PISE

Physics Informed deep learning for traffic State Estimation (PISE)

Data in this repo -

1. Synthetic.mat - case study I - simulated traffic state dataset
2. NGSIM_US80_4pm_Velocity_Data.txt - case study II - NGSIM Velocity Data

Corresponding publications -

• 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, 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

Citations

@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

Reference

• 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.