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Traffic Flow Modeling With Gradual Physics Regularized Learning
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DOI：10.1109/TITS.2021.3131333.png)
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Traffic flow modeling for traffic state estimation is a vital component in many traffic management and operation systems. To leverage both machine learning (ML) methods and classical traffic flow models, the previous study has developed a hybrid framework for encoding traffic flow into multivariant Gaussian Process. However, the computational efficiency is low due to multiple inputs, outputs and equations. To improve the efficiency of the previous method, this paper presents a new modeling framework, named gradual physics regularized learning, to incrementally encode complex traffic flow models into the ML process. More specifically, the method starts with the involvement of traffic flow models from the lower-order version, such as the fundamental diagram and the kinetic wave models. Then the learned parameters and hyperparameters can be further fine-tuned with the high-order models. A field test based on real-world freeway measurements indicates the proposed model can leverage the additional physical equations to achieve better performance in estimation accuracy and robustness. Meanwhile, the gradual learning method can significantly reduce the computational efforts and further enables its application to scenarios with either larger datasets or more complex traffic flow models.
Keyword:
Mathematical models
Data models
Physics
Training
Computational modeling
Gaussian processes
Estimation
Second-order traffic flow model
traffic state estimation
gradual physics regularized learning
multivariate Gaussian process
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8.4
论文数:
9.7K
被引数:
6.3W
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