TrajGAIL

Introduction

Generative model for urban vehicle trajectories based on Deep Learning This repository include implementations of :

• Markov Mobility Chain Model for next location prediction (Gambs et al. 2012)
• RNN based trajectory generator (Choi et al. 2018)
• MaxEnt inverse reinforcement learning (Ziebart et al. 2008)
• TrajGAIL based on Generative Adversarial Imitation Learning (Ho et al. 2016, Choi et al. 2020)
• ShortestPath World (MDP for routing imitations)

Citations

If you use this code for your research, please cite our paper.

@article{choi2021trajgail,
  title={TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning},
  author={Choi, Seongjin and Kim, Jiwon and Yeo, Hwasoo},
  journal={Transportation Research Part C: Emerging Technologies},
  volume={128},
  pages={103091},
  year={2021},
  publisher={Elsevier}
}

Data availability

Due to the public availability issue of taxi data of Gangnam District, it is not possible to upload the taxi data.

The available data is a virtual vehicle trajectory data generated by AIMSUN shortest path routing engine.

Below figure shows the network configuration.

Requirements

python>3.7

required python packages in requirement.txt

pip install -r requirement.txt

How to Run

To run Behavior Cloning MMC Test

python scripts/behavior_clone/run_bc_rnn.py

To run Behavior Cloning RNN Test

python scripts/behavior_clone/run_bc_rnn.py

To run MaxEnt IRL

python scripts/irl/demo_shortestpath.py

To run TrajGAIL

python scripts/gail/run_gail.py