Physics-informed Learning for Identification and State Reconstruction of Traffic Density-Reference-Cited by-同舟云学术

Physics-informed Learning for Identification and State Reconstruction of Traffic Density

Published:2021-12-14 Issue: Volume: Page:
ISSN:
Container-title:2021 60th IEEE Conference on Decision and Control (CDC)
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Author:

Barreau Matthieu¹,Aguiar Miguel¹,Liu John¹,Johansson Karl Henrik¹

Affiliation:

1. KTH Royal Institute of Technology,Division of Decision and Control Systems and Digital Futures,Stockholm,Sweden

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9682670/9682776/09683295.pdf?arnumber=9683295

Reference25 articles.

1. Physics Informed Deep Learning for Traffic State Estimation

2. Physics-informed deep learning for traffic state estimation;shi;preprint arXiv 2101 06580,2021

3. Incorporating kinematic wave theory into a deep learning method for high-resolution traffic speed estimation;thodi;arXiv preprint arXiv 2102 05988,2021

4. Learning-based state reconstruction for a scalar hyperbolic PDE under noisy lagrangian sensing;barreau;Learning for Dynamics and Control,2021

5. Learning-based Traffic State Reconstruction using Probe Vehicles

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