Global-local path choice model

Python code for the estimation of a link-based route choice model with decomposed utilities (a global-local model)

Paper

For more details, please see the paper

Oyama, Y. (2024) Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of visually perceived attributes. Transportation Research Part A: Policy and Practice 181: 103998 (Open Access).

If you find this code useful, please cite the paper:

@article{oyama2024globalocal,
  title = {Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of visually perceived attributes},
  journal = {Transportation Research Part A: Policy and Practice},
  volume = {181},
  pages = {103998},
  year = {2024},
  issn = {0965-8564},
  doi = {https://doi.org/10.1016/j.tra.2024.103998},
  url = {https://www.sciencedirect.com/science/article/pii/S0965856424000466},
  author = {Yuki Oyama},
}

Synthetic Data

I have prepared synthetic data generated in the Sioux Falls network. Two synthetic datasets are available in the data folder:

• data_G0.csv: simulated by a global model, i.e., the original recursive logit model
• data_L0.csv: simulated by a global-local model, wherein capacity is assumed to have a local impact.

You can specify data to use within the run_estimation.py code.

Corresponding part

# for the prepared synthetic dataset: choose
        # "data_G0.csv" for data generated by a global model
        # "data_L0.csv" for data generated by a local model
    obs_data = pd.read_csv(os.path.join(data_dir, 'data_L0.csv'))

Quick Start for Estimation

Estimate a global-local model with both global and local attributes by specifyning vars_g (global attribute names) and vars_l with the initial values, upper and lower bounds.

python run_estimation.py --vars_g "length" --init_beta_g -1 --lb_g None --ub_g 0 --vars_l "caplen" --init_beta_l 0 --lb_l None --ub_l None

Estimate a global-local model with only global attributes (corresponding the original recursive logit model)

python run_estimation.py --vars_g "length" "caplen" --init_beta_g -1 -1 --lb_g None None --ub_g 0 None

Validation and Bootstrapping

For cross-validation, split the data into estimation and validation samples by setting test ratio greater than zero.

python run_estimation.py --n_samples 10 --test_ratio 0.2

For bootstrapping, resample K sets of observations and estimate the model with each of them, by setting n_samples greater than 1 and test ratio to zero.

python run_estimation.py --n_samples 200 --test_ratio 0 --isBootstrap True

Note: this returns only the estimation results for K observations, so you should analyze the standard error or confidential intervals from the results afterwards.