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This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.
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2021
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B. T. Thodi, Z. S. Khan, S. E. Jabari, and M. Menendez, “Incorporating kinematic wave theory into a deep learning method for high-resolution traffic speed estimation,” 2021
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Y. Yuan, Z. Zhang, X. T. Yang, and S. Zhe, “Macroscopic traffic flow modeling with physics regularized gaussian process: A new insight into machine learning applications in transportation,” Transportation Research Part B: Methodological , vol. 146, pp. 88–110, 2021
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R. Shi, Z. Mo, K. Huang, X. Di, and Q. Du, “A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation,” IEEE Transactions on Intelligent Transportation Systems , 2021
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R. Shi, Z. Mo, and X. Di, “Physicsinformed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 1, 2021, pp. 540–547
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X. Wang and L. Sun, “Diagnosing spatiotemporal traffic anomalies with low-rank tensor autoregression,” IEEE Transactions on Intelligent Transportation Systems , 2021
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