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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 1: AAAI-21 Technical Tracks 1

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models

February 1, 2023

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Abstract:

Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy.

Authors

Rongye Shi

Columbia University


Zhaobin Mo

Columbia University


Xuan Di

Columbia University


DOI:

10.1609/aaai.v35i1.16132


Topics: AAAI

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HOW TO CITE:

Rongye Shi||Zhaobin Mo||Xuan Di Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models Proceedings of the AAAI Conference on Artificial Intelligence, 35 (2021) 540-547.

Rongye Shi||Zhaobin Mo||Xuan Di Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models AAAI 2021, 540-547.

Rongye Shi||Zhaobin Mo||Xuan Di (2021). Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 540-547.

Rongye Shi||Zhaobin Mo||Xuan Di. Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. Proceedings of the AAAI Conference on Artificial Intelligence, 35 2021 p.540-547.

Rongye Shi||Zhaobin Mo||Xuan Di. 2021. Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. "Proceedings of the AAAI Conference on Artificial Intelligence, 35". 540-547.

Rongye Shi||Zhaobin Mo||Xuan Di. (2021) "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models", Proceedings of the AAAI Conference on Artificial Intelligence, 35, p.540-547

Rongye Shi||Zhaobin Mo||Xuan Di, "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models", AAAI, p.540-547, 2021.

Rongye Shi||Zhaobin Mo||Xuan Di. "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models". Proceedings of the AAAI Conference on Artificial Intelligence, 35, 2021, p.540-547.

Rongye Shi||Zhaobin Mo||Xuan Di. "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models". Proceedings of the AAAI Conference on Artificial Intelligence, 35, (2021): 540-547.

Rongye Shi||Zhaobin Mo||Xuan Di. Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models. AAAI[Internet]. 2021[cited 2023]; 540-547.


ISSN: 2374-3468


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