From 2cd06a68d040c69178fdddde799118e7733be01b Mon Sep 17 00:00:00 2001 From: David Hensle <51132108+dhensle@users.noreply.github.com> Date: Tue, 21 Mar 2023 12:28:35 -0700 Subject: [PATCH 1/6] BayDAG Contributions (#657) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * code changes to add joint tour utilities in cdap * add joint tour frequency composition component * post process jtfc result to tours * update joint tour participation * update #inmtf to tm2 specs * allow alternative id > 127 * set up testing infrastructure * cdap test script * cdap configs * set up testing infrastructure * joint tour test script * joint tour configs * set up testing infrastructure * nm tour frequency test script * nm tour frequency configs * add jtfc alt table dictionary to yaml * update jtfc yaml * jtfc move coef values to coef.csv * jtfc update preprocessor * add jtfc alt table dictionary to yaml * nmtf consolidate all -999 to one coef * code changes to fix bug in parking location choice model * Revert "code changes to fix bug in parking location choice model" This reverts commit 5a38ebb902df50ea612cef946783b33ace90c75e. * parking location choice bug fix * move coefficients to csv file * restore the original mandatory channels * restore the original mandatory channels * RSG Phase 7 Development (#49) * school escorting initial commit * bundles created * added pure escort tours after non_mand_sched * created escorting tours & trips * integrated escort tours and trips * Differentiate examples between quality and validity of example models (#571) * change examples that are no longer tied to an agency to fictional place names * change name of full example also * add back missing output dir * restore test output dir also * more empty dirs that got lost * clean up docs * example_mtc -> prototype_mtc * Prototype MTC extended * add all the ignored files * add test output dirs * remove superfluous example_test * prototype_sf * prototype_arc * prototype_marin * move dirs * psrc * semcog * sandag_xborder * placeholder_sandag * placeholder_multiple_zone * no more coveralls * repair docs * clean up example names * black and isort (#578) * black and isort * stop emitting output dir it fails later tests * trace files in nested directories because windows * swap files for xborder model * repair ARC MP * downstream model integration * print checksum even when not used * add hashes for sandag_xborder_full * fix dtype in university hack * fix persons to match tours * repair ARC * initial commit of flexible tour/trip ids * pycodestyle * black formatting * Bump version: 1.0.4 → 1.1.0 * adding frequency alternatives configs required for test system * added additional unit tests * added setup function for test configs dir * formatting * handling missing alts files in examples * error catching around extension probs * passing tests * still passing tests around missing config files * accounting for missing mtf spec in marin example * nm tour scheduling passing * updating stop freq in tours table * added check for mand tour overlap with pe tours * num chauffeurs and escortees * fixed defaults not getting used if file missing * merging canonical ids from flexible id work * setting escorting tour and trip ids * remove unneeded nmtf settings file * black formatting * excluding escort trips from trip scheduling * fixing bug where mand tours not getting assigned correct id * adding school_escorting to mp model list * Added mwcog small area * missed one edit * adding school escort tour flavors as own category * reformatting * updating timetable windows with pure escort tours * additional logging * Update README.MD * call as module * github actions tests * adding non-mand destination changes * pre commit hooks * pyproject toml * limit numpy * docs for mamba instead of conda * ignore generated files * add nbmake to test env * fix dupe line * fixing bad tdd merge * repair test multiple zones for github actions * publish develop docs * fix docbuild env * merging tdd alts to all tours * adding ride share threshold to unvailability for pure escort time overlap * cleanup * Update .travis.yml * fixed testing files * fixed testing files (again) * fixed test script again * mins per time bin * black formatting * black formatting * fixing reindex import bug * fixing missed import * replacing trips test table * inserting default setting if no models in config for tests * publish docs to branch name * adding setup function to tests to set configs_dir injectable * documentation * updated testing scripts (note shorter travis script for now) * fixed slash (windows vs. linux testing issue) * added output folders * updated travis script to run all tests, should pass * docs cleaning * docs re-style * rebuild * dynamic versioning docs * version switcher * blacken * fix switcher url * fix conf * switcher update * master to main * deployment actions * actions * build wheel * fix for testpypi * blacken * manual switcher * branch docs service [makedocs] * syntax [makedocs] * travis depends * checkout v3, fix versioning in docs * only build develop docs once * failsafe version * documentation repairs * python-simplified-semver * front cards * end testing w travis * add mwcog test to gh-actions * add mwcog to docs * fixed origin bug and missing outbound trip to work * point to data not copy it * sort dependencies * req sh 2.2.4 * account for variance across platforms in trip dest est * copy bike skims for sandag test * proper cleanup when trips that get removed if schedule failed * changed failed trip scheduling option for example * updating regress table * blacken * extending nmtf and stop frequency to demonstrate flexible ids * param on rtol * fix sandag_2 test files * fix test file names * allowing multiple escort tours in the same period * sandag 3-zone example fix * fixing expression for missing escort participants sandag 3_zone * cleanup * blacken * Update trip_destination.py applying valid primary origin default value * Update trip_destination.py blacken * fixing bug for inbound destination for second escortee * blacken * Disaggregate Accessibilities (#5) * initial commit with basic working proto pop gen * fixed bad dependency range * fixed vary_on method to vary all with mapped fields after * added check if already dataframe * added dataframe check if the table passed is already a dataframe * working proto pop gen. still debugging mandatory work/school model * check all table is df * workplace runs, next testing school * began setting up to get all logsums, not just chosen ones * restructured to run as either one-off or model step * revert to last merge. Removed dataframe check in iterate, no longer needed to modify this file * extracted logsums, need to inject into pipeline or save output? * working standalone or step model * extractable accessibilities for fixed and nonmandatory * extractable accessibilities for fixed and nonmandatory * cleanup and add documentation * resolved duplicates of old 'example' folders * model uses write_tables functionality * disaggregate accessibility runs as model step or standalone. Runs as subprocess * working model test. table initialization not yet working * added logsum short circuit to get logsums and avoid drawing unneccesary samples * working model plus major cleanup * working model plus major cleanup * override sample size * fixed trace output files * skip_choice default to False * fixed empty logsums bug * cleanup redundent parameters * fixed list index instead of set * coordinated model run files * added default skip to false * added multiprocessing line * began setup for multiprocessing * fixed sampling problem. Pipeline for multi processing, NOT WORKING YET * deleted run file, not needed anymore * update release instructions * auto-build docs on release * remind to update switcher * need checkout * added helper functions for handling suffix args * to enable multiprocessing, overhauled pipeline method to include proto_ tables rather than overwriting main tables * working multiprocess, but requires debugging. fails on other models * working multiprocess, but requires debugging. fails on other models * cleanup of mp table registering * fixed tracing and slicing issue * removed old 'run' model * setup example 2 zones * minimum working 2 zone * multizone basic working * fixed buggy settings * multiprocessing working, but empty final tables * fixed blank output bug and cleaned up settings to use verbose table names (not just 'disaggregate_accessibility') * cleanup run scripts a bit * fixed missing base path for full run * fixed missing path * fixed typo * fixed 1 per taz sampling * fixing person merge bug and filtering workers * fixed duplicate tours! * updated settings for initialize disagg * removed obsolete file * cleaned up file and moved initialize to tables/... * added find nearest node function * added initialize disaggregate_accessibility to initialize_households * moved initialize steps to 'tables/disaggregate_accessibility' * updated settings to include initialize disaggregate accessibilities as part of initialize_households * moved initialize disagg accessibilities to tables. Created working merge step using naive bayes and 'hard' join * add mp back in * PEP formatting revisions * fixed logsums merge on households instead of persons * fixed _accessibility suffix * fixed conflict with persons_merged * updated yaml to use simpler join method * PEP formatting fixes * refreshed example folder from develop * added missing line at end of file * black fixes * black fixes to disaggregate accessibility changes * fixed missing column pipeline error * merged disagg accessibilities into mtc_extended and added doc section * ran black on disaggregate_accessibility.py * updated dependencies * removing sklearn imports * blacken * add if none catch * fixed None suffix default * moved order of get_table(persons_merged) to avoid pulling prematurely in xborder * tested and cleaned up rng channels * setup injectable suffixes to allow add_size_table as model step not just function * removed accessibility output from test * re blacken py scripts * fixed tracing typo * added variable index name suffix to pass optionally * pipeline housekeeping to cleanup any tables, traceables, or channels that were touched during disagg * added multiprocess testing * blacken updates * updated test scripts to include MP, problem with vehicle model in mp (even without disagg acc?) * added improved origin sampling, resolved issue with merging with sample <100%. Need to address random seed for origins * added sci-kit learn to test depends * fixed person merging error causing pytest fail, uses inject method to ensure consistency * cleanup comments * fixed pytest to include accessibility table in regress * setup for mp test, but needs debugging * 'blacken' * 'blacken' * cleanup example folder * fixed pipeline NoneType bug for disagg accessibility table * fixed pytest fail on mp, due to exept:True on mp_simulate * 'blacken' * added weighted k-means method * created run script for multiple sampling scenarios * blacken changes * blacken * fixed copy script * blacken sampling script * fixed n_zone when integer * fixed typo * update sampling script * 'blacken' * fixed replacement sample bug * updated documentation * more flexible scenario testing * blacken Co-authored-by: Nick Fournier Co-authored-by: Jeffrey Newman Co-authored-by: Nick Fournier <99497883+nick-fournier-rsg@users.noreply.github.com> * Shadow Pricing Enhancements (#7) * updated scripts to include simulation-based shadow pricing * blacken * Updated shadow_pricing.yaml for mtc example * code cleanup * more cleanup * documentation and passing tests * passing tests * passing tests * updated doc on shadow pricing * 2nd Update model doc on shadow pricing * more doc update on shadow pricing * fixing pandas future warning * blacken * bug in trying to access shadow price settings when not running shadow pricing * limiting pandas version * always updating choices * testing removal of lognormal for hh vot * putting hh vot back in * updating to match sharrow test versions * raw person table for buffer instead of injectable * adding segmentation, output by iteration, and external worker removal * formatting & documentation Co-authored-by: aletzdy <58451076+aletzdy@users.noreply.github.com> * updating mtc_extended test files * no sp for non-work or school, better logging, weighting options * sample TAZ only if available MAZ when shadow pricing * adding missed weight column option * cleaning up comments Co-authored-by: Jeffrey Newman Co-authored-by: Andrew Rohne Co-authored-by: Nick Fournier Co-authored-by: Nick Fournier <99497883+nick-fournier-rsg@users.noreply.github.com> Co-authored-by: aletzdy <58451076+aletzdy@users.noreply.github.com> * set up testing infrastructure (#40) Co-authored-by: Lisa Zorn * BayDAG auto ownership configs and test (#41) * set up testing infrastructure * auto ownership test script * auto ownership configs * ao move coef values to coef.csv Co-authored-by: Lisa Zorn * blacken * more formatting * blacken * blacken * blacken * BayDAG parking location choice configs and test (#45) * code changes to fix bug in parking location choice model * parking location choice configs * Revert "code changes to fix bug in parking location choice model" This reverts commit 5a38ebb902df50ea612cef946783b33ace90c75e. * parking location choice bug fix * parking location choice config updates move coefficients to csv file and activitysim variable naming consistency * parking location choice summary Co-authored-by: Lisa Zorn * blacken * cdap bug fixes * adding sklearn to dependencies * Update activitysim-dev.yml fixing bad reference to scikit-learn * Update activitysim-test.yml fixing bad reference to scikit-learn * Update setup.cfg fixing bad reference to scikit-learn * Memory Bug Fix * working with 2k no sp * optimize nearest zone determination * Changed data type of participant_id in candidates in joint_tour_participation_candidates() to unsigned 64-bit integer to prevent overflow * better comments * fix overflow in jtp participant id * blacken * forcing participation * Trip Scheduling (#51) * Differentiate examples between quality and validity of example models (#571) * change examples that are no longer tied to an agency to fictional place names * change name of full example also * add back missing output dir * restore test output dir also * more empty dirs that got lost * clean up docs * example_mtc -> prototype_mtc * Prototype MTC extended * add all the ignored files * add test output dirs * remove superfluous example_test * prototype_sf * prototype_arc * prototype_marin * move dirs * psrc * semcog * sandag_xborder * placeholder_sandag * placeholder_multiple_zone * no more coveralls * repair docs * clean up example names * black and isort (#578) * black and isort * stop emitting output dir it fails later tests * trace files in nested directories because windows * swap files for xborder model * repair ARC MP * print checksum even when not used * add hashes for sandag_xborder_full * fix dtype in university hack * fix persons to match tours * repair ARC * Bump version: 1.0.4 → 1.1.0 * Added mwcog small area * missed one edit * reformatting * Update README.MD * call as module * github actions tests * pre commit hooks * pyproject toml * limit numpy * docs for mamba instead of conda * ignore generated files * add nbmake to test env * fix dupe line * repair test multiple zones for github actions * publish develop docs * fix docbuild env * Update .travis.yml * fixed testing files * fixed testing files (again) * fixed test script again * publish docs to branch name * updated testing scripts (note shorter travis script for now) * fixed slash (windows vs. linux testing issue) * added output folders * updated travis script to run all tests, should pass * docs cleaning * docs re-style * rebuild * dynamic versioning docs * version switcher * blacken * fix switcher url * fix conf * switcher update * master to main * deployment actions * actions * build wheel * fix for testpypi * blacken * manual switcher * branch docs service [makedocs] * syntax [makedocs] * travis depends * checkout v3, fix versioning in docs * only build develop docs once * failsafe version * documentation repairs * python-simplified-semver * front cards * end testing w travis * add mwcog test to gh-actions * add mwcog to docs * point to data not copy it * sort dependencies * req sh 2.2.4 * account for variance across platforms in trip dest est * copy bike skims for sandag test * param on rtol * fix sandag_2 test files * fix test file names * added pre-processor option to trip scheduling * trip scheduling relative mode initial commit * moved everything to mwcog example * adding output analysis notebook * adding additional segmentation * testing and documentation * blacken * not assuming scheduling mode is set * fixing merge trip scheduling * still fixing merge * reverting regression trips * fixing bad merge and updating test Co-authored-by: Jeffrey Newman Co-authored-by: Andrew Rohne * Trip Scheduling Bug & Trip Mode Choice Annotate (#55) * other sp methods price updates * Trip Scheduling into Resident Debug (#54) * Differentiate examples between quality and validity of example models (#571) * change examples that are no longer tied to an agency to fictional place names * change name of full example also * add back missing output dir * restore test output dir also * more empty dirs that got lost * clean up docs * example_mtc -> prototype_mtc * Prototype MTC extended * add all the ignored files * add test output dirs * remove superfluous example_test * prototype_sf * prototype_arc * prototype_marin * move dirs * psrc * semcog * sandag_xborder * placeholder_sandag * placeholder_multiple_zone * no more coveralls * repair docs * clean up example names * black and isort (#578) * black and isort * stop emitting output dir it fails later tests * trace files in nested directories because windows * swap files for xborder model * repair ARC MP * print checksum even when not used * add hashes for sandag_xborder_full * fix dtype in university hack * fix persons to match tours * repair ARC * Bump version: 1.0.4 → 1.1.0 * Added mwcog small area * missed one edit * reformatting * Update README.MD * call as module * github actions tests * pre commit hooks * pyproject toml * limit numpy * docs for mamba instead of conda * ignore generated files * add nbmake to test env * fix dupe line * repair test multiple zones for github actions * publish develop docs * fix docbuild env * Update .travis.yml * fixed testing files * fixed testing files (again) * fixed test script again * publish docs to branch name * updated testing scripts (note shorter travis script for now) * fixed slash (windows vs. linux testing issue) * added output folders * updated travis script to run all tests, should pass * docs cleaning * docs re-style * rebuild * dynamic versioning docs * version switcher * blacken * fix switcher url * fix conf * switcher update * master to main * deployment actions * actions * build wheel * fix for testpypi * blacken * manual switcher * branch docs service [makedocs] * syntax [makedocs] * travis depends * checkout v3, fix versioning in docs * only build develop docs once * failsafe version * documentation repairs * python-simplified-semver * front cards * end testing w travis * add mwcog test to gh-actions * add mwcog to docs * point to data not copy it * sort dependencies * req sh 2.2.4 * account for variance across platforms in trip dest est * copy bike skims for sandag test * param on rtol * fix sandag_2 test files * fix test file names * added pre-processor option to trip scheduling * trip scheduling relative mode initial commit * moved everything to mwcog example * adding output analysis notebook * adding additional segmentation * testing and documentation * blacken * not assuming scheduling mode is set * fixing merge trip scheduling * still fixing merge * reverting regression trips * fixing bad merge and updating test Co-authored-by: Jeffrey Newman Co-authored-by: Andrew Rohne * adding annotate to trip mode choice * no earliest change if previous trip fail Co-authored-by: David Hensle Co-authored-by: Jeffrey Newman Co-authored-by: Andrew Rohne * adding locals_dict to annotate * chooser cols in final trips table * BayDAG merge with ActivitySim v1.2 (#56) * fixed logsums merge on households instead of persons * code cleanup * more cleanup * documentation and passing tests * passing tests * fixed _accessibility suffix * fixed conflict with persons_merged * updated yaml to use simpler join method * PEP formatting fixes * refreshed example folder from develop * added missing line at end of file * black fixes * black fixes to disaggregate accessibility changes * passing tests * ext cli arg * memory sidecar * restart if resume_after checkpoint is missing * no pandas 1.5 yet * stop the sidecar * updated doc on shadow pricing * 2nd Update model doc on shadow pricing * more doc update on shadow pricing * minor repairs * skip when household_income is None * fixed missing column pipeline error * fixing pandas future warning * blacken * merged disagg accessibilities into mtc_extended and added doc section * ran black on disaggregate_accessibility.py * bug in trying to access shadow price settings when not running shadow pricing * limiting pandas version * updated dependencies * always updating choices * removing sklearn imports * blacken * add if none catch * fixed None suffix default * moved order of get_table(persons_merged) to avoid pulling prematurely in xborder * testing removal of lognormal for hh vot * putting hh vot back in * tested and cleaned up rng channels * setup injectable suffixes to allow add_size_table as model step not just function * removed accessibility output from test * re blacken py scripts * updating to match sharrow test versions * fixed tracing typo * added variable index name suffix to pass optionally * pipeline housekeeping to cleanup any tables, traceables, or channels that were touched during disagg * added multiprocess testing * blacken updates * updated test scripts to include MP, problem with vehicle model in mp (even without disagg acc?) * added improved origin sampling, resolved issue with merging with sample <100%. Need to address random seed for origins * added sci-kit learn to test depends * fixed person merging error causing pytest fail, uses inject method to ensure consistency * cleanup comments * fixed pytest to include accessibility table in regress * setup for mp test, but needs debugging * 'blacken' * 'blacken' * cleanup example folder * fixed pipeline NoneType bug for disagg accessibility table * raw person table for buffer instead of injectable * add mp test for prototype_mtc_extended * fixed pytest fail on mp, due to exept:True on mp_simulate * 'blacken' * added weighted k-means method * created run script for multiple sampling scenarios * blacken changes * blacken * fixed copy script * blacken sampling script * fixed n_zone when integer * fixed typo * Update random.py * Updated test environment to reflect changes in pandas * Removed random random seed generation from placeholder_sandage 1-zone test configuration to get example to pass automated test * update sampling script * 'blacken' * fixed replacement sample bug * adding segmentation, output by iteration, and external worker removal * updated documentation * more flexible scenario testing * blacken * Added test_random_seed.py * blacken test_random_seed.py * Moved random seed generation testing out of prototype_mtc folder and edited core_tests.yaml to call it * Corrected name of check_outputs() * Added print statements to debug script * Blacken test_random_seed.py * keep alternative attributes in parking location * move coeff values to coeff csv * Added pipe character to call that tests the random generator * Added explicit call for pytest to run test_random_seed.py * Added print statement at start of script for debugging * Replaced single quotes with double quotes in print statement * reformat with Black * Moved random seed test into activitysim folder * Edited core_tests.yml to reflect change in location of random seed test * Renamed tests folder to test to reflect call in core-tests.yml * formatting & documentation * Added test_random_seed function for PyTest to read * Fixed defition of seeds list and removed print statements * updating github test env * responding to review comments * Added line to create output folder as that's not being committed * create_rng_configs() now copies entire example configs directory instead of just settings.yaml * Added try except statement when creating the output directory in case it already exists * additional logging, random seed option, landuse sample weight column * ensuring TAZ is not selected if no available MAZ * adding logic to skip external location choice models * Applied suggested changes * Removed activitysim\test\random_seed\configs directory to reduce confusion * Moved random seed test directory to folder that had already existed * temporarily constrain pandas #608 * work around psutil runtimeerror * temp constrain pandas * add mwcog full example * review responses * revisions per Sijia's comments, except for the proto-table-template implementation, still working. * added template pop option * blacken * set back to create_tables to pass test against old tables * updated proto-pop test data * resolved bad test data issue * warn don't fail on duplicate skims * include sharrow in mwcog example * cleanup * zarr digital encoding for mwcog * include reference in psrc mini * fow window ids to int64 because windows * handle expected warnings * unpin dependencies * columns cannot be a set * unpin pandas * columns cannot be a set * numeric_only args in groupby.sum * docs: correct the unit of `chunk_size`. * change 'arry' to more readable 'sizearray' * repair tests * catch FileNotFound * add check that land use is zero-based when using sharrow * better docs on skims in shared memory * better docstrings for skimdataset * remove old code * remove junk * add logging for recoding * optimize nearest zone determination * consistent multiprocessing results * blacken * updating regression trips * adding sandag 2-zone test * blacken * fix merge error * allow pandas 1.5 again * sharrow compatibility with #606 * remove outdated code in comments * note on tracing limitation * allow repeating key cols * fix decoding in MP output * escortee modes match chauf modes * updating regression tests * fixing bad var name in inbound expressions * add note about `condense_parameters` * change default condense_parameters to False * Update .gitignore Co-authored-by: Sijia Wang * ignore caches * remove special install instructions * remove sharrow_skip's * Revert "remove sharrow_skip's" This reverts commit 6b52af2d837d66d3154d8949b2ab5e1d92245e97. * add note about why sharrow_skip is activated * Update school_escorting.yaml * Update config.py * arc trip destination spec skim correction * update regression trips * require sharrow 2.5 * fix mtc_ext for sharrow * fix test consistent with escort mode alignments * allow sharrow skipping * remove stray indent * delete expression values after sharrow testing to make debugging easier * require sharrow 2.5.2 * recode only once * testing repairs * note about why sharrow_skip * fork mtc_extended testing on shadow pricing and sharrow * note on sharrow_skip * size term scaling only for full run * reordering * multiprocessing hangs! * never scale size terms for disagg * tally pending persons * tests cleanup * fix error with orca * let pipeline tables be dropped * check on available MAZs, comment cleanup * work zone to -1 if wfh, removed unneeded extensions * fix tests for sandag 2zone * ignore deprecation warning in tests not clear why this started being a problem suddenly in only the xborder-MP model * add scikit-learn to formal dependencies * docs update for installer * fixing black to 22.12.0 * missed merge conflicts * fixing merge issues in prototype_semcog * fixing prototype_arc merge * fixing placeholder_sandag merge issues * blacken * fixing mwcog merge issues * skims not found during estimation test --------- Co-authored-by: Nick Fournier Co-authored-by: Jeff Newman Co-authored-by: aletzdy <58451076+aletzdy@users.noreply.github.com> Co-authored-by: Jeffrey Newman Co-authored-by: Nick Fournier <99497883+nick-fournier-rsg@users.noreply.github.com> Co-authored-by: Joe Flood Co-authored-by: Sijia Wang Co-authored-by: amarin <17020181+asiripanich@users.noreply.github.com> * do not require all alts in data file * int8 to int16 lost in v1.2 merge --------- Co-authored-by: Lisa Zorn Co-authored-by: Sijia Wang Co-authored-by: Ashish Kulshrestha Co-authored-by: Jeffrey Newman Co-authored-by: Andrew Rohne Co-authored-by: Nick Fournier Co-authored-by: Nick Fournier <99497883+nick-fournier-rsg@users.noreply.github.com> Co-authored-by: aletzdy <58451076+aletzdy@users.noreply.github.com> Co-authored-by: David Hensle Co-authored-by: JoeJimFlood Co-authored-by: Jeffrey Newman Co-authored-by: amarin <17020181+asiripanich@users.noreply.github.com> --- HOW_TO_RELEASE.md | 13 +- activitysim/abm/models/__init__.py | 1 + activitysim/abm/models/cdap.py | 71 ++- .../joint_tour_frequency_composition.py | 204 ++++++++ .../abm/models/joint_tour_participation.py | 25 +- activitysim/abm/models/location_choice.py | 2 + .../models/non_mandatory_tour_frequency.py | 2 +- .../abm/models/parking_location_choice.py | 1 - activitysim/abm/models/trip_mode_choice.py | 18 +- activitysim/abm/models/trip_scheduling.py | 58 ++- activitysim/abm/models/util/annotate.py | 6 +- activitysim/abm/models/util/cdap.py | 337 ++++++++++++- .../models/util/probabilistic_scheduling.py | 26 +- activitysim/abm/models/util/tour_frequency.py | 281 +++++++++++ activitysim/abm/models/vehicle_type_choice.py | 13 +- activitysim/abm/tables/shadow_pricing.py | 6 + activitysim/core/configuration.py | 291 +++++++++++ activitysim/core/los.py | 1 + activitysim/core/pathbuilder_cache.py | 1 + .../test/output/cache/.gitignore | 3 +- ...estination_annotate_trips_preprocessor.csv | 0 .../configs/trip_scheduling_preprocessor.csv | 8 + .../trip_scheduling_probs_purpose_stops.csv | 245 ++++++++++ .../configs/joint_tour_scheduling.yaml | 0 .../prototype_semcog/configs/logging.yaml | 0 .../configs/tour_scheduling_joint.csv | 0 conda-environments/activitysim-dev.yml | 1 + docs/examples.rst | 1 - docs/models.rst | 19 + test/auto_ownership/.gitkeep | 0 .../configs/annotate_households.csv | 19 + .../configs/annotate_landuse.csv | 6 + .../configs/annotate_persons.csv | 40 ++ .../configs/annotate_persons_after_hh.csv | 5 + .../auto_ownership/configs/auto_ownership.csv | 61 +++ .../configs/auto_ownership.yaml | 53 ++ .../configs/auto_ownership_coefficients.csv | 393 +++++++++++++++ test/auto_ownership/configs/constants.yaml | 68 +++ .../configs/initialize_households.yaml | 37 ++ .../configs/initialize_landuse.yaml | 54 +++ test/auto_ownership/configs/network_los.yaml | 14 + test/auto_ownership/configs/settings.yaml | 65 +++ ...ur_departure_and_duration_alternatives.csv | 191 ++++++++ test/auto_ownership/data/.gitkeep | 0 test/auto_ownership/test_auto_ownership.py | 434 +++++++++++++++++ test/cdap/.gitkeep | 0 test/cdap/configs/annotate_households.csv | 18 + .../cdap/configs/annotate_households_cdap.csv | 11 + test/cdap/configs/annotate_landuse.csv | 6 + test/cdap/configs/annotate_persons.csv | 54 +++ .../configs/annotate_persons_after_hh.csv | 5 + test/cdap/configs/annotate_persons_cdap.csv | 6 + test/cdap/configs/cdap.yaml | 49 ++ test/cdap/configs/cdap_coefficients.csv | 235 +++++++++ .../cdap_fixed_relative_proportions.csv | 9 + test/cdap/configs/cdap_indiv_and_hhsize1.csv | 81 ++++ .../configs/cdap_interaction_coefficients.csv | 177 +++++++ .../configs/cdap_joint_tour_coefficients.csv | 22 + test/cdap/configs/constants.yaml | 68 +++ test/cdap/configs/initialize_households.yaml | 37 ++ test/cdap/configs/initialize_landuse.yaml | 54 +++ test/cdap/configs/network_los.yaml | 14 + test/cdap/configs/settings.yaml | 60 +++ ...ur_departure_and_duration_alternatives.csv | 191 ++++++++ test/cdap/data/.gitkeep | 0 test/cdap/test_cdap.py | 413 ++++++++++++++++ test/conftest.py | 105 ++++ test/joint_tour_frequency/.gitkeep | 0 test/joint_tours/.gitkeep | 0 .../configs/JointTourFrequency.xls | Bin 0 -> 95232 bytes .../configs/annotate_households.csv | 19 + test/joint_tours/configs/annotate_landuse.csv | 6 + test/joint_tours/configs/annotate_persons.csv | 16 + .../configs/annotate_persons_jtp.csv | 3 + test/joint_tours/configs/annotate_tours.csv | 6 + test/joint_tours/configs/constants.yaml | 68 +++ .../configs/initialize_households.yaml | 38 ++ .../configs/initialize_landuse.yaml | 54 +++ .../joint_tours/configs/initialize_tours.yaml | 6 + .../joint_tour_frequency_composition.csv | 99 ++++ .../joint_tour_frequency_composition.yaml | 35 ++ ...our_frequency_composition_alternatives.csv | 151 ++++++ ..._composition_annotate_alt_preprocessor.csv | 7 + ...ition_annotate_households_preprocessor.csv | 8 + ...oint_tour_frequency_composition_coeffs.csv | 97 ++++ .../configs/joint_tour_participation.csv | 67 +++ .../configs/joint_tour_participation.yaml | 21 + ...ion_annotate_participants_preprocessor.csv | 13 + .../joint_tour_participation_coefficients.csv | 59 +++ test/joint_tours/configs/network_los.yaml | 14 + test/joint_tours/configs/settings.yaml | 79 +++ ...ur_departure_and_duration_alternatives.csv | 191 ++++++++ test/joint_tours/test_joint_tours.py | 265 ++++++++++ test/non_mandatory_tour_frequency/.gitkeep | 0 .../NonMandatoryIndividualTourFrequency.xls | Bin 0 -> 192512 bytes .../configs/annotate_households.csv | 13 + .../configs/annotate_landuse.csv | 6 + .../configs/annotate_persons.csv | 17 + .../configs/annotate_persons_after_hh.csv | 5 + .../configs/annotate_persons_nmtf.csv | 10 + .../configs/constants.yaml | 68 +++ .../configs/initialize_households.yaml | 48 ++ .../configs/initialize_landuse.yaml | 54 +++ .../configs/network_los.yaml | 14 + .../configs/non_mandatory_tour_frequency.csv | 187 +++++++ .../configs/non_mandatory_tour_frequency.yaml | 52 ++ ..._mandatory_tour_frequency_alternatives.csv | 198 ++++++++ ...requency_annotate_persons_preprocessor.csv | 36 ++ ...r_frequency_coefficients_PTYPE_DRIVING.csv | 60 +++ ...tour_frequency_coefficients_PTYPE_FULL.csv | 103 ++++ ...r_frequency_coefficients_PTYPE_NONWORK.csv | 90 ++++ ...tour_frequency_coefficients_PTYPE_PART.csv | 103 ++++ ...frequency_coefficients_PTYPE_PRESCHOOL.csv | 47 ++ ...r_frequency_coefficients_PTYPE_RETIRED.csv | 84 ++++ ...ur_frequency_coefficients_PTYPE_SCHOOL.csv | 72 +++ ...requency_coefficients_PTYPE_UNIVERSITY.csv | 81 ++++ ...ndatory_tour_frequency_extension_probs.csv | 193 ++++++++ .../configs/settings.yaml | 60 +++ ...ur_departure_and_duration_alternatives.csv | 191 ++++++++ .../data/.gitkeep | 0 .../test_non_mandatory_tour_frequency.py | 336 +++++++++++++ test/parking_location/.gitkeep | 0 .../compare-parking-location-results.R | 62 +++ .../configs/ParkLocationChoice.xls | Bin 0 -> 117248 bytes .../configs/annotate_households.csv | 13 + .../configs/annotate_landuse.csv | 7 + .../configs/annotate_persons.csv | 16 + .../configs/annotate_persons_after_hh.csv | 5 + .../configs/initialize_households.yaml | 37 ++ .../configs/initialize_landuse.yaml | 52 ++ .../parking_location/configs/network_los.yaml | 24 + .../configs/parking_location_choice.csv | 34 ++ .../configs/parking_location_choice.yaml | 22 + ...ion_choice_annotate_trips_preprocessor.csv | 6 + .../parking_location_choice_coeffs.csv | 5 + test/parking_location/configs/settings.yaml | 62 +++ ...ur_departure_and_duration_alternatives.csv | 191 ++++++++ .../parking-location-results-summary.twbx | Bin 0 -> 717798 bytes .../parking_location/test_parking_location.py | 458 ++++++++++++++++++ 139 files changed, 9078 insertions(+), 55 deletions(-) create mode 100644 activitysim/abm/models/joint_tour_frequency_composition.py create mode 100644 activitysim/core/configuration.py mode change 100644 => 100755 activitysim/examples/prototype_mwcog/configs/trip_destination_annotate_trips_preprocessor.csv create mode 100644 activitysim/examples/prototype_mwcog/configs/trip_scheduling_preprocessor.csv create mode 100644 activitysim/examples/prototype_mwcog/configs/trip_scheduling_probs_purpose_stops.csv mode change 100755 => 100644 activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml mode change 100755 => 100644 activitysim/examples/prototype_semcog/configs/logging.yaml mode change 100755 => 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv create mode 100644 test/auto_ownership/.gitkeep create mode 100644 test/auto_ownership/configs/annotate_households.csv create mode 100644 test/auto_ownership/configs/annotate_landuse.csv create mode 100644 test/auto_ownership/configs/annotate_persons.csv create mode 100644 test/auto_ownership/configs/annotate_persons_after_hh.csv create mode 100644 test/auto_ownership/configs/auto_ownership.csv create mode 100644 test/auto_ownership/configs/auto_ownership.yaml create mode 100644 test/auto_ownership/configs/auto_ownership_coefficients.csv create mode 100644 test/auto_ownership/configs/constants.yaml create mode 100644 test/auto_ownership/configs/initialize_households.yaml create mode 100644 test/auto_ownership/configs/initialize_landuse.yaml create mode 100644 test/auto_ownership/configs/network_los.yaml create mode 100644 test/auto_ownership/configs/settings.yaml create mode 100644 test/auto_ownership/configs/tour_departure_and_duration_alternatives.csv create mode 100644 test/auto_ownership/data/.gitkeep create mode 100644 test/auto_ownership/test_auto_ownership.py create mode 100644 test/cdap/.gitkeep create mode 100644 test/cdap/configs/annotate_households.csv create mode 100644 test/cdap/configs/annotate_households_cdap.csv create mode 100644 test/cdap/configs/annotate_landuse.csv create mode 100644 test/cdap/configs/annotate_persons.csv create mode 100644 test/cdap/configs/annotate_persons_after_hh.csv create mode 100644 test/cdap/configs/annotate_persons_cdap.csv create mode 100644 test/cdap/configs/cdap.yaml create mode 100644 test/cdap/configs/cdap_coefficients.csv create mode 100644 test/cdap/configs/cdap_fixed_relative_proportions.csv create mode 100644 test/cdap/configs/cdap_indiv_and_hhsize1.csv create mode 100644 test/cdap/configs/cdap_interaction_coefficients.csv create mode 100644 test/cdap/configs/cdap_joint_tour_coefficients.csv create mode 100644 test/cdap/configs/constants.yaml create mode 100644 test/cdap/configs/initialize_households.yaml create mode 100644 test/cdap/configs/initialize_landuse.yaml create mode 100644 test/cdap/configs/network_los.yaml create mode 100644 test/cdap/configs/settings.yaml create mode 100644 test/cdap/configs/tour_departure_and_duration_alternatives.csv create mode 100644 test/cdap/data/.gitkeep create mode 100644 test/cdap/test_cdap.py create mode 100644 test/conftest.py create mode 100644 test/joint_tour_frequency/.gitkeep create mode 100644 test/joint_tours/.gitkeep create mode 100644 test/joint_tours/configs/JointTourFrequency.xls create mode 100644 test/joint_tours/configs/annotate_households.csv create mode 100644 test/joint_tours/configs/annotate_landuse.csv create mode 100644 test/joint_tours/configs/annotate_persons.csv create mode 100644 test/joint_tours/configs/annotate_persons_jtp.csv create mode 100644 test/joint_tours/configs/annotate_tours.csv create mode 100644 test/joint_tours/configs/constants.yaml create mode 100644 test/joint_tours/configs/initialize_households.yaml create mode 100644 test/joint_tours/configs/initialize_landuse.yaml create mode 100644 test/joint_tours/configs/initialize_tours.yaml create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition.csv create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition.yaml create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition_alternatives.csv create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition_annotate_alt_preprocessor.csv create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition_annotate_households_preprocessor.csv create mode 100644 test/joint_tours/configs/joint_tour_frequency_composition_coeffs.csv create mode 100644 test/joint_tours/configs/joint_tour_participation.csv create mode 100644 test/joint_tours/configs/joint_tour_participation.yaml create mode 100644 test/joint_tours/configs/joint_tour_participation_annotate_participants_preprocessor.csv create mode 100644 test/joint_tours/configs/joint_tour_participation_coefficients.csv create mode 100644 test/joint_tours/configs/network_los.yaml create mode 100644 test/joint_tours/configs/settings.yaml create mode 100644 test/joint_tours/configs/tour_departure_and_duration_alternatives.csv create mode 100644 test/joint_tours/test_joint_tours.py create mode 100644 test/non_mandatory_tour_frequency/.gitkeep create mode 100644 test/non_mandatory_tour_frequency/configs/NonMandatoryIndividualTourFrequency.xls create mode 100644 test/non_mandatory_tour_frequency/configs/annotate_households.csv create mode 100644 test/non_mandatory_tour_frequency/configs/annotate_landuse.csv create mode 100644 test/non_mandatory_tour_frequency/configs/annotate_persons.csv create mode 100644 test/non_mandatory_tour_frequency/configs/annotate_persons_after_hh.csv create mode 100644 test/non_mandatory_tour_frequency/configs/annotate_persons_nmtf.csv create mode 100644 test/non_mandatory_tour_frequency/configs/constants.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/initialize_households.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/initialize_landuse.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/network_los.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_alternatives.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv create mode 100644 test/non_mandatory_tour_frequency/configs/non_mandatory_tour_frequency_extension_probs.csv create mode 100644 test/non_mandatory_tour_frequency/configs/settings.yaml create mode 100644 test/non_mandatory_tour_frequency/configs/tour_departure_and_duration_alternatives.csv create mode 100644 test/non_mandatory_tour_frequency/data/.gitkeep create mode 100644 test/non_mandatory_tour_frequency/test_non_mandatory_tour_frequency.py create mode 100644 test/parking_location/.gitkeep create mode 100644 test/parking_location/compare-parking-location-results.R create mode 100644 test/parking_location/configs/ParkLocationChoice.xls create mode 100644 test/parking_location/configs/annotate_households.csv create mode 100644 test/parking_location/configs/annotate_landuse.csv create mode 100644 test/parking_location/configs/annotate_persons.csv create mode 100644 test/parking_location/configs/annotate_persons_after_hh.csv create mode 100644 test/parking_location/configs/initialize_households.yaml create mode 100644 test/parking_location/configs/initialize_landuse.yaml create mode 100644 test/parking_location/configs/network_los.yaml create mode 100644 test/parking_location/configs/parking_location_choice.csv create mode 100644 test/parking_location/configs/parking_location_choice.yaml create mode 100644 test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv create mode 100644 test/parking_location/configs/parking_location_choice_coeffs.csv create mode 100644 test/parking_location/configs/settings.yaml create mode 100644 test/parking_location/configs/tour_departure_and_duration_alternatives.csv create mode 100644 test/parking_location/notebook/parking-location-results-summary.twbx create mode 100644 test/parking_location/test_parking_location.py 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..fdf1b1076 100644 --- a/activitysim/abm/models/trip_scheduling.py +++ b/activitysim/abm/models/trip_scheduling.py @@ -8,7 +8,7 @@ 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, inject, pipeline, tracing, expressions from activitysim.core.util import reindex from .util.school_escort_tours_trips import split_out_school_escorting_trips @@ -36,6 +36,7 @@ DEPARTURE_MODE = "departure" DURATION_MODE = "stop_duration" +RELATIVE_MODE = "relative" PROBS_JOIN_COLUMNS_DEPARTURE_BASED = [ "primary_purpose", "outbound", @@ -43,6 +44,7 @@ "trip_num", ] PROBS_JOIN_COLUMNS_DURATION_BASED = ["outbound", "stop_num"] +PROBS_JOIN_COLUMNS_RELATIVE_BASED = ["outbound", "periods_left"] def set_tour_hour(trips, tours): @@ -142,8 +144,11 @@ 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 + tmp_trips["earliest"] = np.where( + ~tmp_trips["outbound"] & ~tmp_trips["max_outbound_departure"].isna(), + tmp_trips["max_outbound_departure"], + tmp_trips["earliest"], ) trips["earliest"] = tmp_trips["earliest"].reindex(trips.index) @@ -181,6 +186,7 @@ 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 +196,14 @@ 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 +242,35 @@ 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): - if outbound or scheduling_mode == DURATION_MODE: + nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i) + + # - 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 +292,12 @@ 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 + update_tour_earliest(trips, choices) + # adjust allowed depart range of next trip has_next_trip = nth_trips.next_trip_id != NO_TRIP_ID if has_next_trip.any(): @@ -437,9 +470,9 @@ 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) @@ -499,6 +532,11 @@ 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/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/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 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b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv old mode 100755 new mode 100644 diff --git a/conda-environments/activitysim-dev.yml b/conda-environments/activitysim-dev.yml index ea1a36ab7..c46b436de 100644 --- a/conda-environments/activitysim-dev.yml +++ b/conda-environments/activitysim-dev.yml @@ -58,6 +58,7 @@ dependencies: - scikit-learn >= 1.1 - sharrow >= 2.5.2 - simwrapper > 1.7 +- scikit-learn >= 1.1.1 - snakeviz # for profiling - sphinx - sphinx_rtd_theme diff --git a/docs/examples.rst b/docs/examples.rst index add56f8b3..881867132 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -513,7 +513,6 @@ Configuration This section has been moved to :ref:`configuration`. - .. _sub-model-spec-files: Sub-Model Specification Files diff --git a/docs/models.rst b/docs/models.rst index ef79a38cd..ca2c1122a 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -1232,6 +1232,25 @@ 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 probabalisitic 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 subsequet 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. + +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. + 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/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/test/auto_ownership/configs/constants.yaml b/test/auto_ownership/configs/constants.yaml new file mode 100644 index 000000000..b0bd5a1f3 --- /dev/null +++ b/test/auto_ownership/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/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/test/auto_ownership/configs/network_los.yaml b/test/auto_ownership/configs/network_los.yaml new file mode 100644 index 000000000..391125a38 --- /dev/null +++ b/test/auto_ownership/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/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 + + 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z5XgIjjT?+LX1C(u%5-c)9K6k#WA86#>% zQPEd~l?s;LA2usZ-Ix{Gx;Zh7-JBTKZcYqyHz$U@_b0xlkxJtvMb!0Y#gFBv2L7i6 zXbM%tAk8X|#L5vrV>A1%C*FO^f>9W%B)Lq-3@=UaUx z;o{xG$H=Fl6o)StaoEZYX7I2W4O*``?#8BVYT7WUAzn7MYZsrtiRsyjWrzu)aod+q zN0DU=^r#d}hZ!EeIL)5pdgaX`7Cq2XhlV_Pc;v^94eX9K%y3KcF|05ds EH}Ov4asU7T literal 0 HcmV?d00001 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 0000000000000000000000000000000000000000..4726c3f73248adae3cc3c978e86424907238e027 GIT binary patch literal 192512 zcmeFa2Y40L);_)y0)*bB3Wq8k5}Hy22_^L2G_S7kj_v|GsPX%-J(%PDs4p_dMV4|1o(c=j=6m-goV`_L@C&W0b{sms{Xb9xb&}DL zwQ8z`)1IyUv`SR9l9hTFpSLGHivLQzhkcP+rRpg)FRNf#@4&3Q<#}0w|M6{%=#Z5_ z9m>(4MkF*<$tpqBQFWn9ebqoURE^YS$kjx(7EPPV|7P<4HaYL0{2!s3s*24On^!IU ztmo@}ZPZiXuh_g78gQ$bFj^IsMau3S&Edomyit+Jn6P z`bv~EdcJwIeNsDB6D_Jbb?MZ(cjwOim-HC6sJoq5)T4b;N6d5WT*ptH8O{m1Gj&q6 z)rrweefzs{kS5egC}t;bE)SZ{Iz;tUsY)%ODm??XW;(g3y9gHb7^VfC`gDTcOS-2m 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zq${M)N>@sslm1@%2k9#5AEm3M&r8=x+oXSz{#m+K`WNX7(sj}orR$|HNne(3kZzRz zRk}&~igdH|Z_-z#uSvH^Uzffi-70-k`j&K?^zYK`(zm7WNOwqgO8+5km%c0ACEYE3 zPx`*}1L+>=htj=Li*%oKzqCX8Pw4^aLFq@*L(;?2e@Tx>k4le8Kb9Vsej@!;+9~}^ zdO~_q`nmKAX_xer^h@bi($ms2(zDWY((}>_(yyi6(jMtW=_Tnm(r=}grT>PcCEG*lDfN>TjcMG5ByC+;*%!G2W*{yXY76?2avN_o?oW z`+wxayVh@M*wuz(^XZG^-l*1XsXdO>KKY>k>X)4oe~#l|cYN>xcbx6ja&J_tx<)NA zWuLBESYeNelnrWs8*F>qT3iwSoIP-EYLzwUTMYW~&b IU!C&*3nxMplK=n! literal 0 HcmV?d00001 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 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+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 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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 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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 From 0fe7bcf1c9c5021855e04e113cb284aea96c6d40 Mon Sep 17 00:00:00 2001 From: Amarin Siripanich <17020181+asiripanich@users.noreply.github.com> Date: Tue, 28 Mar 2023 06:47:05 +1100 Subject: [PATCH 2/6] docs: apply a minor correction to user guides (#659) --- docs/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/examples.rst b/docs/examples.rst index 881867132..0b9634d28 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -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 From fbec0a59f9ee9a0886d12b7845d3bed0f9a7f6e0 Mon Sep 17 00:00:00 2001 From: Jeffrey Newman Date: Tue, 28 Mar 2023 13:36:03 -0400 Subject: [PATCH 3/6] Trip scheduling logic (#660) * trip scheduling logic_version * in testing ignore the warning we just added * cannot use logic_version 1 with relative scheduling --- activitysim/abm/models/trip_scheduling.py | 63 +++++++++++++++++------ activitysim/core/config.py | 8 +++ docs/models.rst | 26 ++++++++-- 3 files changed, 76 insertions(+), 21 deletions(-) diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py index fdf1b1076..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, pipeline, tracing, expressions +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__) @@ -47,6 +48,22 @@ 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): """ add columns 'tour_hour', 'earliest', 'latest' to trips @@ -108,7 +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. @@ -121,6 +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 @@ -145,11 +170,18 @@ 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 # FIXME - extra logic added because max_outbound_departure can be NA if previous failed trip was removed - tmp_trips["earliest"] = np.where( - ~tmp_trips["outbound"] & ~tmp_trips["max_outbound_departure"].isna(), - tmp_trips["max_outbound_departure"], - tmp_trips["earliest"], - ) + 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) @@ -248,7 +280,6 @@ def schedule_trips_in_leg( 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) # - annotate trips @@ -296,7 +327,12 @@ def schedule_trips_in_leg( # choices are relative to the previous departure time choices = nth_trips.earliest + choices # need to update the departure time based on the choice - update_tour_earliest(trips, choices) + 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 @@ -332,7 +368,6 @@ def run_trip_scheduling( trace_hh_id, trace_label, ): - set_tour_hour(trips_chunk, tours) set_stop_num(trips_chunk) @@ -361,7 +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] @@ -386,7 +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. @@ -498,13 +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 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/docs/models.rst b/docs/models.rst index ca2c1122a..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. @@ -1232,14 +1232,15 @@ 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 probabalisitic trip scheduling mode was developed named "relative". +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 subsequet trip, the choices are selected with respect to +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. +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: @@ -1251,6 +1252,21 @@ scheduling probabilities are indexed by the following columns: 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. From 372f943ce39b42b4263be40fb632daee25bdd253 Mon Sep 17 00:00:00 2001 From: Jeffrey Newman Date: Tue, 11 Apr 2023 12:06:57 -0500 Subject: [PATCH 4/6] Pin Dependencies (#665) * pinderella * pandas in setup.cfg deps --- activitysim/cli/create.py | 6 +-- conda-environments/activitysim-dev-base.yml | 47 ++++++++++---------- conda-environments/activitysim-dev.yml | 48 ++++++++++----------- conda-environments/github-actions-tests.yml | 33 +++++++------- setup.cfg | 2 +- 5 files changed, 69 insertions(+), 67 deletions(-) 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/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 c46b436de..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 @@ -26,46 +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 -- scikit-learn >= 1.1.1 - 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/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 From 41d89618193700e69237113eaed8826f8dba33a0 Mon Sep 17 00:00:00 2001 From: David Hensle <51132108+dhensle@users.noreply.github.com> Date: Fri, 19 May 2023 13:10:54 -0700 Subject: [PATCH 5/6] Updated SEMCOG Example (#603) * initial example_semcog commit * documentation * single process passing but not multiprocess * testing infrastructure * blacken * formatting * implemented stable sample for university extensions * adding ignored output folder * removed squared term and re-estimated * updating test env * calibrated configs * fixing bad merge * updated configs * reading in extension folder * updated shadowpricing yaml file * updated test regress * Revert "updated test regress" This reverts commit 8d5af7def30e80b40c4a48cc62a457fe67fb828e. * updated regress test table * updated test regress table * updated cropped land use and skim * updated shadow pricing method * updated network_los to read segmented skims if needed * updated example_manifest.yaml * fixing conflicts * making fixes per review comments * push mistake! * Revert "making fixes per review comments" This reverts commit c05c7f3b4818f60623873cbabb68f71434282223. * Revert "Revert "making fixes per review comments"" This reverts commit d83019add63adbcd87ce1f195fad4e714df42841. * added back accidentally untracked output folder + updated regress table * misc small fixes --------- Co-authored-by: Ali Etezady <58451076+aletzdy@users.noreply.github.com> --- .github/workflows/core_tests.yml | 2 +- activitysim/examples/example_manifest.yaml | 92 +- .../.gitignore | 0 .../README.MD | 0 .../configs/_dummy_coefficients.csv | 0 .../configs/accessibility.csv | 1 + .../configs/accessibility.yaml | 2 +- .../configs/annotate_households.csv | 12 +- .../configs/annotate_households_cdap.csv | 0 .../configs/annotate_households_workplace.csv | 0 .../configs/annotate_landuse.csv | 1 + .../configs/annotate_persons.csv | 15 +- .../configs/annotate_persons_after_hh.csv | 4 +- .../configs/annotate_persons_cdap.csv | 1 - .../configs/annotate_persons_jtp.csv | 0 .../configs/annotate_persons_mtf.csv | 0 .../configs/annotate_persons_nmtf.csv | 0 .../configs/annotate_persons_school.csv | 0 .../configs/annotate_persons_workplace.csv | 4 + .../configs/atwork_subtour_destination.csv | 2 - .../configs/atwork_subtour_destination.yaml | 4 + .../atwork_subtour_destination_coeffs.csv | 0 .../atwork_subtour_destination_sample.csv | 2 - .../configs/atwork_subtour_frequency.csv | 0 .../configs/atwork_subtour_frequency.yaml | 0 .../atwork_subtour_frequency_alternatives.csv | 0 ..._frequency_annotate_tours_preprocessor.csv | 0 .../atwork_subtour_frequency_coeffs.csv | 0 .../configs/auto_ownership.csv | 23 + .../configs/auto_ownership.yaml | 0 .../configs/auto_ownership_coeffs.csv | 76 + .../configs/cdap.yaml | 2 - .../cdap_fixed_relative_proportions.csv | 0 .../configs/cdap_indiv_and_hhsize1.csv | 16 + .../configs/cdap_interaction_coefficients.csv | 0 .../production_semcog/configs/constants.yaml | 123 + .../configs/destination_choice_size_terms.csv | 12 +- .../configs/free_parking.csv | 3 + .../configs/free_parking.yaml | 0 ..._parking_annotate_persons_preprocessor.csv | 4 + .../configs/free_parking_coeffs.csv | 0 .../configs/initialize_households.yaml | 3 - .../configs/initialize_landuse.yaml | 0 .../configs/joint_tour_composition.csv | 4 +- .../configs/joint_tour_composition.yaml | 0 ...ition_annotate_households_preprocessor.csv | 0 .../configs/joint_tour_composition_coeffs.csv | 0 .../configs/joint_tour_destination.csv | 0 .../configs/joint_tour_destination.yaml | 4 +- .../configs/joint_tour_destination_coeffs.csv | 0 .../configs/joint_tour_destination_sample.csv | 0 .../configs/joint_tour_frequency.csv | 0 .../configs/joint_tour_frequency.yaml | 0 .../joint_tour_frequency_alternatives.csv | 0 ...uency_annotate_households_preprocessor.csv | 2 +- .../configs/joint_tour_frequency_coeffs.csv | 0 .../configs/joint_tour_participation.csv | 0 .../configs/joint_tour_participation.yaml | 0 ...ion_annotate_participants_preprocessor.csv | 0 .../joint_tour_participation_coeffs.csv | 0 .../configs/joint_tour_scheduling.yaml | 0 ...scheduling_annotate_tours_preprocessor.csv | 0 .../configs/logging.yaml | 123 +- .../configs/mandatory_tour_frequency.csv | 6 + .../configs/mandatory_tour_frequency.yaml | 0 .../mandatory_tour_frequency_alternatives.csv | 0 .../mandatory_tour_frequency_coeffs.csv | 33 +- .../configs/mandatory_tour_scheduling.yaml | 0 ..._scheduling_annotate_alts_preprocessor.csv | 15 + .../configs/network_los.yaml | 39 + .../non_mandatory_tour_destination.csv | 8 + .../non_mandatory_tour_destination.yaml | 14 +- .../non_mandatory_tour_destination_coeffs.csv | 0 .../non_mandatory_tour_destination_sample.csv | 8 + .../configs/non_mandatory_tour_frequency.csv | 9 +- .../configs/non_mandatory_tour_frequency.yaml | 0 ..._mandatory_tour_frequency_alternatives.csv | 0 ...requency_annotate_persons_preprocessor.csv | 0 ...ry_tour_frequency_coeffs_PTYPE_DRIVING.csv | 0 ...atory_tour_frequency_coeffs_PTYPE_FULL.csv | 0 ...ry_tour_frequency_coeffs_PTYPE_NONWORK.csv | 0 ...atory_tour_frequency_coeffs_PTYPE_PART.csv | 0 ..._tour_frequency_coeffs_PTYPE_PRESCHOOL.csv | 0 ...ry_tour_frequency_coeffs_PTYPE_RETIRED.csv | 0 ...ory_tour_frequency_coeffs_PTYPE_SCHOOL.csv | 0 ...tour_frequency_coeffs_PTYPE_UNIVERSITY.csv | 0 ...ndatory_tour_frequency_extension_probs.csv | 0 .../non_mandatory_tour_scheduling.yaml | 9 +- ...scheduling_annotate_tours_preprocessor.csv | 0 ...parking_location_choice_at_university.yaml | 25 + .../configs/school_location - Copy.csv} | 0 .../configs/school_location.csv | 19 + .../configs/school_location.yaml | 0 .../configs/school_location_coeffs.csv | 0 .../configs/school_location_sample.csv | 0 .../configs/settings.yaml | 333 +- .../configs/settings_source.yaml | 158 + .../configs/shadow_pricing.yaml | 0 .../configs/stop_frequency.yaml | 20 +- .../configs/stop_frequency_alternatives.csv | 0 ..._frequency_annotate_tours_preprocessor.csv | 1 + .../configs/stop_frequency_atwork.csv | 13 + .../stop_frequency_coefficients_atwork.csv | 17 + .../stop_frequency_coefficients_eatout.csv | 33 + .../stop_frequency_coefficients_escort.csv | 28 + .../stop_frequency_coefficients_othdiscr.csv | 30 + .../stop_frequency_coefficients_othmaint.csv | 38 + .../stop_frequency_coefficients_school.csv | 21 + .../stop_frequency_coefficients_shopping.csv | 33 + .../stop_frequency_coefficients_social.csv | 33 + .../stop_frequency_coefficients_univ.csv | 20 + .../stop_frequency_coefficients_work.csv | 34 + .../configs/stop_frequency_eatout.csv | 53 + 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(78%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_atwork.csv (90%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_atwork.yaml (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_atwork_coeffs.csv (81%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_atwork_preprocessor.csv (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_joint.csv (52%) create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_joint_coeffs.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_nonmandatory_eatout.csv (63%) create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_nonmandatory_escort.csv (57%) create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_nonmandatory_othdiscr.csv (53%) create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_school.csv create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_school_coeffs.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_university.csv (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_university_coeffs.csv (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/tour_scheduling_work.csv (50%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/tour_scheduling_work_coeffs.csv create mode 100644 activitysim/examples/production_semcog/configs/transit_pass_ownership.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/transit_pass_ownership.yaml (100%) rename activitysim/examples/{prototype_semcog => production_semcog}/configs/transit_pass_ownership_coeffs.csv (65%) create mode 100644 activitysim/examples/production_semcog/configs/transit_pass_subsidy.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/transit_pass_subsidy.yaml (100%) create mode 100644 activitysim/examples/production_semcog/configs/transit_pass_subsidy_coeffs.csv create mode 100644 activitysim/examples/production_semcog/configs/trip_destination.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_destination.yaml (66%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_destination_annotate_trips_preprocessor.csv (72%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/trip_destination_coefficients.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_destination_sample.csv (75%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_mode_choice.yaml (80%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice_.csv create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients.csv create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_.csv create mode 100644 activitysim/examples/production_semcog/configs/trip_mode_choice_coefficients_template.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_mode_choice_coeffs.csv (54%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_purpose.yaml (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_purpose_and_destination.yaml (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_purpose_annotate_trips_preprocessor.csv (100%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_purpose_probs.csv (79%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_scheduling.yaml (91%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/trip_scheduling_probs.csv (87%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/university_location_zone_override.yaml rename activitysim/examples/{prototype_semcog => production_semcog}/configs/work_from_home.csv (76%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/work_from_home.yaml rename activitysim/examples/{prototype_semcog => production_semcog}/configs/work_from_home_coeffs.csv (94%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/workplace_location.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/workplace_location.yaml (97%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/configs/workplace_location_coeffs.csv create mode 100644 activitysim/examples/production_semcog/configs/workplace_location_sample.csv rename activitysim/examples/{prototype_semcog => production_semcog}/configs/write_trip_matrices.yaml (88%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs/write_trip_matrices_annotate_trips_preprocessor.csv (79%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs_mp/logging.yaml (98%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs_mp/settings.yaml (77%) mode change 100755 => 100644 rename activitysim/examples/{prototype_semcog => production_semcog}/configs_mp/shadow_pricing.yaml (55%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/data/households.csv create mode 100644 activitysim/examples/production_semcog/data/land_use.csv create mode 100644 activitysim/examples/production_semcog/data/maz_to_maz_bike.csv create mode 100644 activitysim/examples/production_semcog/data/maz_to_maz_walk.csv create mode 100644 activitysim/examples/production_semcog/data/mazs.csv create mode 100644 activitysim/examples/production_semcog/data/persons.csv rename activitysim/examples/{prototype_semcog => production_semcog}/data/skims.omx (55%) create mode 100644 activitysim/examples/production_semcog/data/taz.csv create mode 100644 activitysim/examples/production_semcog/extensions/__init__.py create mode 100644 activitysim/examples/production_semcog/extensions/parking_location_choice_at_university.py create mode 100644 activitysim/examples/production_semcog/extensions/stop_frequency_university_parking.py create mode 100644 activitysim/examples/production_semcog/extensions/university_location_zone_override.py rename activitysim/examples/{prototype_semcog => production_semcog}/scripts/semcog_crop.py (100%) rename activitysim/examples/{prototype_semcog => production_semcog}/simulation.py (93%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/test/configs/settings.yaml rename activitysim/examples/{prototype_semcog/configs => production_semcog/test/configs_mp}/network_los.yaml (51%) mode change 100755 => 100644 create mode 100644 activitysim/examples/production_semcog/test/configs_mp/settings.yaml rename activitysim/examples/{prototype_semcog/configs => production_semcog/test/configs_mp}/work_from_home_annotate_persons_preprocessor.csv (100%) rename activitysim/examples/{prototype_semcog => production_semcog/test}/output/cache/.gitignore (100%) rename activitysim/examples/{prototype_semcog => production_semcog/test}/output/log/.gitignore (100%) rename activitysim/examples/{prototype_semcog => production_semcog/test}/output/trace/.gitignore (100%) create mode 100644 activitysim/examples/production_semcog/test/regress/final_trips.csv create mode 100644 activitysim/examples/production_semcog/test/test_semcog.py delete mode 100644 activitysim/examples/prototype_semcog/change_log.txt delete mode 100755 activitysim/examples/prototype_semcog/configs/auto_ownership.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/constants.yaml delete mode 100755 activitysim/examples/prototype_semcog/configs/free_parking_annotate_persons_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_segments.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv delete mode 100644 activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/trip_destination.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/work_from_home.yaml delete mode 100755 activitysim/examples/prototype_semcog/configs/workplace_location.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv delete mode 100755 activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv delete mode 100644 activitysim/examples/prototype_semcog/data/households.csv delete mode 100644 activitysim/examples/prototype_semcog/data/land_use.csv delete mode 100644 activitysim/examples/prototype_semcog/data/persons.csv delete mode 100644 activitysim/examples/prototype_semcog/output/.gitignore delete mode 100644 activitysim/examples/prototype_semcog/scripts/reindex_household_ids.py delete mode 100644 activitysim/examples/prototype_semcog/test/configs/settings.yaml delete mode 100644 activitysim/examples/prototype_semcog/test/output/.gitignore delete mode 100644 activitysim/examples/prototype_semcog/test/output/cache/.gitignore delete mode 100644 activitysim/examples/prototype_semcog/test/output/trace/.gitignore delete mode 100644 activitysim/examples/prototype_semcog/test/regress/final_trips.csv delete mode 100644 activitysim/examples/prototype_semcog/test/test_semcog.py 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/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 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 similarity index 75% rename from activitysim/examples/prototype_semcog/configs/logging.yaml rename to activitysim/examples/production_semcog/configs/logging.yaml index 657507ee6..7b4f40795 100644 --- 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 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+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 +walk_ASC_auto_sufficient_work,-0.3839252643739825,F +walk_ASC_auto_sufficient_atwork,0.6888285413821493,F +bike_ASC_no_auto_eatout,1.3406128683316336,F +bike_ASC_no_auto_escort,-1.8776730763253895,F +bike_ASC_no_auto_othdiscr,0.09611870833163341,F +bike_ASC_no_auto_othmaint,0.37797232367461003,F +bike_ASC_no_auto_school,0.0,F +bike_ASC_no_auto_shopping,-0.32730557632538987,F +bike_ASC_no_auto_social,0.49312511833163325,F +bike_ASC_no_auto_univ,1.6230401393208198,F +bike_ASC_no_auto_work,1.9366521491848292,F +bike_ASC_no_auto_atwork,9.618481328604576,F +bike_ASC_auto_deficient_eatout,-0.7329167548814319,F +bike_ASC_auto_deficient_escort,-3.4918334798970845,F +bike_ASC_auto_deficient_othdiscr,0.743725505118568,F +bike_ASC_auto_deficient_othmaint,-0.48237037989708625,F +bike_ASC_auto_deficient_school,-0.6293683143338639,F +bike_ASC_auto_deficient_shopping,0.16024986010291362,F +bike_ASC_auto_deficient_social,1.4707152451185685,F +bike_ASC_auto_deficient_univ,1.3540126982889809,F +bike_ASC_auto_deficient_work,0.38853979137973904,F +bike_ASC_auto_deficient_atwork,-10.0,F +bike_ASC_auto_sufficient_eatout,-0.7818248753539668,F +bike_ASC_auto_sufficient_escort,-4.144424226894791,F +bike_ASC_auto_sufficient_othdiscr,-0.6529374753539668,F +bike_ASC_auto_sufficient_othmaint,-1.889618226894787,F +bike_ASC_auto_sufficient_school,-2.7124064651591198,F +bike_ASC_auto_sufficient_shopping,-1.6475261268947872,F +bike_ASC_auto_sufficient_social,-0.9495487753539665,F +bike_ASC_auto_sufficient_univ,0.24301176351955547,F +bike_ASC_auto_sufficient_work,-1.6346998140156905,F +bike_ASC_auto_sufficient_atwork,-0.0814361118561595,F +sr2_ASC_auto_deficient_eatout,0.2185010004632255,F +sr2_ASC_auto_deficient_escort,0.6179848495837379,F +sr2_ASC_auto_deficient_othdiscr,0.2904178004632254,F +sr2_ASC_auto_deficient_othmaint,0.8801375495837381,F +sr2_ASC_auto_deficient_school,-0.03039932776852437,F +sr2_ASC_auto_deficient_shopping,0.8620824095837377,F +sr2_ASC_auto_deficient_social,1.4861193004632254,F +sr2_ASC_auto_deficient_univ,-0.15502896636128363,F +sr2_ASC_auto_deficient_work,-0.33890161643754213,F +sr2_ASC_auto_deficient_atwork,-8.38235708427838,F +sr2_ASC_auto_sufficient_eatout,0.8512547065657425,F +sr2_ASC_auto_sufficient_escort,0.13915068657630886,F +sr2_ASC_auto_sufficient_othdiscr,0.48529532656574287,F +sr2_ASC_auto_sufficient_othmaint,0.39732951657630877,F +sr2_ASC_auto_sufficient_school,-1.9766014711035016,F +sr2_ASC_auto_sufficient_shopping,0.3368577565763089,F +sr2_ASC_auto_sufficient_social,0.5120516565657426,F +sr2_ASC_auto_sufficient_univ,-0.5409005150645907,F +sr2_ASC_auto_sufficient_work,-0.7529892043147851,F +sr2_ASC_auto_sufficient_atwork,-1.6460505204420481,F +sr3p_ASC_no_auto_eatout,2.152686073151814,F +sr3p_ASC_no_auto_escort,0.23921653518614996,F +sr3p_ASC_no_auto_othdiscr,2.1028552931518147,F +sr3p_ASC_no_auto_othmaint,1.2489578351861494,F +sr3p_ASC_no_auto_school,9.54825400101845,F +sr3p_ASC_no_auto_shopping,1.77235375518615,F +sr3p_ASC_no_auto_social,0.4269960731518134,F +sr3p_ASC_no_auto_univ,-0.06379806855317753,F +sr3p_ASC_no_auto_work,-1.657572555319718,F +sr3p_ASC_no_auto_atwork,-0.4128430336117345,F +sr3p_ASC_auto_deficient_eatout,0.1589796232393426,F +sr3p_ASC_auto_deficient_escort,0.3718187023338501,F +sr3p_ASC_auto_deficient_othdiscr,1.1600238632393436,F +sr3p_ASC_auto_deficient_othmaint,-0.5693861376661502,F +sr3p_ASC_auto_deficient_school,0.5014775649363076,F +sr3p_ASC_auto_deficient_shopping,0.7066361963338501,F +sr3p_ASC_auto_deficient_social,1.6136515632393427,F +sr3p_ASC_auto_deficient_univ,0.09124397105209198,F +sr3p_ASC_auto_deficient_work,-0.8287197437017003,F +sr3p_ASC_auto_deficient_atwork,-8.319829985728258,F +sr3p_ASC_auto_sufficient_eatout,0.6564145713994466,F +sr3p_ASC_auto_sufficient_escort,0.09144046140386361,F +sr3p_ASC_auto_sufficient_othdiscr,0.3980570213994468,F +sr3p_ASC_auto_sufficient_othmaint,0.07335432140386364,F +sr3p_ASC_auto_sufficient_school,-1.3721533940435513,F +sr3p_ASC_auto_sufficient_shopping,0.07128169640386361,F +sr3p_ASC_auto_sufficient_social,0.3157338313994467,F +sr3p_ASC_auto_sufficient_univ,-0.7218797348763317,F +sr3p_ASC_auto_sufficient_work,-1.3843196221151146,F +sr3p_ASC_auto_sufficient_atwork,-2.0671168042321697,F +walk_transit_ASC_no_auto_eatout,2.029089338207329,F +walk_transit_ASC_no_auto_escort,-2.2323603057337933,F +walk_transit_ASC_no_auto_othdiscr,1.6792310382073277,F +walk_transit_ASC_no_auto_othmaint,2.5491933942662075,F +walk_transit_ASC_no_auto_school,9.213473618097698,F +walk_transit_ASC_no_auto_shopping,2.091595394266208,F +walk_transit_ASC_no_auto_social,0.8169176382073282,F +walk_transit_ASC_no_auto_univ,2.0135478195801326,F +walk_transit_ASC_no_auto_work,5.14261357757391,F +walk_transit_ASC_no_auto_atwork,-0.8872481847137047,F +walk_transit_ASC_auto_deficient_eatout,0.812829769856199,F +walk_transit_ASC_auto_deficient_escort,-1.5469043973327803,F +walk_transit_ASC_auto_deficient_othdiscr,1.804881409856198,F +walk_transit_ASC_auto_deficient_othmaint,0.3540738026672199,F +walk_transit_ASC_auto_deficient_school,2.242793135312562,F +walk_transit_ASC_auto_deficient_shopping,2.566142702667222,F +walk_transit_ASC_auto_deficient_social,1.8262378798561996,F +walk_transit_ASC_auto_deficient_univ,4.900803013484653,F +walk_transit_ASC_auto_deficient_work,2.386253230190461,F +walk_transit_ASC_auto_deficient_atwork,-8.352730475537332,F +walk_transit_ASC_auto_sufficient_eatout,-3.343308797914361,F +walk_transit_ASC_auto_sufficient_escort,-6.228887825007517,F +walk_transit_ASC_auto_sufficient_othdiscr,-3.0369861279143593,F +walk_transit_ASC_auto_sufficient_othmaint,-2.841158022007515,F +walk_transit_ASC_auto_sufficient_school,-3.2895991904991178,F +walk_transit_ASC_auto_sufficient_shopping,-3.497720622007516,F +walk_transit_ASC_auto_sufficient_social,-2.5759940979143594,F +walk_transit_ASC_auto_sufficient_univ,-1.1666131817578116,F +walk_transit_ASC_auto_sufficient_work,-2.9189515976760094,F +walk_transit_ASC_auto_sufficient_atwork,-4.70624346069921,F +ridehail_ASC_no_auto_eatout,-1.8322254964003977,F +ridehail_ASC_no_auto_escort,-0.3125120531577249,F +ridehail_ASC_no_auto_othdiscr,-1.8322254964003977,F +ridehail_ASC_no_auto_othmaint,-0.3125120531577249,F +ridehail_ASC_no_auto_school,0.0,F +ridehail_ASC_no_auto_shopping,-0.3125120531577249,F +ridehail_ASC_no_auto_social,-1.8322254964003977,F +ridehail_ASC_no_auto_univ,-2.0,F +ridehail_ASC_no_auto_work,-6.033875454337849,F +ridehail_ASC_no_auto_atwork,-7.269004145817317,F +ridehail_ASC_auto_deficient_eatout,2.7986037739708376,F +ridehail_ASC_auto_deficient_escort,-4.659222701492694,F +ridehail_ASC_auto_deficient_othdiscr,2.7986037739708376,F +ridehail_ASC_auto_deficient_othmaint,-4.659222701492694,F +ridehail_ASC_auto_deficient_school,-8.0,F +ridehail_ASC_auto_deficient_shopping,-4.659222701492694,F +ridehail_ASC_auto_deficient_social,2.7986037739708376,F +ridehail_ASC_auto_deficient_univ,-4.57587934978779,F +ridehail_ASC_auto_deficient_work,-0.6927625574174675,F +ridehail_ASC_auto_deficient_atwork,-5.378092449952847,F +ridehail_ASC_auto_sufficient_eatout,1.3016339166215867,F +ridehail_ASC_auto_sufficient_escort,-3.717708848697935,F +ridehail_ASC_auto_sufficient_othdiscr,1.3016339166215867,F +ridehail_ASC_auto_sufficient_othmaint,-3.717708848697935,F +ridehail_ASC_auto_sufficient_school,-10.0,F +ridehail_ASC_auto_sufficient_shopping,-3.717708848697935,F +ridehail_ASC_auto_sufficient_social,1.3016339166215867,F +ridehail_ASC_auto_sufficient_univ,-3.6095625269189897,F +ridehail_ASC_auto_sufficient_work,-1.1838060217952593,F +ridehail_ASC_auto_sufficient_atwork,-1.0249904101125786,F +joint_walk_ASC_no_auto_all,0.15584223578259168,F +joint_walk_ASC_auto_deficient_all,0.9233175529322957,F +joint_walk_ASC_auto_sufficient_all,-2.331084062768546,F +joint_bike_ASC_no_auto_all,-1.7583826656688333,F +joint_bike_ASC_auto_deficient_all,-1.730334855321718,F +joint_bike_ASC_auto_sufficient_all,-5.5926750569216495,F +joint_sr2_ASC_no_auto_all,0.0,T +joint_sr2_ASC_auto_deficient_all,0.0,T +joint_sr2_ASC_auto_sufficient_all,0.0,T +joint_sr3p_ASC_no_auto_all,-0.842816521690442,F +joint_sr3p_ASC_auto_deficient_all,-1.07878787842315,F +joint_sr3p_ASC_auto_sufficient_all,-3.1712210090824624,F +joint_walk_transit_ASC_no_auto_all,-0.513721366632039,F +joint_walk_transit_ASC_auto_deficient_all,-2.351842334690234,F +joint_walk_transit_ASC_auto_sufficient_all,-8.60193441473402,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,-1.2537061510826404,F +joint_knr_transit_ASC_auto_sufficient_all,-8.112897077787881,F +joint_knr_transit_ASC_no_auto_all,1.108537399695504,F +joint_pnr_transit_ASC_auto_deficient_all,-1.911231256535309,F +joint_pnr_transit_ASC_auto_sufficient_all,-8.765350792242202,F +joint_pnr_transit_ASC_no_auto_all,0.0,T +knr_transit_ASC_auto_deficient_atwork,0.2091798070955826,F +knr_transit_ASC_auto_deficient_eatout,-0.735879161537163,F +knr_transit_ASC_auto_deficient_escort,-1.3895921757602858,F +knr_transit_ASC_auto_deficient_othdiscr,0.25617247846283664,F +knr_transit_ASC_auto_deficient_othmaint,0.511386024239715,F +knr_transit_ASC_auto_deficient_school,3.5976455194036117,F +knr_transit_ASC_auto_deficient_shopping,2.723454924239715,F +knr_transit_ASC_auto_deficient_social,0.27752894846283693,F +knr_transit_ASC_auto_deficient_univ,0.4040538964240499,F +knr_transit_ASC_auto_deficient_work,0.4316262292334842,F +knr_transit_ASC_auto_sufficient_atwork,-8.401027000000001,F +knr_transit_ASC_auto_sufficient_eatout,-4.170306985100459,F +knr_transit_ASC_auto_sufficient_escort,-6.808179208227433,F +knr_transit_ASC_auto_sufficient_othdiscr,-3.8639843151004585,F +knr_transit_ASC_auto_sufficient_othmaint,-3.4204494082274253,F +knr_transit_ASC_auto_sufficient_school,-2.6811653858341096,F +knr_transit_ASC_auto_sufficient_shopping,-4.07701200822743,F +knr_transit_ASC_auto_sufficient_social,-3.402992285100459,F +knr_transit_ASC_auto_sufficient_univ,-5.311815905141975,F +knr_transit_ASC_auto_sufficient_work,-5.078464133612999,F +knr_transit_ASC_no_auto_atwork,8.9209204607444,F +knr_transit_ASC_no_auto_eatout,0.8624477497730498,F +knr_transit_ASC_no_auto_escort,0.6589788615034985,F +knr_transit_ASC_no_auto_othdiscr,0.8624477497730498,F +knr_transit_ASC_no_auto_othmaint,0.6589788615034985,F +knr_transit_ASC_no_auto_school,11.252782407387176,F +knr_transit_ASC_no_auto_shopping,0.6589788615034985,F +knr_transit_ASC_no_auto_social,0.8624477497730498,F +knr_transit_ASC_no_auto_univ,-0.1368308740942435,F +knr_transit_ASC_no_auto_work,3.260097090750686,F +local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.0,F +local_ASC_school_univ,0.0,F +local_ASC_work,0.0,F +mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.0,F +mix_ASC_school_univ,0.0,F +mix_ASC_work,0.0,F +pnr_transit_ASC_auto_deficient_atwork,-998.8196,F +pnr_transit_ASC_auto_deficient_eatout,-0.4723885459758467,F +pnr_transit_ASC_auto_deficient_escort,-1.1248134304411364,F +pnr_transit_ASC_auto_deficient_othdiscr,-0.7522638459758466,F +pnr_transit_ASC_auto_deficient_othmaint,-0.2705390104411357,F +pnr_transit_ASC_auto_deficient_school,2.0540720380987447,F +pnr_transit_ASC_auto_deficient_shopping,-0.3895985104411358,F +pnr_transit_ASC_auto_deficient_social,0.4905248540241536,F +pnr_transit_ASC_auto_deficient_univ,2.3242889303868233,F +pnr_transit_ASC_auto_deficient_work,-0.18538837708471065,F +pnr_transit_ASC_auto_sufficient_atwork,-999.21466,F +pnr_transit_ASC_auto_sufficient_eatout,-4.08907753677699,F +pnr_transit_ASC_auto_sufficient_escort,-5.978381222654008,F +pnr_transit_ASC_auto_sufficient_othdiscr,-3.4981533767769926,F +pnr_transit_ASC_auto_sufficient_othmaint,-4.001904322654012,F +pnr_transit_ASC_auto_sufficient_school,-0.8561246162539882,F +pnr_transit_ASC_auto_sufficient_shopping,-3.548850322654009,F +pnr_transit_ASC_auto_sufficient_social,-3.73541742677699,F +pnr_transit_ASC_auto_sufficient_univ,0.1898781940098056,F +pnr_transit_ASC_auto_sufficient_work,-2.780285380301299,F +pnr_transit_ASC_no_auto_atwork,0.0,F +pnr_transit_ASC_no_auto_eatout,0.0,F +pnr_transit_ASC_no_auto_escort,0.0,F +pnr_transit_ASC_no_auto_othdiscr,0.0,F +pnr_transit_ASC_no_auto_othmaint,0.0,F +pnr_transit_ASC_no_auto_school,0.0,F +pnr_transit_ASC_no_auto_shopping,0.0,F +pnr_transit_ASC_no_auto_social,0.0,F +pnr_transit_ASC_no_auto_univ,0.0,F +pnr_transit_ASC_no_auto_work,0.0,F +premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.0,F +premium_ASC_school_univ,0.0,F +premium_ASC_work,0.0,F +schoolbus_ASC_auto_deficient_school,0.7376214587002142,F +schoolbus_ASC_auto_sufficient_school,-1.2613082018801485,F +schoolbus_ASC_no_auto_school,10.158616513444322,F +schoolbus_ASC_not_school,0.0,T +sr2_ASC_no_auto_atwork,-0.08515343095908816,F +sr2_ASC_no_auto_eatout,1.3692494203881804,F +sr2_ASC_no_auto_escort,1.6083870840920869,F +sr2_ASC_no_auto_othdiscr,1.3692494203881804,F +sr2_ASC_no_auto_othmaint,1.6083870840920869,F +sr2_ASC_no_auto_school,8.12416756661324,F +sr2_ASC_no_auto_shopping,1.6083870840920869,F +sr2_ASC_no_auto_social,1.3692494203881804,F +sr2_ASC_no_auto_univ,0.4508430059869959,F +sr2_ASC_no_auto_work,-0.1523914463096275,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,-7.0,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,6.1148062378316315,F +joint_ridehail_ASC_auto_sufficient_all,7.215420846651934,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,0.964270028396554,F +walk_transit_ASC_inc_low_escort,1.019046214958657,F +walk_transit_ASC_inc_low_othdiscr,0.964270028396554,F +walk_transit_ASC_inc_low_othmaint,1.019046214958657,F +walk_transit_ASC_inc_low_school,1.133297715093305,F +walk_transit_ASC_inc_low_shopping,1.019046214958657,F +walk_transit_ASC_inc_low_social,0.964270028396554,F +walk_transit_ASC_inc_low_univ,1.8037811633554368,F +walk_transit_ASC_inc_low_work,2.3136169139529006,F +walk_transit_ASC_inc_low_atwork,1.3809880998990771,F +walk_transit_ASC_inc_mid_eatout,0.581207139502496,F +walk_transit_ASC_inc_mid_escort,0.9834778460656279,F +walk_transit_ASC_inc_mid_othdiscr,0.581207139502496,F +walk_transit_ASC_inc_mid_othmaint,0.9834778460656279,F +walk_transit_ASC_inc_mid_school,-0.010484902645769378,F +walk_transit_ASC_inc_mid_shopping,0.9834778460656279,F +walk_transit_ASC_inc_mid_social,0.581207139502496,F +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 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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 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 100644 --- 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 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+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 +coef_sov_ASC_walk_othmaint,-1.607249092,F +coef_sov_ASC_walk_school,-9.0,F +coef_sov_ASC_walk_shopping,-1.777249092,F +coef_sov_ASC_walk_social,-1.403603919,F +coef_sov_ASC_walk_univ,-1.0456165393073815,F +coef_sov_ASC_walk_work,-1.8119810474039342,F +coef_sov_ASC_taxi_atwork,-7.0,F +coef_sov_ASC_taxi_eatout,0.0,F +coef_sov_ASC_taxi_escort,0.0,F +coef_sov_ASC_taxi_othdiscr,0.0,F +coef_sov_ASC_taxi_othmaint,0.0,F +coef_sov_ASC_taxi_school,-5.65,F +coef_sov_ASC_taxi_shopping,0.0,F +coef_sov_ASC_taxi_social,0.0,F +coef_sov_ASC_taxi_univ,-6.65,F +coef_sov_ASC_taxi_work,-7.0,F +coef_sov_ASC_tnc_single_atwork,-7.0,F +coef_sov_ASC_tnc_single_eatout,0.0,F +coef_sov_ASC_tnc_single_escort,0.0,F +coef_sov_ASC_tnc_single_othdiscr,0.0,F +coef_sov_ASC_tnc_single_othmaint,0.0,F +coef_sov_ASC_tnc_single_school,-5.65,F +coef_sov_ASC_tnc_single_shopping,0.0,F +coef_sov_ASC_tnc_single_social,0.0,F +coef_sov_ASC_tnc_single_univ,-6.65,F +coef_sov_ASC_tnc_single_work,-7.0,F +coef_sov_ASC_tnc_shared_atwork,-7.0,F +coef_sov_ASC_tnc_shared_eatout,0.0,F +coef_sov_ASC_tnc_shared_escort,0.0,F +coef_sov_ASC_tnc_shared_othdiscr,0.0,F +coef_sov_ASC_tnc_shared_othmaint,0.0,F +coef_sov_ASC_tnc_shared_school,-5.65,F +coef_sov_ASC_tnc_shared_shopping,0.0,F +coef_sov_ASC_tnc_shared_social,0.0,F +coef_sov_ASC_tnc_shared_univ,-6.65,F +coef_sov_ASC_tnc_shared_work,-7.0,F +coef_sr2_ASC_sov_atwork,-1.6354716768872632,F +coef_sr2_ASC_sov_eatout,-0.7911641055933043,F +coef_sr2_ASC_sov_escort,-0.511497347719579,F +coef_sr2_ASC_sov_othdiscr,-0.7911641055933043,F +coef_sr2_ASC_sov_othmaint,-0.511497347719579,F +coef_sr2_ASC_sov_school,0.05091266147739875,F +coef_sr2_ASC_sov_shopping,-0.511497347719579,F +coef_sr2_ASC_sov_social,-0.7911641055933043,F +coef_sr2_ASC_sov_univ,-1.0193647729228377,F +coef_sr2_ASC_sov_work,-0.505150185,F +coef_sr2_ASC_walk_atwork,-4.147994410593764,F +coef_sr2_ASC_walk_eatout,-0.44971948,F +coef_sr2_ASC_walk_escort,-3.3641230274416705,F +coef_sr2_ASC_walk_othdiscr,-0.39971948,F +coef_sr2_ASC_walk_othmaint,-1.904123027441671,F +coef_sr2_ASC_walk_school,-0.3402487620181724,F +coef_sr2_ASC_walk_shopping,-1.4241230274416707,F +coef_sr2_ASC_walk_social,-1.16971948,F +coef_sr2_ASC_walk_univ,-0.1767642334449623,F +coef_sr2_ASC_walk_work,-0.71543715,F +coef_sr2_ASC_taxi_atwork,0.0,F +coef_sr2_ASC_taxi_eatout,0.0,F +coef_sr2_ASC_taxi_escort,0.0,F +coef_sr2_ASC_taxi_othdiscr,0.0,F +coef_sr2_ASC_taxi_othmaint,0.0,F +coef_sr2_ASC_taxi_school,0.0,F +coef_sr2_ASC_taxi_shopping,0.0,F +coef_sr2_ASC_taxi_social,0.0,F +coef_sr2_ASC_taxi_univ,0.0,F +coef_sr2_ASC_taxi_work,0.0,F +coef_sr2_ASC_tnc_single_atwork,0.0,F +coef_sr2_ASC_tnc_single_eatout,0.0,F +coef_sr2_ASC_tnc_single_escort,0.0,F +coef_sr2_ASC_tnc_single_othdiscr,0.0,F +coef_sr2_ASC_tnc_single_othmaint,0.0,F +coef_sr2_ASC_tnc_single_school,0.0,F +coef_sr2_ASC_tnc_single_shopping,0.0,F +coef_sr2_ASC_tnc_single_social,0.0,F +coef_sr2_ASC_tnc_single_univ,0.0,F +coef_sr2_ASC_tnc_single_work,0.0,F +coef_sr2_ASC_tnc_shared_atwork,0.0,F +coef_sr2_ASC_tnc_shared_eatout,0.0,F +coef_sr2_ASC_tnc_shared_escort,0.0,F +coef_sr2_ASC_tnc_shared_othdiscr,0.0,F +coef_sr2_ASC_tnc_shared_othmaint,0.0,F +coef_sr2_ASC_tnc_shared_school,0.0,F +coef_sr2_ASC_tnc_shared_shopping,0.0,F +coef_sr2_ASC_tnc_shared_social,0.0,F +coef_sr2_ASC_tnc_shared_univ,0.0,F +coef_sr2_ASC_tnc_shared_work,0.0,F +coef_sr3p_ASC_sov_atwork,-1.7323098448413663,F +coef_sr3p_ASC_sov_eatout,-1.4759569469999998,F +coef_sr3p_ASC_sov_escort,-1.1080696848858578,F +coef_sr3p_ASC_sov_othdiscr,-1.4759569469999998,F +coef_sr3p_ASC_sov_othmaint,-1.1080696848858578,F +coef_sr3p_ASC_sov_school,-1.832414661,F +coef_sr3p_ASC_sov_shopping,-1.1080696848858578,F +coef_sr3p_ASC_sov_social,-1.4759569469999998,F +coef_sr3p_ASC_sov_univ,-1.158705551593187,F +coef_sr3p_ASC_sov_work,-0.2592363978200444,F +coef_sr3p_ASC_sr2_atwork,-0.6488919856009046,F +coef_sr3p_ASC_sr2_eatout,-0.724799547843461,F +coef_sr3p_ASC_sr2_escort,-0.6506202677776334,F +coef_sr3p_ASC_sr2_othdiscr,-1.0147995478434608,F +coef_sr3p_ASC_sr2_othmaint,-0.6706202677776334,F +coef_sr3p_ASC_sr2_school,-1.094024963,F +coef_sr3p_ASC_sr2_shopping,-0.46062026777763343,F +coef_sr3p_ASC_sr2_social,-0.564799547843461,F +coef_sr3p_ASC_sr2_univ,-0.7916773814959738,F +coef_sr3p_ASC_sr2_work,-0.34403688400801846,F +coef_sr3p_ASC_walk_atwork,-3.3513939359485425,F +coef_sr3p_ASC_walk_eatout,0.012300988999999998,F +coef_sr3p_ASC_walk_escort,-2.12241919,F +coef_sr3p_ASC_walk_othdiscr,-0.467699011,F +coef_sr3p_ASC_walk_othmaint,-1.07241919,F +coef_sr3p_ASC_walk_school,-0.8186915641890801,F +coef_sr3p_ASC_walk_shopping,-1.62241919,F +coef_sr3p_ASC_walk_social,-0.25769901100000003,F +coef_sr3p_ASC_walk_univ,-0.5738777678640601,F +coef_sr3p_ASC_walk_work,-1.0513663652532266,F +coef_sr3p_ASC_taxi_atwork,0.0,F +coef_sr3p_ASC_taxi_eatout,0.0,F +coef_sr3p_ASC_taxi_escort,0.0,F +coef_sr3p_ASC_taxi_othdiscr,0.0,F +coef_sr3p_ASC_taxi_othmaint,0.0,F +coef_sr3p_ASC_taxi_school,0.0,F +coef_sr3p_ASC_taxi_shopping,0.0,F +coef_sr3p_ASC_taxi_social,0.0,F +coef_sr3p_ASC_taxi_univ,0.0,F +coef_sr3p_ASC_taxi_work,0.0,F +coef_sr3p_ASC_tnc_single_atwork,0.0,F +coef_sr3p_ASC_tnc_single_eatout,0.0,F +coef_sr3p_ASC_tnc_single_escort,0.0,F +coef_sr3p_ASC_tnc_single_othdiscr,0.0,F +coef_sr3p_ASC_tnc_single_othmaint,0.0,F +coef_sr3p_ASC_tnc_single_school,0.0,F +coef_sr3p_ASC_tnc_single_shopping,0.0,F +coef_sr3p_ASC_tnc_single_social,0.0,F +coef_sr3p_ASC_tnc_single_univ,0.0,F +coef_sr3p_ASC_tnc_single_work,0.0,F +coef_sr3p_ASC_tnc_shared_atwork,0.0,F +coef_sr3p_ASC_tnc_shared_eatout,0.0,F +coef_sr3p_ASC_tnc_shared_escort,0.0,F +coef_sr3p_ASC_tnc_shared_othdiscr,0.0,F +coef_sr3p_ASC_tnc_shared_othmaint,0.0,F +coef_sr3p_ASC_tnc_shared_school,0.0,F +coef_sr3p_ASC_tnc_shared_shopping,0.0,F +coef_sr3p_ASC_tnc_shared_social,0.0,F +coef_sr3p_ASC_tnc_shared_univ,0.0,F +coef_sr3p_ASC_tnc_shared_work,0.0,F +coef_walk_transit_ASC_sr2_atwork,-38.0,F +coef_walk_transit_ASC_sr2_eatout,-2.69150151820353,F +coef_walk_transit_ASC_sr2_escort,-2.2831062557576907,F +coef_walk_transit_ASC_sr2_othdiscr,-2.69150151820353,F +coef_walk_transit_ASC_sr2_othmaint,-2.2831062557576907,F +coef_walk_transit_ASC_sr2_school,-0.560423807853411,F +coef_walk_transit_ASC_sr2_shopping,-2.2831062557576907,F +coef_walk_transit_ASC_sr2_social,-2.69150151820353,F +coef_walk_transit_ASC_sr2_univ,-1.712143171121282,F +coef_walk_transit_ASC_sr2_work,-2.0169880749952127,F +coef_walk_transit_ASC_sr3p_atwork,5.943781468568362,F +coef_walk_transit_ASC_sr3p_eatout,-3.2919601040677207,F +coef_walk_transit_ASC_sr3p_escort,-3.486763969245068,F +coef_walk_transit_ASC_sr3p_othdiscr,-3.2919601040677207,F +coef_walk_transit_ASC_sr3p_othmaint,-3.486763969245068,F +coef_walk_transit_ASC_sr3p_school,-1.5182111238163243,F +coef_walk_transit_ASC_sr3p_shopping,-3.486763969245068,F +coef_walk_transit_ASC_sr3p_social,-3.2919601040677207,F +coef_walk_transit_ASC_sr3p_univ,-3.1941596022459335,F +coef_walk_transit_ASC_sr3p_work,-2.0849977660366132,F +coef_walk_transit_ASC_walk_atwork,9.903123590796667,F +coef_walk_transit_ASC_walk_eatout,6.453777205471383,F +coef_walk_transit_ASC_walk_escort,5.149096783652612,F +coef_walk_transit_ASC_walk_othdiscr,6.453777205471383,F +coef_walk_transit_ASC_walk_othmaint,5.149096783652612,F +coef_walk_transit_ASC_walk_school,1.9267509510461833,F +coef_walk_transit_ASC_walk_shopping,5.149096783652612,F +coef_walk_transit_ASC_walk_social,6.453777205471383,F +coef_walk_transit_ASC_walk_univ,2.5555595204205757,F +coef_walk_transit_ASC_walk_work,2.472534326341435,F +coef_walk_transit_ASC_taxi_atwork,-26.0,F +coef_walk_transit_ASC_taxi_eatout,-6.0,F +coef_walk_transit_ASC_taxi_escort,-6.0,F +coef_walk_transit_ASC_taxi_othdiscr,-6.0,F +coef_walk_transit_ASC_taxi_othmaint,-6.0,F +coef_walk_transit_ASC_taxi_school,-6.0,F +coef_walk_transit_ASC_taxi_shopping,-6.0,F +coef_walk_transit_ASC_taxi_social,-6.0,F +coef_walk_transit_ASC_taxi_univ,-8.0,F +coef_walk_transit_ASC_taxi_work,-6.0,F +coef_walk_transit_ASC_tnc_single_atwork,-26.18169468050849,F +coef_walk_transit_ASC_tnc_single_eatout,-2.9088288021266377,F +coef_walk_transit_ASC_tnc_single_escort,-3.013007945719428,F +coef_walk_transit_ASC_tnc_single_othdiscr,-2.9088288021266377,F +coef_walk_transit_ASC_tnc_single_othmaint,-3.013007945719428,F +coef_walk_transit_ASC_tnc_single_school,-6.0,F +coef_walk_transit_ASC_tnc_single_shopping,-3.013007945719428,F +coef_walk_transit_ASC_tnc_single_social,-2.9088288021266377,F +coef_walk_transit_ASC_tnc_single_univ,-4.7194566242421185,F +coef_walk_transit_ASC_tnc_single_work,-1.6131819481180394,F +coef_walk_transit_ASC_tnc_shared_atwork,-24.0,F +coef_walk_transit_ASC_tnc_shared_eatout,-6.0,F +coef_walk_transit_ASC_tnc_shared_escort,-2.4508351572397338,F +coef_walk_transit_ASC_tnc_shared_othdiscr,-6.0,F +coef_walk_transit_ASC_tnc_shared_othmaint,-2.4508351572397338,F +coef_walk_transit_ASC_tnc_shared_school,-6.0,F +coef_walk_transit_ASC_tnc_shared_shopping,-2.4508351572397338,F +coef_walk_transit_ASC_tnc_shared_social,-6.0,F +coef_walk_transit_ASC_tnc_shared_univ,-8.0,F +coef_walk_transit_ASC_tnc_shared_work,-1.901181988033481,F +coef_pnr_ASC_taxi_atwork,0.0,F +coef_pnr_ASC_taxi_eatout,-4.0,F +coef_pnr_ASC_taxi_escort,-4.0,F +coef_pnr_ASC_taxi_othdiscr,-4.0,F +coef_pnr_ASC_taxi_othmaint,-4.0,F +coef_pnr_ASC_taxi_school,-2.0,F +coef_pnr_ASC_taxi_shopping,-4.0,F +coef_pnr_ASC_taxi_social,-4.0,F +coef_pnr_ASC_taxi_univ,-4.0,F +coef_pnr_ASC_taxi_work,-18.0,F +coef_pnr_ASC_tnc_single_atwork,0.0,F +coef_pnr_ASC_tnc_single_eatout,-6.0,F +coef_pnr_ASC_tnc_single_escort,-4.0,F +coef_pnr_ASC_tnc_single_othdiscr,-6.0,F +coef_pnr_ASC_tnc_single_othmaint,-4.0,F +coef_pnr_ASC_tnc_single_school,-2.0,F +coef_pnr_ASC_tnc_single_shopping,-4.0,F +coef_pnr_ASC_tnc_single_social,-6.0,F +coef_pnr_ASC_tnc_single_univ,-1.3402368194425927,F +coef_pnr_ASC_tnc_single_work,-18.0,F +coef_pnr_ASC_tnc_shared_atwork,0.0,F +coef_pnr_ASC_tnc_shared_eatout,-6.0,F +coef_pnr_ASC_tnc_shared_escort,-1.8515688444695777,F +coef_pnr_ASC_tnc_shared_othdiscr,-6.0,F +coef_pnr_ASC_tnc_shared_othmaint,-1.8515688444695777,F +coef_pnr_ASC_tnc_shared_school,-2.0,F +coef_pnr_ASC_tnc_shared_shopping,-1.8515688444695777,F +coef_pnr_ASC_tnc_shared_social,-6.0,F +coef_pnr_ASC_tnc_shared_univ,-0.9555320109705572,F +coef_pnr_ASC_tnc_shared_work,-20.0,F +coef_knr_ASC_taxi_atwork,-4.0,F +coef_knr_ASC_taxi_eatout,-2.0,F +coef_knr_ASC_taxi_escort,-4.0,F +coef_knr_ASC_taxi_othdiscr,-2.0,F +coef_knr_ASC_taxi_othmaint,-4.0,F +coef_knr_ASC_taxi_school,-2.0,F +coef_knr_ASC_taxi_shopping,-4.0,F +coef_knr_ASC_taxi_social,-2.0,F +coef_knr_ASC_taxi_univ,-4.0,F +coef_knr_ASC_taxi_work,-6.0,F +coef_knr_ASC_tnc_single_atwork,-6.0,F +coef_knr_ASC_tnc_single_eatout,0.4833518998809241,F +coef_knr_ASC_tnc_single_escort,-4.0,F +coef_knr_ASC_tnc_single_othdiscr,0.4833518998809241,F +coef_knr_ASC_tnc_single_othmaint,-4.0,F +coef_knr_ASC_tnc_single_school,-4.0,F +coef_knr_ASC_tnc_single_shopping,-4.0,F +coef_knr_ASC_tnc_single_social,0.4833518998809241,F +coef_knr_ASC_tnc_single_univ,-4.0,F +coef_knr_ASC_tnc_single_work,-6.0,F +coef_knr_ASC_tnc_shared_atwork,-6.0,F +coef_knr_ASC_tnc_shared_eatout,-2.0,F +coef_knr_ASC_tnc_shared_escort,-6.0,F +coef_knr_ASC_tnc_shared_othdiscr,-2.0,F +coef_knr_ASC_tnc_shared_othmaint,-6.0,F +coef_knr_ASC_tnc_shared_school,-4.0,F +coef_knr_ASC_tnc_shared_shopping,-6.0,F +coef_knr_ASC_tnc_shared_social,-2.0,F +coef_knr_ASC_tnc_shared_univ,-4.0,F +coef_knr_ASC_tnc_shared_work,-4.0,F +coef_ride_hail_ASC_sr2_atwork,-5.454019246971898,F +coef_ride_hail_ASC_sr2_eatout,-2.7940550122485104,F +coef_ride_hail_ASC_sr2_escort,-6.155332876194065,F +coef_ride_hail_ASC_sr2_othdiscr,-2.7940550122485104,F +coef_ride_hail_ASC_sr2_othmaint,-6.155332876194065,F +coef_ride_hail_ASC_sr2_school,-2.0,F +coef_ride_hail_ASC_sr2_shopping,-6.155332876194065,F +coef_ride_hail_ASC_sr2_social,-2.7940550122485104,F +coef_ride_hail_ASC_sr2_univ,-8.0,F +coef_ride_hail_ASC_sr2_work,-2.3519779180292857,F +coef_ride_hail_ASC_sr3p_atwork,-4.187228123359013,F +coef_ride_hail_ASC_sr3p_eatout,-2.2736279720757144,F +coef_ride_hail_ASC_sr3p_escort,-7.88411520414933,F +coef_ride_hail_ASC_sr3p_othdiscr,-2.2736279720757144,F +coef_ride_hail_ASC_sr3p_othmaint,-7.88411520414933,F +coef_ride_hail_ASC_sr3p_school,-2.0,F +coef_ride_hail_ASC_sr3p_shopping,-7.88411520414933,F +coef_ride_hail_ASC_sr3p_social,-2.2736279720757144,F +coef_ride_hail_ASC_sr3p_univ,-8.0,F +coef_ride_hail_ASC_sr3p_work,-4.015431891692123,F +coef_ride_hail_ASC_walk_atwork,-4.532888086258602,F +coef_ride_hail_ASC_walk_eatout,2.043672436692793,F +coef_ride_hail_ASC_walk_escort,-1.805366437835127,F +coef_ride_hail_ASC_walk_othdiscr,2.043672436692793,F +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 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-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 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+othdiscr,True,45,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +othdiscr,True,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +othdiscr,True,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +othdiscr,True,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +othdiscr,True,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +othdiscr,True,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +othdiscr,True,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +othdiscr,True,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +othdiscr,True,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +othdiscr,True,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +othdiscr,True,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +othdiscr,True,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +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,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 -escort,TRUE,20,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0630023942882305,0.3397940588767672,0.25870023779066315,0.17760641670455904,0.11283443634811303,0.04806245599166703,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,20,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12560993068803886,0.14275476926063188,0.15989960783322488,0.16388569048794663,0.14991784553233295,0.13595000057671927,0.12198215562110559,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,20,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -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 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-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 -escort,TRUE,27,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13023089022400866,0.8053988566875924,0.016127893204456404,0.016083707754592842,0.013401629232188027,0.010719550709783211,0.008037472187378398,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,27,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004256896296630929,0.009246887493529287,0.21591695644910233,0.18934039000932226,0.16276382356954225,0.13618725712976223,0.1096106906899822,0.08303412425020218,0.05645755781042213,0.029880991370642086,0.003304424930862062,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.016228927127358597,0.3333333333333333,0.6504377395393081,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.533792440291268,0.46620755970873196,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07397406559413638,0.7370597675968947,0.1889661668089689,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.472492983314563,0.3333333333333333,0.19417368335210364,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.48969361016894036,0.5047835007226725,0.00552288910838719,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02996762765509789,0.8643501983316954,0.10568217401320677,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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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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