Physics-Informed Spatiotemporal Learning Framework for Urban Traffic State Estimation
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 7
Abstract
Accurate traffic estimation on urban networks is a prerequisite for efficient traffic detection, congestion warning, and transportation schedule. The current estimation methods can be roughly divided into model-driven and the data-driven methods. The estimation accuracy of the model-driven methods cannot satisfy certain applications. Meanwhile, the data-driven methods have the disadvantages of poor generalization ability and weak interpretability. To overcome these challenges, this paper proposes a framework named the physics-informed spatiotemporal graph convolution neural network (PSTGCN) based on physics-informed deep learning theories. The PSTGCN uses a spatiotemporal graph convolution neural network combined with traffic flow models to estimate the traffic state. The proposed model not only considers the temporal and spatial dependence of traffic flow but also abides by the internal law of traffic flow. Furthermore, the estimation objects of the proposed model are multiple variables that comprehensively represent the traffic state. Experiments on real-world traffic data reveal the error of the PSTGCN is reduced by 38.39% compared to the baselines. Also, the PSTGCN can achieve a similar prediction effect as the baselines by using half of the global spatial information. These results demonstrate that the PSTGCN outperforms the state-of-the-art models in urban traffic estimation and is robust under variable road conditions and data scales.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments. Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research presented in this work was supported by the National Natural Science Foundation of China (Grant No. 61573030), National Key Research and Development Program of China (Grant No. 2017YFC0803903), and National Natural Science Foundation of China (Grant No. 6210020816). We would like to thank MogoEdit (https://www.mogoedit.com) for its English editing during the preparation of this manuscript.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
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Received: May 25, 2022
Accepted: Dec 21, 2022
Published online: May 9, 2023
Published in print: Jul 1, 2023
Discussion open until: Oct 9, 2023
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Cited by
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