Traffic Flow Modeling With Gradual Physics Regularized Learning
Traffic Flow Modeling With Gradual Physics Regularized Learning
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DOI：
10.1109/tits.2021.3131333
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发表时间：
2022-09
影响因子：
8.5
通讯作者：
Yun Yuan;Qinzheng Wang;X. Yang
中科院分区：
文献类型：
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作者：
Yun Yuan;Qinzheng Wang;X. Yang
用于交通状态估计的交通流建模是许多交通管理和运营系统的重要组成部分。为了利用机器学习（ML）方法和经典交通流模型，之前的研究开发了一种混合框架，用于将交通流编码为多元高斯过程。然而，由于多个输入、输出和方程，计算效率较低。为了提高先前方法的效率，本文提出了一种新的建模框架，称为渐进物理正则化学习，以增量地将复杂的交通流模型编码到机器学习过程中。更具体地说，该方法从低阶版本的交通流模型开始，例如基本图和动波模型。然后可以使用高阶模型进一步微调学习到的参数和超参数。基于现实世界高速公路测量的现场测试表明，所提出的模型可以利用额外的物理方程在估计精度和鲁棒性方面实现更好的性能。同时，渐进学习方法可以显着减少计算量，进一步使其能够应用于数据集更大或更复杂的交通流模型的场景。
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.