Physics Informed Deep Learning (PIDL) for Traffic State Estimation
Data in this repo -
- Synthetic.mat - case study I - simulated traffic state dataset
- NGSIM_US80_4pm_Velocity_Data.txt - case study II - NGSIM Velocity Data
-
A. J. Huang and S. Agarwal, "Physics Informed Deep Learning for Traffic State Estimation: Illustrations with LWR and CTM Models," in IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 503-518, 2022, doi: 10.1109/OJITS.2022.3182925.
-
A. J. Huang and S. Agarwal, "Physics Informed Deep Learning for Traffic State Estimation," 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 2020, pp. 1-6, doi: 10.1109/ITSC45102.2020.9294236.
@ARTICLE{huang2022physics,
author={Huang, Archie J. and Agarwal, Shaurya},
journal={IEEE Open Journal of Intelligent Transportation Systems},
title={Physics-Informed Deep Learning for Traffic State Estimation: Illustrations with LWR and CTM Models},
year={2022},
volume={3},
number={},
pages={503-518},
doi={10.1109/OJITS.2022.3182925}
}
@inproceedings{huang2020physics,
title={Physics informed deep learning for traffic state estimation},
author={Huang, Archie J. and Agarwal, Shaurya},
booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
pages={1--6},
year={2020},
organization={IEEE}
}
Code is built upon Dr. Maziar Raissi's PINNs - https://github.com/maziarraissi/PINNs
Source of processed NGSIM data : Dr. Allan Avila - https://github.com/Allan-Avila/Highway-Traffic-Dynamics-KMD-Code
- Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.