Awesome
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.
<img src="./img/network.jpg" width="400" height="400">Requirements
python>3.7
required python packages in requirement.txt
<Bash terminal>pip install -r requirement.txt
How to Run
<Bash terminal>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