Markovian traffic equilibrium assignment based on network generalized extreme value model

Python code for a link-based stochastic user equilibrium based on the network-GEV model and its solution algorithms for both primal and dual problems.

Paper

For more details, please see our paper that has been published in Transportation Research Part B: Methodological.

Oyama, Y., Hara, Y., Akamatsu, T. (2022) Markovian traffic equilibrium assignment based on network generalized extreme value model. Transportation Research Part B: Methodological 155: 135-159.

Please cite this paper if you find this code useful:

@article{oyama2022markovian,
  title={Markovian traffic equilibrium assignment based on network generalized extreme value model},
  author={Oyama, Yuki and Hara, Yusuke and Akamatsu, Takashi},
  journal={Transportation Research Part B: Methodological},
  volume={155},
  pages={135--159},
  year={2022},
  publisher={Elsevier}
}

Example for Quick Start

Solve the NGEV equilibrium with Accelerated Gradient Projection method (the dual algorithm) in the Sioux Falls network.

python run.py --model_name 'NGEVMCA' --optimizers 'AGDBT'

Model options

Loading model options ('syntax' for parameter specification):

• Logit-based Dial assignment (LogitDial)
• Logit-based Markov chain assignment (LogitMCA)
• NGEV-based Dial assignment (NGEVDial)
• NGEV-based Markov chain assignment (NGEVMCA)
• Probit assignment (Probit)

Solution algorithm options:

• Method of Successive Averages (MSA)
• Partial Linearization (PL)
• Gradient Projection (GP/GD)
• Accelerated Gradient Projection (AGP/AGD)