LAUSR

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.