This is the repository associated with the paper "Forecasting the evolution of fast-changing transportation networks using machine learning" Nature Communications (2022)
Repository structure:
- data/ --
- raw_usair_data/ -- data folder to put raw US air transportation data (download at https://doi.org/10.21985/n2-9r77-p344)
- cleaned_brazilbus_data/ -- data folder to put raw Brazil bus transportation data (download at https://doi.org/10.21985/n2-9r77-p344)
- features/ -- data folder to put calculated topological features (running raw2features)
- results/ -- data folder to put model output (running classification and longterm_prediction)
- notebooks/ -- notebooks for visulizations
- src/ -- other supporting codes for analysis and visualization
Steps:
- env.txt -- create conda environment (
$ conda create --name <env> --file <this file>) - 0_raw2features_usair.py -- building feature matrix for the models(
$ python3 0_raw2features_usair.py) - 0_raw2features_brazilbus.py -- building feature matrix for the models (
$ python3 0_raw2features_brazilbus.py) - 1_classification.py -- running different models for results in Fig.3, Fig.4, Fig.5 (
$ python3 1_classification.py) - 2_longterm_prediction.py -- running long term prediction for results in Fig.6a (
$ python3 2_longterm_prediction.py)