RecursiveRouteChoice

RecursiveRouteChoice is an efficient Python implementation of the recursive logit model for route choice. This kind of model was first developed in Fosgerau, Frejinger & Karlstrom 2013

This implementation contains both prediction and estimation models. See the docs for a more detailed introduction. Here is a quick start overview.

Installation

(This was all written back in 2020, and python packaging workflow tools have come a long way since then, more modern workflow tools like pixi, rye, pdm etc could be considered. )

The current most direct method of installation is to clone the repository and install using pip:

python -m virtualenv venv
python -m pip install -r requirements_strict_2024.txt

Example Usage

Note the tntp file here is not included in the repository data, download separately from TransportationNetworks

import numpy as np
from recursiveRouteChoice.data_loading import load_tntp_node_formulation
from recursiveRouteChoice import RecursiveLogitModelPrediction, ModelDataStruct, \
RecursiveLogitModelEstimation, optimisers

# DATA
network_file = "SiouxFalls_net.tntp"
node_max = 24  # from network file

data_list, data_list_names = load_tntp_node_formulation(
    network_file, columns_to_extract=["length", "capacity"], sparse_format=False)

distances = data_list[0]

incidence_mat = (distances > 0).astype(int)
network_struct = ModelDataStruct(data_list, incidence_mat)

beta_sim = np.array([-0.8, -0.00015])
model = RecursiveLogitModelPrediction(network_struct,
                                      initial_beta=beta_sim, mu=1)
print("Linear system size", model.get_exponential_utility_matrix().shape)

orig_indices = np.arange(0, node_max, 1)
# dest_indices = (orig_indices + 5) % node_max
dest_indices = np.arange(0, node_max, 1)  # sample every OD pair
obs_per_pair = 1
print(f"Generating {obs_per_pair * len(orig_indices) * len(dest_indices)} obs total per "
      f"configuration")
seed = 42
obs = model.generate_observations(origin_indices=orig_indices, dest_indices=dest_indices,
                                  num_obs_per_pair=obs_per_pair, iter_cap=2000, rng_seed=seed)

optimiser = optimisers.ScipyOptimiser(method='l-bfgs-b')
beta_est_init = [-5, -0.00001]
model_est = RecursiveLogitModelEstimation(network_struct, observations_record=obs,
                                          initial_beta=beta_est_init, mu=1,
                                          optimiser=optimiser)

beta = model_est.solve_for_optimal_beta(verbose=False)

print(f"beta expected: [{beta_sim[0]:6.4f}, {beta_sim[1]:6.4f}],"
              f" beta_actual: [{beta[0]:6.4f}, {beta[1]:6.4f}]")