F +coef_low_income_group_and_shopping_tour,-0.584603787,F +coef_medium_low_income_group_and_shopping_tour,-0.191476033,F +coef_medium_high_income_group_and_shopping_tour,0.302369299,F +coef_high_income_group_and_shopping_tour,0.302369299,F +coef_low_income_group_and_eating_out_tour,-1.70963028,F +coef_medium_low_income_group_and_eating_out_tour,-1.018936807,F +coef_low_income_group_and_discretionary_tour,-0.697953689,F +coef_mediumhigh_income_group_and_discretionary_tour,0.441095148,F +coef_high_income_group_and_discretionary_tour,0.441095148,F +coef_female_and_escorting_tour,0.827860636,F +coef_female_and_shopping_tour,0.650180816,F +coef_female_and_eatingout_tour,-1.25440798,F +coef_zero_car_ownership_and_tour_frequency_is_2_plus,-2.213461849,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2_plus,-0.490152764,F +coef_car_shortage_vs_workers_and_number_of_escorting_tours,0.245040809,F +coef_car_surplus_vs_workers_and_number_of_escorting_tours,-0.273518269,F +coef_car_shortage_vs_workers_and_number_of_shopping_tours,0.377878264,F +coef_car_surplus_vs_workers_and_number_of_shopping_tours,-0.579935901,F +coef_number_of_full_time_worker_and_escorting_tour,0.097720305,F +coef_number_of_part_time_worker_and_escorting_tour,-0.13505138,F +coef_number_of_non_worker_and_escorting_tour,-0.264726717,F +coef_number_of_university_student_and_escorting_tour,0.324362136,F +coef_number_of_driving_school_kid_and_escorting_tour,0.592250815,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.807393603,F +coef_go_out_school_kid_and_escorting_tour,0.584027557,F +coef_number_of_full_time_worker_and_shopping_tour,-0.571692163,F +coef_number_of_part_time_worker_and_shopping_tour,-0.571692163,F +coef_number_of_non_worker_and_shopping_tour,-0.571692163,F +coef_number_of_full_time_worker_and_maintenance_tour,-0.242545271,F +coef_number_of_part_time_worker_and_maintenance_tour,-0.242545271,F +coef_number_of_non_worker_and_maintenance_tour,-1.173436234,F +coef_number_of_full_time_worker_and_eating_out_tour,-0.273301702,F +coef_number_of_part_time_worker_and_visiting_tour,0.887755866,F +coef_number_of_retiree_and_visiting_tour,0.885418039,F +coef_number_of_driving_school_kid_and_visiting_tour,0.023898086,F +coef_number_of_pre_driving_school_kid_and_visiting_tour,0.023898086,F +coef_number_of_pre_school_kid_and_visiting_tour,0.023898086,F +coef_number_of_driving_school_kid_and_discretionary_tour,0.257383748,F +coef_number_of_pre_driving_school_kid_and_discretionary_tour,0.257383748,F +coef_number_of_pre_school_kid_and_discretionary_tour,-2.431661289,F +coef_work_accessibility_and_tour_frequency_is_1,0.628740481,F +coef_work_accessibility_and_tour_frequency_is_2,0.842604039,F +coef_work_accessibility_and_tour_frequency_is_3_plus,0.814255739,F +coef_retail_accessibility_for_escorting,0.199705264,F +coef_retail_accessibility_for_maintenance,0.189588653,F +coef_retail_accessibility_for_eating_out,0.387577711,F +coef_walk_accessibility_for_discretionary,0.032230313,F +coef_origin_population_density_for_visiting,0.048808797,F +coef_college_education_for_discretionary,0.256548173,F +coef_low_education_for_discretionary,-0.974189529,F +coef_detached_household_and_escorting,0.290441415,F +coef_detached_household_and_eating_out,-0.227906236,F +coef_1_escort_tour_constant,0.444537569,F +coef_2_plus_escort_tours_constant,1.45203917,F +coef_1_shopping_tours_constant,0.19959348,F +coef_2_plus_shopping_tours_constant,0.365593218,F +coef_1_maintenance_tours_constant,-0.050890967,F +coef_2_plus_maintenance_tours_constant,0.025818889,F +coef_1_plus_eating_out_tours_constant,-0.688218447,F +coef_1_plus_visting_tours_constant,-0.761523103,F +coef_1_other_discretionary_tours_constant,0.151171319,F +coef_2_plus_other_discretionary_tours_constant,0.349908582,F +coef_1_escort_tour_constant_tm2,-1.041387049,F +coef_2_plus_escort_tours_constant_tm2,-1.209189663,F +coef_1_shopping_tours_constant_tm2,-1.496588445,F +coef_2_plus_shopping_tours_constant_tm2,-1.496588445,F +coef_1_maintenance_tours_constant_tm2,-0.067949689,F +coef_2_plus_maintenance_tours_constant_tm2,-0.067949689,F +coef_1_plus_eating_out_tours_constant_tm2,0.534488324,F +coef_1_plus_visting_tours_constant_tm2,-0.010969663,F +coef_1_other_discretionary_tours_constant_tm2,-0.530568731,F +coef_2_plus_other_discretionary_tours_constant_tm2,-0.530568731,F +coef_mandatory_dap_and_1_non_mand_tour,0.435298756,F +coef_mandatory_dap_and_2_non_mand_tour,1.193250166,F +coef_mandatory_dap_and_3_plus_non_mand_tour,1.803605616,F +coef_non_mandatory_dap_and_1_non_mand_tour,0.612888101,F +coef_non_mandatory_dap_and_2_non_mand_tour,0.745415157,F +coef_non_mandatory_dap_and_3_non_mand_tour,1.266755805,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,2.308013647,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv new file mode 100644 index 000000000..e3f9dce15 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv @@ -0,0 +1,47 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-7.2688,F +coef_discretionary_tour,-4.2006,F +coef_shopping_tour,-43.4464,F +coef_maintenance_tour,-3.3790,F +coef_visiting_or_social_tour,-4.0352,F +coef_eating_out_tour,-5.2924,F +coef_total_number_of_tours_is_0_prior_tours,0.0000,T +coef_total_number_of_tours_is_2,-0.7216,F +coef_total_number_of_tours_is_3,-0.9586,F +coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-2.1108,F +coef_more_than_one_escorting_tours,2.3269,F +coef_high_income_group_and_escorting_tour,-0.6921,F +coef_zero_car_ownership_and_escorting_tour,-0.9936,F +coef_car_shortage_vs_workers_and_number_of_escorting_tours,-0.9936,F +coef_number_of_part_time_worker_and_escorting_tour,0.7831,F +coef_number_of_non_worker_and_escorting_tour,0.7831,F +coef_number_of_university_student_and_escorting_tour,0.8044,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.5152,F +coef_go_out_school_kid_and_escorting_tour,0.6072,F +coef_retail_accessibility_for_escorting,0.2739,F +coef_retail_accessibility_for_shopping,2.7431,F +coef_1_escort_tour_constant,-8.402,F +coef_2_plus_escort_tours_constant,-9.519,F +coef_1_shopping_tours_constant,-3.856,F +coef_2_plus_shopping_tours_constant,0.000,T +coef_1_maintenance_tours_constant,-7.785,F +coef_2_plus_maintenance_tours_constant,-6.925,F +coef_1_plus_eating_out_tours_constant,-7.585,F +coef_1_plus_visting_tours_constant,-7.599,F +coef_1_other_discretionary_tours_constant,-7.462,F +coef_2_plus_other_discretionary_tours_constant,-15.000,F +coef_1_escort_tour_constant_tm2,-1.496,F +coef_2_plus_escort_tours_constant_tm2,-1.588,F +coef_1_shopping_tours_constant_tm2,0.031,F +coef_2_plus_shopping_tours_constant_tm2,0.031,F +coef_1_maintenance_tours_constant_tm2,-1.011,F +coef_2_plus_maintenance_tours_constant_tm2,-1.011,F +coef_1_plus_eating_out_tours_constant_tm2,-1.923,F +coef_1_plus_visting_tours_constant_tm2,-1.085,F +coef_1_other_discretionary_tours_constant_tm2,-1.126,F +coef_2_plus_other_discretionary_tours_constant_tm2,-1.126,F +coef_non_mandatory_dap_and_1_non_mand_tour,1.113941467,F +coef_non_mandatory_dap_and_2_non_mand_tour,0.375527882,F +coef_non_mandatory_dap_and_3_non_mand_tour,2.691413911,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,8.404994214,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv new file mode 100644 index 000000000..896947920 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv @@ -0,0 +1,84 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-10.4405,F +coef_discretionary_tour,-6.1189,F +coef_shopping_tour,-4.6661,F +coef_maintenance_tour,-2.4724,F +coef_visiting_or_social_tour,-3.677,F +coef_eating_out_tour,-2.5648,F +coef_total_number_of_tours_is_0_prior_tours,-1.140675606,F +coef_has_joint_tours_and_tour_frequency_is_1,-1.601015729,F +coef_has_joint_tours_and_tour_frequency_is_2_plus,-2.105529269,F +coef_number_of_joint_shopping_tours,-0.6377,F +coef_number_of_joint_maintenance_tours,-0.4348,F +coef_number_of_joint_eating_out_tours,-0.7839,F +coef_number_of_joint_discretionary_tours,-0.085,F +coef_more_than_one_escorting_tours,2.0794,F +coef_more_than_one_shopping_tours,-1.1624,F +coef_more_than_one_maintenance_tours,-0.5943,F +coef_mediumhigh_income_group_and_shopping_tour,0.4056,F +coef_high_income_group_and_shopping_tour,0.4056,F +coef_low_income_group_and_maintenance_tour,-0.2202,F +coef_mediumlow_income_group_and_maintenance_tour,-0.2202,F +coef_low_income_group_and_eating_out_tour,-1.0384,F +coef_medium_low_income_group_and_eating_out_tour,-0.4217,F +coef_low_income_group_and_discretionary_tour,-0.3758,F +coef_mediumhigh_income_group_and_discretionary_tour,0.1222,F +coef_high_income_group_and_discretionary_tour,0.1222,F +coef_high_income_group_and_visiting_tour,-1.104,F +coef_female_and_shopping_tour,-0.0455,F +coef_female_and_maintenance_tour,-0.2926,F +coef_female_and_eatingout_tour,-0.2472,F +coef_female_and_discretionary_tour,-0.0647,F +coef_number_of_full_time_worker_and_escorting_tour,-0.1294,F +coef_number_of_part_time_worker_and_escorting_tour,-0.1294,F +coef_number_of_non_worker_and_escorting_tour,-0.1294,F +coef_number_of_driving_school_kid_and_escorting_tour,0.6054,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.7285,F +coef_number_of_retiree_and_shopping_tour,-0.4462,F +coef_number_of_retiree_and_maintenance_tour,-0.4112,F +coef_number_of_retiree_and_visiting_tour,-0.2327,F +coef_retired_hh_and_escorting_tour,-0.8133,F +coef_retired_hh_and_shopping_tour,0.2715,F +coef_retired_hh_and_eating_out_tour,0.2011,F +coef_retired_hh_and_discretionary_tour,0.2624,F +coef_retail_accessibility_for_escorting,0.5109,F +coef_retail_accessibility_for_shopping,0.2262,F +coef_retail_accessibility_for_maintenance,0.1154,F +coef_retail_accessibility_for_discretionary,0.3252,F +coef_retail_accessibility_for_visiting,0.0533,F +coef_college_education_for_escorting,0.1522,F +coef_college_education_for_visiting,0.2179,F +coef_college_education_for_discretionary,0.3046,F +coef_college_education_for_shopping,0.2046,F +coef_low_education_for_discretionary,-0.8504,F +coef_low_education_for_escorting,-0.8443,F +coef_low_education_for_shopping,-0.5676,F +coef_low_education_for_maintenance,-0.6861,F +coef_detached_household_and_escorting,0.3809,F +coef_detached_household_and_eating_out,-0.3389,F +coef_detached_household_and_discretionary,-0.3286,F +coef_1_escort_tour_constant,-2.342,F +coef_2_plus_escort_tours_constant,-1.988,F +coef_1_shopping_tours_constant,-2.414,F +coef_2_plus_shopping_tours_constant,-2.101,F +coef_1_maintenance_tours_constant,-2.869,F +coef_2_plus_maintenance_tours_constant,-3.015,F +coef_1_plus_eating_out_tours_constant,-2.979,F +coef_1_plus_visting_tours_constant,-2.806,F +coef_1_other_discretionary_tours_constant,-3.196,F +coef_2_plus_other_discretionary_tours_constant,-3.482,F +coef_1_escort_tour_constant_tm2,-0.287618769,F +coef_2_plus_escort_tours_constant_tm2,0.65554552,F +coef_1_shopping_tours_constant_tm2,-1.385280597,F +coef_2_plus_shopping_tours_constant_tm2,-1.385280597,F +coef_1_maintenance_tours_constant_tm2,-1.480017973,F +coef_2_plus_maintenance_tours_constant_tm2,-1.480017973,F +coef_1_plus_eating_out_tours_constant_tm2,-1.385672408,F +coef_1_plus_visting_tours_constant_tm2,-1.218799144,F +coef_1_other_discretionary_tours_constant_tm2,0.538161829,F +coef_2_plus_other_discretionary_tours_constant_tm2,0.538161829,F +coef_non_mandatory_dap_and_1_non_mand_tour,1.152725777,F +coef_non_mandatory_dap_and_2_non_mand_tour,0.022553605,F +coef_non_mandatory_dap_and_3_non_mand_tour,6.338227151,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,6.237997735,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv new file mode 100644 index 000000000..405ec48a1 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv @@ -0,0 +1,72 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-10.3136,F +coef_discretionary_tour,-8.0543,F +coef_shopping_tour,-17.8882,F +coef_maintenance_tour,-14.5925,F +coef_visiting_or_social_tour,-2.9744,F +coef_eating_out_tour,-42.1591,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_2,-0.7045,F +coef_total_number_of_tours_is_3,-0.7045,F +coef_has_mandatory_tours_and_tour_frequency_is_1,-1.8596,F +coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-6.1247,F +coef_has_joint_tours_and_tour_frequency_is_1_plus,-1.0154,F +coef_number_of_joint_discretionary_tours,-1.2257,F +coef_more_than_one_escorting_tours,0.3358,F +coef_more_than_one_discretionary_tours,-0.8724,F +coef_high_income_group_and_shopping_tour,1.0878,F +coef_mediumhigh_income_group_and_maintenance_tour,1.0499,F +coef_high_income_group_and_maintenance_tour,1.0499,F +coef_low_income_group_and_discretionary_tour,-2.1495,F +coef_medium_low_income_group_and_discretionary_tour,-0.9706,F +coef_high_income_group_and_discretionary_tour,0.6655,F +coef_low_income_group_and_visiting_tour,-1.6227,F +coef_medium_low_income_group_and_visiting_tour,-1.6227,F +coef_high_income_group_and_visiting_tour,1.1424,F +coef_car_surplus_vs_workers_and_number_of_maintenance_tours,0.4721,F +coef_car_surplus_vs_workers_and_number_of_discretionary_tours,0.1531,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.4372,F +coef_number_of_driving_school_kid_and_shopping_tour,-0.1631,F +coef_number_of_pre_driving_school_kid_and_shopping_tour,-0.1631,F +coef_number_of_pre_school_kid_and_shopping_tour,-0.1631,F +coef_number_of_driving_school_kid_and_visiting_tour,0.3656,F +coef_number_of_pre_driving_school_kid_and_visiting_tour,0.3656,F +coef_number_of_pre_school_kid_and_visiting_tour,0.3656,F +coef_number_of_driving_school_kid_and_discretionary_tour,0.2287,F +coef_number_of_pre_driving_school_kid_and_discretionary_tour,0.2287,F +coef_number_of_pre_school_kid_and_discretionary_tour,0.2287,F +coef_school_accessibility_and_tour_frequency_is_1,0.6512,F +coef_school_accessibility_and_tour_frequency_is_2_plus,3.8532,F +coef_retail_accessibility_for_escorting,0.5583,F +coef_retail_accessibility_for_shopping,0.9818,F +coef_retail_accessibility_for_maintenance,0.8866,F +coef_retail_accessibility_for_eating_out,2.8733,F +coef_retail_accessibility_for_discretionary,0.5082,F +coef_1_escort_tour_constant,-0.405,F +coef_2_plus_escort_tours_constant,-0.688,F +coef_1_shopping_tours_constant,1.708,F +coef_2_plus_shopping_tours_constant,0,T +coef_1_maintenance_tours_constant,-1.042,F +coef_2_plus_maintenance_tours_constant,0,T +coef_1_plus_eating_out_tours_constant,-3.915,F +coef_1_plus_visting_tours_constant,-0.653,F +coef_1_other_discretionary_tours_constant,-1.095,F +coef_2_plus_other_discretionary_tours_constant,-2.257,F +coef_1_escort_tour_constant_tm2,-0.491,F +coef_2_plus_escort_tours_constant_tm2,-0.491,F +coef_1_shopping_tours_constant_tm2,0.246,F +coef_2_plus_shopping_tours_constant_tm2,0.246,F +coef_1_maintenance_tours_constant_tm2,1.445,F +coef_2_plus_maintenance_tours_constant_tm2,1.445,F +coef_1_plus_eating_out_tours_constant_tm2,0.005,F +coef_1_plus_visting_tours_constant_tm2,-2.381,F +coef_1_other_discretionary_tours_constant_tm2,0.614,F +coef_2_plus_other_discretionary_tours_constant_tm2,0.614,F +coef_mandatory_dap_and_1_non_mand_tour,2.692763095,F +coef_mandatory_dap_and_2_non_mand_tour,5.492779247,F +coef_mandatory_dap_and_3_plus_non_mand_tour,1.891501612,F +coef_non_mandatory_dap_and_1_non_mand_tour,4.002447263,F +coef_non_mandatory_dap_and_2_non_mand_tour,2.426047336,F +coef_non_mandatory_dap_and_3_non_mand_tour,-5.579294246,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,-3.501958214,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv new file mode 100644 index 000000000..8c8809347 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv @@ -0,0 +1,81 @@ +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_escorting_tour,-3.151460752,F +coef_discretionary_tour,-4.548188518,F +coef_shopping_tour,-2.772689119,F +coef_maintenance_tour,-2.262570738,F +coef_visiting_or_social_tour,-4.212581954,F +coef_eating_out_tour,-14.69186496,F +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-0.257343536,F +coef_has_mandatory_tours_and_tour_frequency_is_2_plus,-1.072062082,F +coef_has_joint_tours_and_tour_frequency_is_1,-0.336245855,F +coef_has_joint_tours_and_tour_frequency_is_2_plus,-0.388633303,F +coef_more_than_one_escorting_tours,2.72245229,F +coef_low_income_group_and_escorting_tour,0.202123266,F +coef_medium_low_income_group_and_escorting_tour,0.202123266,F +coef_medium_high_income_group_and_escorting_tour,-0.060806947,F +coef_high_income_group_and_escorting_tour,-0.060806947,F +coef_medium_high_income_group_and_shopping_tour,0.685514685,F +coef_high_income_group_and_shopping_tour,0.702798296,F +coef_mediumhigh_income_group_and_maintenance_tour,-0.252582535,F +coef_high_income_group_and_maintenance_tour,-0.252582535,F +coef_medium_high_income_group_and_eating_out_tour,1.584951486,F +coef_high_income_group_and_eating_out_tour,1.584951486,F +coef_low_income_group_and_discretionary_tour,-0.174583696,F +coef_mediumhigh_income_group_and_discretionary_tour,0.61551534,F +coef_high_income_group_and_discretionary_tour,0.61551534,F +coef_low_income_group_and_visiting_tour,0.709390489,F +coef_medium_low_income_group_and_visiting_tour,0.709390489,F +coef_high_income_group_and_visiting_tour,-0.174583696,F +coef_female_and_escorting_tour,0.187876356,F +coef_female_and_shopping_tour,0.986977296,F +coef_female_and_maintenance_tour,0.203259744,F +coef_female_and_visit_tour,1.063972932,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1_plus,0.427064876,F +coef_number_of_full_time_worker_and_escorting_tour,-0.30107932,F +coef_number_of_part_time_worker_and_escorting_tour,-0.457600247,F +coef_number_of_non_worker_and_escorting_tour,-0.457600247,F +coef_number_of_retiree_and_escorting_tour,-0.637017887,F +coef_number_of_university_student_and_escorting_tour,0.420147909,F +coef_go_out_pre_driving_school_kid_and_escorting_tour,0.767818174,F +coef_go_out_school_kid_and_escorting_tour,0.578638577,F +coef_number_of_full_time_worker_and_shopping_tour,-0.500867474,F +coef_number_of_part_time_worker_and_shopping_tour,-0.760659628,F +coef_number_of_university_student_and_shopping_tour,-1.440951366,F +coef_number_of_full_time_worker_and_maintenance_tour,-0.540708813,F +coef_number_of_part_time_worker_and_maintenance_tour,-0.540708813,F +coef_number_of_non_worker_and_maintenance_tour,-0.540708813,F +coef_number_of_university_student_and_maintenance_tour,-1.047970628,F +coef_number_of_university_student_and_discretionary_tour,0.089391871,F +coef_school_accessibility_and_tour_frequency_is_1_plus,0.512981401,F +coef_retail_accessibility_for_discretionary,0.146060018,F +coef_walk_accessibility_for_eatout,1.002231311,F +coef_detached_household_and_discretionary,-0.330258504,F +coef_1_escort_tour_constant,0.212419359,F +coef_2_plus_escort_tours_constant,0.941880945,F +coef_1_shopping_tours_constant,0.292005818,F +coef_2_plus_shopping_tours_constant,0.365107316,F +coef_1_maintenance_tours_constant,-0.361543976,F +coef_2_plus_maintenance_tours_constant,0,T +coef_1_plus_eating_out_tours_constant,-0.297763109,F +coef_1_plus_visting_tours_constant,-0.657474419,F +coef_1_other_discretionary_tours_constant,0.941880945,F +coef_2_plus_other_discretionary_tours_constant,-0.390350079,F +coef_1_escort_tour_constant_tm2,-0.367737876,F +coef_2_plus_escort_tours_constant_tm2,-1.248352386,F +coef_1_shopping_tours_constant_tm2,-1.437353532,F +coef_2_plus_shopping_tours_constant_tm2,-1.437353532,F +coef_1_maintenance_tours_constant_tm2,-1.454556703,F +coef_2_plus_maintenance_tours_constant_tm2,-1.454556703,F +coef_1_plus_eating_out_tours_constant_tm2,1.506897479,F +coef_1_plus_visting_tours_constant_tm2,-0.716560806,F +coef_1_other_discretionary_tours_constant_tm2,-0.194927725,F +coef_2_plus_other_discretionary_tours_constant_tm2,-0.194927725,F +coef_mandatory_dap_and_1_non_mand_tour,0.775208618,F +coef_mandatory_dap_and_2_non_mand_tour,1.056946603,F +coef_mandatory_dap_and_3_plus_non_mand_tour,-5.001269397,F +coef_non_mandatory_dap_and_1_non_mand_tour,-0.396651853,F +coef_non_mandatory_dap_and_2_non_mand_tour,-0.794997137,F +coef_non_mandatory_dap_and_3_non_mand_tour,-2.141825245,F +coef_non_mandatory_dap_and_4_plus_non_mand_tour,-5.009271306,F diff --git a/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv new file mode 100644 index 000000000..830885237 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -0,0 +1,193 @@ +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.71087271,0.886861358,1 +2,0,0,1,0.640712953,0.863555773,1 +3,0,0,1,0.726456335,0.726456335,1 +4,0,0,1,0.665679012,0.903816044,1 +5,0,0,1,0.861430335,0.92284115,1 +6,0,0,1,1,1,1 +7,0,0,1,0,1,1 +8,0,0,1,0.754632385,0.918132897,1 +1,1,0,1,0.824975545,1,1 +2,1,0,1,0.881285721,0.964131273,1 +3,1,0,1,0.856158692,1,1 +4,1,0,1,1,1,1 +5,1,0,1,1,1,1 +6,1,0,1,1,1,1 +7,1,0,1,1,1,1 +8,1,0,1,1,1,1 +1,0,0,2,0.918802841,1,1 +2,0,0,2,1,1,1 +3,0,0,2,1,1,1 +4,0,0,2,1,1,1 +5,0,0,2,0.911351921,0.964395496,1 +6,0,0,2,1,1,1 +7,0,0,2,1,1,1 +8,0,0,2,0.91707294,1,1 +1,1,0,2,1,1,1 +2,1,0,2,1,1,1 +3,1,0,2,1,1,1 +4,1,0,2,1,1,1 +5,1,0,2,1,1,1 +6,1,0,2,1,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,0,3,0.885080064,0.942540032,1 +2,0,0,3,0.832615585,1,1 +3,0,0,3,1,1,1 +4,0,0,3,1,1,1 +5,0,0,3,0.874364173,0.972270724,1 +6,0,0,3,1,1,1 +7,0,0,3,1,1,1 +8,0,0,3,1,1,1 +1,1,0,3,1,1,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,1,1,1 +5,1,0,3,1,1,1 +6,1,0,3,1,1,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,0,4,1,1,1 +2,0,0,4,0.99296599,1,1 +3,0,0,4,1,1,1 +4,0,0,4,0.976072814,1,1 +5,0,0,4,0.956476086,0.993572522,1 +6,0,0,4,1,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.991144007,1,1 +1,1,0,4,0.99740908,1,1 +2,1,0,4,0.939184617,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.749851433,1,1 +5,1,0,4,1,1,1 +6,1,0,4,1,1,1 +7,1,0,4,1,1,1 +8,1,0,4,0.949874016,1,1 +1,0,0,5,1,1,1 +2,0,0,5,1,1,1 +3,0,0,5,1,1,1 +4,0,0,5,0.954541082,1,1 +5,0,0,5,0.955216408,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.931183243,1,1 +1,1,0,5,0.970742777,1,1 +2,1,0,5,0.882901912,1,1 +3,1,0,5,1,1,1 +4,1,0,5,0.334132623,1,1 +5,1,0,5,1,1,1 +6,1,0,5,0.934804611,0.934804611,1 +7,1,0,5,0.965932685,1,1 +8,1,0,5,1,1,1 +1,0,0,6,1,1,1 +2,0,0,6,1,1,1 +3,0,0,6,1,1,1 +4,0,0,6,0.86514867,1,1 +5,0,0,6,0.896321835,1,1 +6,0,0,6,1,1,1 +7,0,0,6,1,1,1 +8,0,0,6,1,1,1 +1,1,0,6,1,1,1 +2,1,0,6,1,1,1 +3,1,0,6,1,1,1 +4,1,0,6,1,1,1 +5,1,0,6,1,1,1 +6,1,0,6,1,1,1 +7,1,0,6,1,1,1 +8,1,0,6,1,1,1 +1,0,1,1,1,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,1,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,1,2,1,1,1 +2,0,1,2,1,1,1 +3,0,1,2,1,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,1,1,1 +7,1,1,2,1,1,1 +8,1,1,2,1,1,1 +1,0,1,3,1,1,1 +2,0,1,3,1,1,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,1,1,1 +7,0,1,3,1,1,1 +8,0,1,3,1,1,1 +1,1,1,3,1,1,1 +2,1,1,3,1,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,1,1,1 +6,1,1,3,1,1,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,1,4,1,1,1 +2,0,1,4,1,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,1,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,1,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,1,5,1,1,1 +2,0,1,5,1,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,1,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,1,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,1,6,1,1,1 +2,0,1,6,1,1,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,1,1,1 +6,0,1,6,1,1,1 +7,0,1,6,1,1,1 +8,0,1,6,1,1,1 +1,1,1,6,1,1,1 +2,1,1,6,1,1,1 +3,1,1,6,1,1,1 +4,1,1,6,1,1,1 +5,1,1,6,1,1,1 +6,1,1,6,1,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/test/non_mandatory_tour_frequency/configs/settings.yaml b/test/non_mandatory_tour_frequency/configs/settings.yaml new file mode 100644 index 000000000..4136d0b80 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/settings.yaml @@ -0,0 +1,60 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + unique_hh_id: household_id + NP: hhsize + hh_workers_from_esr: num_workers + VEH: auto_ownership + MAZ: home_zone_id + HINCP: income + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + unique_hh_id: household_id + AGEP: age + SPORDER: PNUM + SEX: sex + employed: pemploy + student_status: pstudent + person_type: ptype + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ_ORIGINAL: zone_id + CountyID: county_id + TAZ_ORIGINAL: TAZ + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + +trace_hh_id: \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv b/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 000000000..bddab06b9 --- /dev/null +++ b/test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/test/non_mandatory_tour_frequency/data/.gitkeep b/test/non_mandatory_tour_frequency/data/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py b/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py new file mode 100644 index 000000000..1563de756 --- /dev/null +++ b/test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py @@ -0,0 +1,336 @@ +import logging +import pytest +import os +import shutil +import pandas as pd +import numpy as np +from numpy import dot +from numpy.linalg import norm + +# import models is necessary to initalize the model steps with orca +from activitysim.abm import models +from activitysim.core import pipeline, config +from activitysim.core import tracing + +logger = logging.getLogger(__name__) + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return "non_mandatory_tour_frequency" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables(prepare_module_inputs) -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "MAZ_ORIGINAL", + "persons": "person_id", + "households": "household_id", + "accessibility": "MAZ_ORIGINAL", + "tours": "tour_id", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return False + + +@pytest.fixture(scope="module") +def load_checkpoint() -> bool: + """ + checkpoint to be loaded from the pipeline when reconnecting. + """ + return "initialize_households" + + +# make a reconnect_pipeline internal to test module +@pytest.mark.skipif( + os.path.isfile("test/non_mandatory_tour_frequency/output/pipeline.h5"), + reason="no need to recreate pipeline store if alreayd exist", +) +def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["initialize_landuse", "initialize_households"]) + + # get the updated pipeline data + person_df = pipeline.get_table("persons") + person_df.to_csv("test/non_mandatory_tour_frequency/output/person.csv", index=False) + + # get the updated pipeline data + household_df = pipeline.get_table("households") + household_df.to_csv( + "test/non_mandatory_tour_frequency/output/household.csv", index=False + ) + + pipeline.close_pipeline() + + +def test_nmtf_from_pipeline(reconnect_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run( + models=["non_mandatory_tour_frequency"], resume_after="initialize_households" + ) + + # get the updated pipeline data + person_df = pipeline.get_table("persons") + + # get the updated pipeline data + household_df = pipeline.get_table("households") + + ############################ + # person nmtf validation + ############################ + # nmtf person result from the model + logger.info("person nmtf pattern validation") + + target_key = "inmf_choice" + simulated_key = "non_mandatory_tour_frequency" + similarity_threshold = 0.99 + + simulated_df = create_summary( + person_df, key=simulated_key, out_col="Simulated_Share" + ) + + # result from the TM2 run + target_df = create_summary(person_df, key=target_key, out_col="Target_Share") + + # compare simulated and target results + similarity_value = compare_simulated_against_target( + target_df, simulated_df, target_key, simulated_key + ) + + # if the cosine_similarity >= threshold then the simulated and target results are "similar" + assert similarity_value >= similarity_threshold + + +# fetch/prepare existing files for model inputs +# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim +@pytest.fixture(scope="module") +def prepare_module_inputs() -> None: + """ + copy input files from sharepoint into test folder + + create unique person id in person file + + :return: None + """ + # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/ + # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1 + test_dir = os.path.join("test", "non_mandatory_tour_frequency", "data") + + accessibility_file = os.path.join(test_dir, "tm2_outputs", "accessibilities.csv") + household_file = os.path.join(test_dir, "popsyn", "households.csv") + person_file = os.path.join(test_dir, "popsyn", "persons.csv") + landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv") + + shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv")) + shutil.copy(household_file, os.path.join(test_dir, "households.csv")) + shutil.copy(person_file, os.path.join(test_dir, "persons.csv")) + shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv")) + + # add original maz id to accessibility table + land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv")) + accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv")) + + accessibility_df = pd.merge( + accessibility_df, + land_use_df[["MAZ", "MAZ_ORIGINAL"]].rename(columns={"MAZ": "mgra"}), + how="left", + on="mgra", + ) + + accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False) + + # currently household file has to have these two columns, even before annotation + # because annotate person happens before household and uses these two columns + # TODO find a way to get around this + #### + household_df = pd.read_csv(os.path.join(test_dir, "households.csv")) + + household_columns_dict = {"HHID": "household_id", "MAZ": "home_zone_id"} + + household_df.rename(columns=household_columns_dict, inplace=True) + + tm2_simulated_household_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "householdData_1.csv") + ) + tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + household_df = pd.merge( + household_df, + tm2_simulated_household_df[ + [ + "household_id", + "autos", + "automated_vehicles", + "transponder", + "cdap_pattern", + "jtf_choice", + ] + ], + how="inner", # tm2 is not 100% sample run + on="household_id", + ) + + household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False) + + person_df = pd.read_csv(os.path.join(test_dir, "persons.csv")) + + person_columns_dict = {"HHID": "household_id", "PERID": "person_id"} + + person_df.rename(columns=person_columns_dict, inplace=True) + + tm2_simulated_person_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "personData_1.csv") + ) + tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + person_df = pd.merge( + person_df, + tm2_simulated_person_df[ + [ + "household_id", + "person_id", + "person_num", + "type", + "value_of_time", + "activity_pattern", + "imf_choice", + "inmf_choice", + "fp_choice", + "reimb_pct", + "workDCLogsum", + "schoolDCLogsum", + ] + ], + how="inner", # tm2 is not 100% sample run + on=["household_id", "person_id"], + ) + + # get tm2 simulated workplace and school location results + tm2_simulated_wsloc_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "wsLocResults_1.csv") + ) + tm2_simulated_wsloc_df.rename( + columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True + ) + + person_df = pd.merge( + person_df, + tm2_simulated_wsloc_df[ + [ + "household_id", + "person_id", + "WorkLocation", + "WorkLocationLogsum", # this is the same as `workDCLogsum` in tm2 person output + "SchoolLocation", + "SchoolLocationLogsum", # this is the same as `schoolDCLogsum` in tm2 person output + ] + ], + how="inner", # ctramp might not be 100% sample run + on=["household_id", "person_id"], + ) + + person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False) + + ## get tour data from tn2 output + + tm2_simulated_indiv_tour_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv") + ) + tm2_simulated_joint_tour_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "jointTourData_1.csv") + ) + + tm2_simulated_tour_df = pd.concat( + [tm2_simulated_indiv_tour_df, tm2_simulated_joint_tour_df], + sort=False, + ignore_index=True, + ) + + tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}).to_csv( + os.path.join(test_dir, "tours.csv"), index=False + ) + + #### + + +def create_summary(input_df, key, out_col="Share") -> pd.DataFrame: + """ + Create summary for the input data. + 1. group input data by the "key" column + 2. calculate the percent of input data records in each "key" category. + + :return: pd.DataFrame + """ + + out_df = input_df.groupby(key).size().reset_index(name="Count") + out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4) + + return out_df[[key, out_col]] + + +def cosine_similarity(a, b): + """ + Computes cosine similarity between two vectors. + + Cosine similarity is used here as a metric to measure similarity between two sequence of numbers. + Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them + i.e., dot products of the vectors divided by the product of their lengths. + + :return: + """ + + return dot(a, b) / (norm(a) * norm(b)) + + +def compare_simulated_against_target( + target_df: pd.DataFrame, + simulated_df: pd.DataFrame, + target_key: str, + simulated_key: str, +) -> bool: + """ + compares the simulated and target results by computing the cosine similarity between them. + + :return: + """ + + merged_df = pd.merge( + target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer" + ) + merged_df = merged_df.fillna(0) + + logger.info("simulated vs target share:\n%s" % merged_df) + + similarity_value = cosine_similarity( + merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist() + ) + + logger.info("cosine similarity:\n%s" % similarity_value) + + return similarity_value diff --git a/test/parking_location/.gitkeep b/test/parking_location/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/test/parking_location/compare-parking-location-results.R b/test/parking_location/compare-parking-location-results.R new file mode 100644 index 000000000..22c607f8d --- /dev/null +++ b/test/parking_location/compare-parking-location-results.R @@ -0,0 +1,62 @@ +library(tidyverse) +library(ggplot2) + +trips_df <- read_csv("./output/trips_after_parking_choice.csv") + +walk_dist_df <- read_csv("./data/ped_distance_maz_maz.csv") + +landuse_df <- read_csv("./data/land_use.csv") + +landuse_df <- landuse_df %>% + select(maz, maz_county_based, TAZ_ORIGINAL) + +work_df <- trips_df %>% + rename(parking_maz_target = parking_mgra) %>% + rename(parking_maz_simulated = parking_zone) %>% + select(trip_id, household_id, person_id, person_num, stop_id, tour_purpose, orig_purpose, dest_purpose, orig_maz = origin, dest_maz = destination, + activity_duration_in_hours, parking_maz_target, parking_maz_simulated) %>% + left_join(landuse_df, by = c("orig_maz" = "maz")) %>% + rename(orig_maz_county_based = maz_county_based, orig_taz = TAZ_ORIGINAL) %>% + left_join(landuse_df, by = c("dest_maz" = "maz")) %>% + rename(dest_maz_county_based = maz_county_based, dest_taz = TAZ_ORIGINAL) %>% + left_join(landuse_df, by = c("parking_maz_target" = "maz")) %>% + rename(parking_maz_county_based_target = maz_county_based, parking_taz_target = TAZ_ORIGINAL) %>% + left_join(landuse_df, by = c("parking_maz_simulated" = "maz")) %>% + rename(parking_maz_county_based_simulated = maz_county_based, parking_taz_simulated = TAZ_ORIGINAL) %>% + left_join(walk_dist_df, by = c("parking_maz_simulated" = "OMAZ", "dest_maz" = "DMAZ")) %>% + rename(parking_distance_simulated = DISTWALK) %>% + left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "dest_maz" = "DMAZ")) %>% + rename(parking_distance_target = DISTWALK) %>% + left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "parking_maz_simulated" = "DMAZ")) %>% + rename(distance_target_simulation = DISTWALK) %>% + select(trip_id, household_id, person_id, stop_id, tour_purpose, orig_purpose, dest_purpose, + orig_maz, dest_maz, orig_maz_county_based, dest_maz_county_based, orig_taz, dest_taz, + parking_maz_target, parking_maz_simulated, parking_taz_target, parking_taz_simulated, + parking_distance_target, parking_distance_simulated, distance_target_simulation) + + +write.csv(work_df, "./output/parking-location-choice-results.csv", row.names = F) + + +summary_df <- work_df %>% + group_by(dest_purpose) %>% + summarise( + trips_count = n(), + min_distance_target = round(min(parking_distance_target, na.rm = T), 3), + min_distance_simulated = round(min(parking_distance_simulated, na.rm = T), 3), + max_distance_target = round(max(parking_distance_target, na.rm = T), 3), + max_distance_simulated = round(max(parking_distance_simulated, na.rm = T), 3), + mean_distance_target = round(mean(parking_distance_target, na.rm = T), 3), + mean_distance_simulated = round(mean(parking_distance_simulated, na.rm = T), 3), + median_distance_target = round(median(parking_distance_target, na.rm = T), 3), + median_distance_simulated = round(median(parking_distance_simulated, na.rm = T), 3) + ) + +#ggplot(work_df, aes(x=distance_target_simulation)) + geom_histogram(binwidth=0.025, color="black", fill="white") + +#ggplot(work_df, aes(x=parking_distance_target)) + geom_histogram(binwidth=0.05, color="black", fill="white") + +#ggplot(work_df, aes(x=parking_distance_simulated)) + geom_histogram(binwidth=0.05, color="black", fill="white") + + + diff --git a/test/parking_location/configs/ParkLocationChoice.xls b/test/parking_location/configs/ParkLocationChoice.xls new file mode 100644 index 000000000..3ea952910 Binary files /dev/null and b/test/parking_location/configs/ParkLocationChoice.xls differ diff --git a/test/parking_location/configs/annotate_households.csv b/test/parking_location/configs/annotate_households.csv new file mode 100644 index 000000000..ac21d3797 --- /dev/null +++ b/test/parking_location/configs/annotate_households.csv @@ -0,0 +1,13 @@ +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +number of fulltime workers,num_full_time_workers,"_PERSON_COUNT('ptype == 1', persons, households)" +number of parttime workers,num_part_time_workers,"_PERSON_COUNT('ptype == 2', persons, households)" +number of university student,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)" +number of non-workers,num_non_workers,"_PERSON_COUNT('ptype == 4', persons, households)" +number of retirees,num_retirees,"_PERSON_COUNT('ptype == 5', persons, households)" +number of driving age students,num_driving_age_students,"_PERSON_COUNT('ptype == 6', persons, households)" +number of pre-driving age school kids,num_pre_driving_age_school_kids,"_PERSON_COUNT('ptype == 7', persons, households)" +number of pre-school kids,num_pre_school_kids,"_PERSON_COUNT('ptype == 8', persons, households)" +number of pre-driving age school kids who go out,num_pre_driving_age_school_kids_go_out,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)" +number of pre-school kids who go out,num_pre_school_kids_go_out,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)" \ No newline at end of file diff --git a/test/parking_location/configs/annotate_landuse.csv b/test/parking_location/configs/annotate_landuse.csv new file mode 100644 index 000000000..76c722914 --- /dev/null +++ b/test/parking_location/configs/annotate_landuse.csv @@ -0,0 +1,7 @@ +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / land_use.TOTACRE +employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE +population_density,population_density,land_use.TOTPOP / land_use.TOTACRE +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +is_parking_zone,is_parking_zone,"np.where(land_use.parkarea==1,True,False)" \ No newline at end of file diff --git a/test/parking_location/configs/annotate_persons.csv b/test/parking_location/configs/annotate_persons.csv new file mode 100644 index 000000000..eff11ab68 --- /dev/null +++ b/test/parking_location/configs/annotate_persons.csv @@ -0,0 +1,16 @@ +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +,ptype,0 +,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)" +,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)" +,ptype,"np.where(persons.type == 'University student', 3, ptype)" +,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)" +,ptype,"np.where(persons.type == 'Retired', 5, ptype)" +,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)" +,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)" +,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)" \ No newline at end of file diff --git a/test/parking_location/configs/annotate_persons_after_hh.csv b/test/parking_location/configs/annotate_persons_after_hh.csv new file mode 100644 index 000000000..0dfa16be6 --- /dev/null +++ b/test/parking_location/configs/annotate_persons_after_hh.csv @@ -0,0 +1,5 @@ +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" \ No newline at end of file diff --git a/test/parking_location/configs/initialize_households.yaml b/test/parking_location/configs/initialize_households.yaml new file mode 100644 index 000000000..15891431e --- /dev/null +++ b/test/parking_location/configs/initialize_households.yaml @@ -0,0 +1,37 @@ +annotate_tables: + - tablename: persons + column_map: + HHID: household_id + PERID: person_id + AGEP: age + SEX: sex + SCHL: education_attainment + OCCP: occupation + WKHP: hours_worked + WKW: weeks_worked + EMPLOYED: employment_status + ESR: esr + SCHG: grade_attending + activity_pattern: cdap_activity + annotate: + SPEC: annotate_persons + DF: persons + TABLES: + - households + - tablename: households + column_map: + HHID: household_id + HHINCADJ: income + NWRKRS_ESR: num_workers + VEH: auto_ownership + NP: hhsize + HHT: hh_type + BLD: building_size + TYPE: hh_unit_type + MTCCountyID: county_id + annotate: + SPEC: annotate_households + DF: households + TABLES: + - persons + - land_use \ No newline at end of file diff --git a/test/parking_location/configs/initialize_landuse.yaml b/test/parking_location/configs/initialize_landuse.yaml new file mode 100644 index 000000000..d30ddb245 --- /dev/null +++ b/test/parking_location/configs/initialize_landuse.yaml @@ -0,0 +1,52 @@ +annotate_tables: + - tablename: land_use + column_map: + CountyID: county_id + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + annotate: + SPEC: annotate_landuse + DF: land_use + - tablename: accessibility + column_map: + column_1: nonmandatory_auto_accessibility + column_2: nonmandatory_transit_accessibility + column_3: nonmandatory_nm_accessibility + column_4: nonmandatory_sov0_accessibility + column_5: nonmandatory_sov1_accessibility + column_6: nonmandatory_sov2_accessibility + column_7: nonmandatory_hov0_accessibility + column_8: nonmandatory_hov1_accessibility + column_9: nonmandatory_hov2_accessibility + column_10: shop_hov_insufficient_accessibility + column_11: shop_hov_sufficient_accessibility + column_12: shop_hov_oversufficient_accessibility + column_13: maint_hov_insufficient_accessibility + column_14: maint_hov_sufficient_accessibility + column_15: maint_hov_oversufficient_accessibility + column_16: eat_hov_insufficient_accessibility + column_17: eat_hov_sufficient_accessibility + column_18: eat_hov_oversufficient_accessibility + column_19: visit_hov_insufficient_accessibility + column_20: visit_hov_sufficient_accessibility + column_21: visit_hov_oversufficient_accessibility + column_22: discr_hov_insufficient_accessibility + column_23: discr_hov_sufficient_accessibility + column_24: discr_hov_oversufficient_accessibility + column_25: escort_hov_insufficient_accessibility + column_26: escort_hov_sufficient_accessibility + column_27: escort_hov_oversufficient_accessibility + column_28: shop_sov_insufficient_accessibility + column_29: shop_sov_sufficient_accessibility + column_30: shop_sov_oversufficient_accessibility + column_31: maint_sov_insufficient_accessibility + column_32: maint_sov_sufficient_accessibility + column_33: maint_sov_oversufficient_accessibility + column_40: discr_sov_insufficient_accessibility + column_41: discr_sov_sufficient_accessibility + column_42: discr_sov_oversufficient_accessibility + column_45: total_emp_accessibility + column_47: hh_walktransit_accessibility \ No newline at end of file diff --git a/test/parking_location/configs/network_los.yaml b/test/parking_location/configs/network_los.yaml new file mode 100644 index 000000000..5603ab859 --- /dev/null +++ b/test/parking_location/configs/network_los.yaml @@ -0,0 +1,24 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: False + +zone_system: 2 + +taz_skims: skims.omx + +maz: maz.csv + +maz_to_maz: + tables: + - ped_distance_maz_maz.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DISTWALK: 0 + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/test/parking_location/configs/parking_location_choice.csv b/test/parking_location/configs/parking_location_choice.csv new file mode 100644 index 000000000..d9a10c39d --- /dev/null +++ b/test/parking_location/configs/parking_location_choice.csv @@ -0,0 +1,34 @@ +Description,Expression,no_segmentation +Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1 +Trip is work purpose,_work_trip@(df.purpose == 'work'),1 +Number of hourly stalls,_hourly_stalls@df.hstallsoth,1 +Number of daily stalls,_daily_stalls@df.dstallsoth,1 +Number of monthly stalls,_monthly_stalls@df.mstallsoth,1 +Hourly stalls available,_hourly_stalls_available@_hourly_stalls > 0,1 +Daily stalls available,_daily_stalls_available@_daily_stalls > 0,1 +Monthly stalls available,_monthly_stalls_available@_monthly_stalls > 0,1 +Hourly parking cost cents,_hourly_parking_cost@df.hparkcost * 100,1 +Number of free parking hours,_num_free_hours@df.numfreehrs,1 +Daily parking cost cents,_daily_parking_cost@df.dparkcost * 100,1 +Monthly parking cost cents,_monthly_parking_cost@df.mparkcost * 100,1 +Monthly parking is allowed,_monthly_allowed@_monthly_stalls_available & _work_trip & _person_is_worker,1 +Total hourly parking cost,_total_hourly_parking_cost@(_hourly_parking_cost * (df.activity_duration - _num_free_hours)),1 +Hourly is cheaper than daily,"_hourly_cheaper_than_daily@np.where(_daily_stalls_available, _total_hourly_parking_cost < _daily_parking_cost,True)",1 +Hourly is cheaper than monthly,"_hourly_cheaper_than_monthly@np.where(_monthly_allowed, _total_hourly_parking_cost < _monthly_parking_cost,True)",1 +Hourly parking is best,_hourly_best@(_hourly_stalls_available & _hourly_cheaper_than_daily & _hourly_cheaper_than_monthly),1 +Daily is cheaper than hourly,"_daily_cheaper_than_hourly@np.where(_hourly_stalls_available, _daily_parking_cost < _total_hourly_parking_cost,True)",1 +Daily is cheaper than monthly,"_daily_cheaper_than_monthly@np.where(_monthly_allowed, _daily_parking_cost < _monthly_parking_cost,True)",1 +Daily parking is best,_daily_best@(_daily_stalls_available & _daily_cheaper_than_hourly & _daily_cheaper_than_monthly),1 +Monthly is cheaper than hourly,"_monthly_cheaper_than_hourly@np.where(_hourly_stalls_available, _monthly_parking_cost < _total_hourly_parking_cost,True)",1 +Monthly is cheaper than daily,"_monthly_cheaper_than_daily@np.where(_daily_stalls_available, _monthly_parking_cost < _daily_stalls_available,True)",1 +Monthly parking is best,_monthly_best@(_monthly_allowed & _monthly_cheaper_than_hourly & _monthly_cheaper_than_daily),1 +Parking size,_parking_size@(_hourly_best * _hourly_stalls) + (_daily_best * _daily_stalls) + (_monthly_best * _monthly_stalls),1 +Parking cost,_parking_cost@(_hourly_best * _total_hourly_parking_cost) + (_daily_best * _daily_parking_cost) + (_monthly_best * _monthly_parking_cost),1 +Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1 +# actual utility terms starts from here +Log of parking size,"@np.where(_parking_size>0,np.log(_parking_size),-9999)",1 +Cost work trip,"@np.where(df.purpose == 'work', _parking_cost, 0)",coef_cost_work_trip +Cost other trip,"@np.where(df.purpose != 'work', _parking_cost, 0)",coef_cost_other_trip +Walk distance,@_walk_distance,coef_walk_distance +Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable +Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable \ No newline at end of file diff --git a/test/parking_location/configs/parking_location_choice.yaml b/test/parking_location/configs/parking_location_choice.yaml new file mode 100644 index 000000000..e7992c7e6 --- /dev/null +++ b/test/parking_location/configs/parking_location_choice.yaml @@ -0,0 +1,22 @@ +SPECIFICATION: parking_location_choice.csv + +COEFFICIENTS: parking_location_choice_coeffs.csv + +PREPROCESSOR: + SPEC: parking_location_choice_annotate_trips_preprocessor + DF: trips_merged + TABLES: + - land_use + - persons + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_park_eligible +CHOOSER_SEGMENT_COLUMN_NAME: parking_segment + +ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone +ALT_DEST_COL_NAME: parking_zone + +TRIP_DEPARTURE_PERIOD: 'stop_period' + +TRIP_ORIGIN: origin +TRIP_DESTINATION: destination diff --git a/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv b/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv new file mode 100644 index 000000000..a49f48b12 --- /dev/null +++ b/test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv @@ -0,0 +1,6 @@ +Description,Target,Expression +Person Type,ptype,"reindex(persons.ptype, df.person_id)" +Trip mode is drive,drive_trip,df.trip_mode <= 8 +Parking segment,parking_segment,setting('segment') +Parking eligible trip,is_park_eligible,(drive_trip & (df.purpose != "home")) +Activity duration,activity_duration,df.activity_duration_in_hours diff --git a/test/parking_location/configs/parking_location_choice_coeffs.csv b/test/parking_location/configs/parking_location_choice_coeffs.csv new file mode 100644 index 000000000..0edccfbf1 --- /dev/null +++ b/test/parking_location/configs/parking_location_choice_coeffs.csv @@ -0,0 +1,5 @@ +coefficient_name,value,constrain +coef_unavailable,-9999,T +coef_walk_distance,-11.8,F +coef_cost_work_trip,-0.0072,F +coef_cost_other_trip,-0.0041,F diff --git a/test/parking_location/configs/settings.yaml b/test/parking_location/configs/settings.yaml new file mode 100644 index 000000000..1cd739cb4 --- /dev/null +++ b/test/parking_location/configs/settings.yaml @@ -0,0 +1,62 @@ +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + unique_hh_id: household_id + NP: hhsize + hh_workers_from_esr: num_workers + VEH: auto_ownership + MAZ: home_zone_id + HINCP: income + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + unique_hh_id: household_id + AGEP: age + SPORDER: PNUM + SEX: sex + employed: pemploy + student_status: pstudent + person_type: ptype + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ_ORIGINAL: zone_id + CountyID: county_id + TAZ_ORIGINAL: TAZ + DistID: DISTRICT + HH: TOTHH + POP: TOTPOP + ACRES: TOTACRE + emp_total: TOTEMP + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + +trace_hh_id: 5924 + +segment: "no_segmentation" \ No newline at end of file diff --git a/test/parking_location/configs/tour_departure_and_duration_alternatives.csv b/test/parking_location/configs/tour_departure_and_duration_alternatives.csv new file mode 100644 index 000000000..bddab06b9 --- /dev/null +++ b/test/parking_location/configs/tour_departure_and_duration_alternatives.csv @@ -0,0 +1,191 @@ +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 +23,23 \ No newline at end of file diff --git a/test/parking_location/notebook/parking-location-results-summary.twbx b/test/parking_location/notebook/parking-location-results-summary.twbx new file mode 100644 index 000000000..fa837b590 Binary files /dev/null and b/test/parking_location/notebook/parking-location-results-summary.twbx differ diff --git a/test/parking_location/test_parking_location.py b/test/parking_location/test_parking_location.py new file mode 100644 index 000000000..6859c93c4 --- /dev/null +++ b/test/parking_location/test_parking_location.py @@ -0,0 +1,458 @@ +import logging +import pytest +import os +import shutil +import pandas as pd +import numpy as np +from numpy import dot +from numpy.linalg import norm +import orca + +# import models is necessary to initalize the model steps with orca +from activitysim.abm import models +from activitysim.core import pipeline, config +from activitysim.core import tracing + +from activitysim.core import simulate +from activitysim.core import config +from activitysim.abm.models.util import estimation +import yaml + +logger = logging.getLogger(__name__) + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return "parking_location" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables(prepare_module_inputs) -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "maz", + "persons": "person_id", + "households": "household_id", + "accessibility": "maz", + "tours": "tour_id", + "trips": "trip_id", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: boolcls + """ + return True + + +@pytest.fixture(scope="module") +def load_checkpoint() -> bool: + """ + checkpoint to be loaded from the pipeline when reconnecting. + """ + return "initialize_households" + + +# make a reconnect_pipeline internal to test module +@pytest.mark.skipif( + os.path.isfile("test/parking_location/output/pipeline.h5"), + reason="no need to recreate pipeline store if already exist", +) +def test_prepare_input_pipeline(initialize_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["initialize_landuse", "initialize_households"]) + + # save the updated pipeline tables + person_df = pipeline.get_table("persons") + person_df.to_csv("test/parking_location/output/person.csv") + + household_df = pipeline.get_table("households") + household_df.to_csv("test/parking_location/output/household.csv") + + land_use_df = pipeline.get_table("land_use") + land_use_df.to_csv("test/parking_location/output/land_use.csv") + + accessibility_df = pipeline.get_table("accessibility") + accessibility_df.to_csv("test/parking_location/output/accessibility.csv") + + tours_df = pipeline.get_table("tours") + tours_df.to_csv("test/parking_location/output/tours.csv") + + trips_df = pipeline.get_table("trips") + trips_df.to_csv("test/parking_location/output/trips.csv") + + pipeline.close_pipeline() + + +# @pytest.mark.skip +def test_parking_location(reconnect_pipeline: pipeline.Pipeline, caplog): + # Run summarize model + caplog.set_level(logging.INFO) + + # run model step + pipeline.run(models=["parking_location"], resume_after="initialize_households") + + # get the updated trips data + trips_df = pipeline.get_table("trips") + trips_df.to_csv("test/parking_location/output/trips_after_parking_choice.csv") + + +# fetch/prepare existing files for model inputs +# e.g. read accessibilities.csv from ctramp result, rename columns, write out to accessibility.csv which is the input to activitysim +@pytest.fixture(scope="module") +def prepare_module_inputs() -> None: + """ + + copy input files from sharepoint into test folder + + create unique person id in person file + + :return: None + """ + # https://wsponlinenam.sharepoint.com/sites/US-TM2ConversionProject/Shared%20Documents/Forms/ + # AllItems.aspx?id=%2Fsites%2FUS%2DTM2ConversionProject%2FShared%20Documents%2FTask%203%20ActivitySim&viewid=7a1eaca7%2D3999%2D4d45%2D9701%2D9943cc3d6ab1 + test_dir = os.path.join("test", "parking_location", "data") + + accessibility_file = os.path.join( + "test", "parking_location", "data", "accessibilities.csv" + ) + household_file = os.path.join(test_dir, "popsyn", "households.csv") + person_file = os.path.join(test_dir, "popsyn", "persons.csv") + landuse_file = os.path.join(test_dir, "landuse", "maz_data_withDensity.csv") + + shutil.copy(accessibility_file, os.path.join(test_dir, "accessibility.csv")) + shutil.copy(household_file, os.path.join(test_dir, "households.csv")) + shutil.copy(person_file, os.path.join(test_dir, "persons.csv")) + shutil.copy(landuse_file, os.path.join(test_dir, "land_use.csv")) + + # add original maz id to accessibility table + land_use_df = pd.read_csv(os.path.join(test_dir, "land_use.csv")) + + land_use_df.rename( + columns={"MAZ": "maz", "MAZ_ORIGINAL": "maz_county_based"}, inplace=True + ) + + land_use_df.to_csv(os.path.join(test_dir, "land_use.csv"), index=False) + + accessibility_df = pd.read_csv(os.path.join(test_dir, "accessibility.csv")) + + accessibility_df["maz"] = accessibility_df["mgra"] + + accessibility_df.to_csv(os.path.join(test_dir, "accessibility.csv"), index=False) + + # currently household file has to have these two columns, even before annotation + # because annotate person happens before household and uses these two columns + # TODO find a way to get around this + #### + household_df = pd.read_csv(os.path.join(test_dir, "households.csv")) + + household_columns_dict = { + "HHID": "household_id", + "TAZ": "taz", + "MAZ": "maz_county_based", + } + + household_df.rename(columns=household_columns_dict, inplace=True) + + tm2_simulated_household_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "householdData_1.csv") + ) + tm2_simulated_household_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + household_df = pd.merge( + household_df, + tm2_simulated_household_df[ + [ + "household_id", + "autos", + "automated_vehicles", + "transponder", + "cdap_pattern", + "jtf_choice", + ] + ], + how="inner", # tm2 is not 100% sample run + on="household_id", + ) + + household_df.to_csv(os.path.join(test_dir, "households.csv"), index=False) + + person_df = pd.read_csv(os.path.join(test_dir, "persons.csv")) + + person_columns_dict = {"HHID": "household_id", "PERID": "person_id"} + + person_df.rename(columns=person_columns_dict, inplace=True) + + tm2_simulated_person_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "personData_1.csv") + ) + tm2_simulated_person_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + person_df = pd.merge( + person_df, + tm2_simulated_person_df[ + [ + "household_id", + "person_id", + "person_num", + "type", + "value_of_time", + "activity_pattern", + "imf_choice", + "inmf_choice", + "fp_choice", + "reimb_pct", + "workDCLogsum", + "schoolDCLogsum", + ] + ], + how="inner", # tm2 is not 100% sample run + on=["household_id", "person_id"], + ) + + # get tm2 simulated workplace and school location results + tm2_simulated_wsloc_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "wsLocResults_1.csv") + ) + tm2_simulated_wsloc_df.rename( + columns={"HHID": "household_id", "PersonID": "person_id"}, inplace=True + ) + + person_df = pd.merge( + person_df, + tm2_simulated_wsloc_df[ + [ + "household_id", + "person_id", + "WorkLocation", + "WorkLocationLogsum", # this is the same as `workDCLogsum` in tm2 person output + "SchoolLocation", + "SchoolLocationLogsum", # this is the same as `schoolDCLogsum` in tm2 person output + ] + ], + how="inner", # ctramp might not be 100% sample run + on=["household_id", "person_id"], + ) + + person_df.to_csv(os.path.join(test_dir, "persons.csv"), index=False) + + ## get tour data from tm2 output + + tm2_simulated_indiv_tour_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "indivTourData_1.csv") + ) + tm2_simulated_joint_tour_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "jointTourData_1.csv") + ) + + tm2_simulated_tour_df = pd.concat( + [tm2_simulated_indiv_tour_df, tm2_simulated_joint_tour_df], + sort=False, + ignore_index=True, + ) + + tm2_simulated_tour_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + tm2_simulated_tour_df["unique_tour_id"] = range(1, len(tm2_simulated_tour_df) + 1) + + ## get trip data from tm2 output + tm2_simulated_indiv_trip_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "indivTripData_1.csv") + ) + tm2_simulated_joint_trip_df = pd.read_csv( + os.path.join(test_dir, "tm2_outputs", "jointTripData_1.csv") + ) + + tm2_simulated_trip_df = pd.concat( + [tm2_simulated_indiv_trip_df, tm2_simulated_joint_trip_df], + sort=False, + ignore_index=True, + ) + + tm2_simulated_trip_df.rename(columns={"hh_id": "household_id"}, inplace=True) + + tm2_simulated_trip_df["trip_id"] = range(1, len(tm2_simulated_trip_df) + 1) + + tm2_simulated_trip_df = pd.merge( + tm2_simulated_trip_df, + tm2_simulated_tour_df[ + [ + "household_id", + "person_id", + "tour_id", + "tour_purpose", + "unique_tour_id", + "start_period", + "end_period", + ] + ], + how="left", + on=["household_id", "person_id", "tour_id", "tour_purpose"], + ) + # drop tour id and rename unique_tour_id to tour_id + tm2_simulated_tour_df.drop(["tour_id"], axis=1, inplace=True) + tm2_simulated_tour_df.rename(columns={"unique_tour_id": "tour_id"}, inplace=True) + + tm2_simulated_trip_df.drop(["tour_id"], axis=1, inplace=True) + tm2_simulated_trip_df.rename( + columns={ + "unique_tour_id": "tour_id", + "orig_mgra": "origin", + "dest_mgra": "destination", + "start_period": "tour_start_period", + "end_period": "tour_end_period", + }, + inplace=True, + ) + + tm2_simulated_trip_df["purpose"] = tm2_simulated_trip_df["dest_purpose"].str.lower() + + period_map_df = pd.read_csv(os.path.join(test_dir, "period_mapping_mtc.csv")) + + tm2_simulated_trip_df.sort_values( + by=["household_id", "person_id", "person_num", "stop_period", "tour_id"], + inplace=True, + ) + + tm2_simulated_trip_df = pd.merge( + tm2_simulated_trip_df, + period_map_df, + left_on="stop_period", + right_on="period", + how="left", + ) + + tm2_simulated_trip_df["trip_departure_time"] = tm2_simulated_trip_df[ + "minutes_reference" + ] + tm2_simulated_trip_df["trip_arrival_time"] = ( + tm2_simulated_trip_df["trip_departure_time"] + + tm2_simulated_trip_df["TRIP_TIME"] + ) + tm2_simulated_trip_df["next_trip_departure_time"] = tm2_simulated_trip_df.groupby( + ["household_id", "person_id", "person_num"] + )["trip_arrival_time"].shift(-1) + + # activity duration is start of next trip - end of current trip + tm2_simulated_trip_df["activity_duration"] = ( + tm2_simulated_trip_df["next_trip_departure_time"] + - tm2_simulated_trip_df["trip_arrival_time"] + ) + + # set default activity duration to 5 minutes + tm2_simulated_trip_df["activity_duration"] = np.where( + tm2_simulated_trip_df["activity_duration"] > 0, + tm2_simulated_trip_df["activity_duration"], + 5, + ) + + # activity duration in hours + tm2_simulated_trip_df["activity_duration_in_hours"] = ( + tm2_simulated_trip_df["activity_duration"] / 60 + ) + + tm2_simulated_trip_df.drop( + [ + "period", + "start_time", + "end_time", + "minutes_reference", + "trip_departure_time", + "trip_arrival_time", + "next_trip_departure_time", + "activity_duration", + ], + inplace=True, + axis=1, + ) + + # randomly select 100,000 trips to run the parking location choice model + # memory constraint on `logit.interaction_dataset` for large trips table + # tm2_simulated_trip_df = tm2_simulated_trip_df.sample(100000, random_state=1) + + # select only if target parking maz is > 0 + tm2_simulated_trip_df = tm2_simulated_trip_df[ + tm2_simulated_trip_df["parking_mgra"] > 0 + ] + + tm2_simulated_tour_df = tm2_simulated_tour_df[ + tm2_simulated_tour_df.household_id.isin(tm2_simulated_trip_df.household_id) + ] + tm2_simulated_tour_df.to_csv(os.path.join(test_dir, "tours.csv"), index=False) + + tm2_simulated_trip_df.to_csv(os.path.join(test_dir, "trips.csv"), index=False) + + +def create_summary(input_df, key, out_col="Share") -> pd.DataFrame: + """ + Create summary for the input data. + 1. group input data by the "key" column + 2. calculate the percent of input data records in each "key" category. + + :return: pd.DataFrame + """ + + out_df = input_df.groupby(key).size().reset_index(name="Count") + out_df[out_col] = round(out_df["Count"] / out_df["Count"].sum(), 4) + + return out_df[[key, out_col]] + + +def cosine_similarity(a, b): + """ + Computes cosine similarity between two vectors. + + Cosine similarity is used here as a metric to measure similarity between two sequence of numbers. + Two sequence of numbers are represented as vectors (in a multi-dimensional space) and cosine similiarity is defined as the cosine of the angle between them + i.e., dot products of the vectors divided by the product of their lengths. + + :return: + """ + + return dot(a, b) / (norm(a) * norm(b)) + + +def compare_simulated_against_target( + target_df: pd.DataFrame, + simulated_df: pd.DataFrame, + target_key: str, + simulated_key: str, +) -> bool: + """ + compares the simulated and target results by computing the cosine similarity between them. + + :return: + """ + + merged_df = pd.merge( + target_df, simulated_df, left_on=target_key, right_on=simulated_key, how="outer" + ) + merged_df = merged_df.fillna(0) + + logger.info("simulated vs target share:\n%s" % merged_df) + + similarity_value = cosine_similarity( + merged_df["Target_Share"].tolist(), merged_df["Simulated_Share"].tolist() + ) + + logger.info("cosine similarity:\n%s" % similarity_value) + + return similarity_value