论文标题
宏观交通状态估计的混合物理机器学习方法
A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation
论文作者
论文摘要
全场交通状态信息（即流量，速度和密度）对于在高速公路上成功运行（ITS）的成功运行至关重要。但是，不完整的流量信息往往是从在大多数地区安装不足的交通探测器中直接收集的，这是其普及的主要障碍。为了解决这个问题，本文介绍了创新的交通状态估计（TSE）框架，该框架是混合回归机器学习技术（例如，人工神经网络（ANN），随机森林（RF）和支持向量机（SVM）与交通物理学模型（例如，使用二级流量流量的算法和限量信息）构造的交通量和支持矢量机（svm），以构造有限信息的信息，以实现限量信息，以构建有限的信息。系统。为了研究拟议的TSE框架的有效性，本文对犹他州盐湖城的I-15高速公路收集的现实世界数据集进行了经验研究。实验结果表明，已证明所提出的方法可以准确估计全场流量信息。因此，所提出的方法可以提供准确和全场的交通信息，从而为普遍的普及提供了基础。
Full-field traffic state information (i.e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways. However, incomplete traffic information tends to be directly collected from traffic detectors that are insufficiently installed in most areas, which is a major obstacle to the popularization of ITS. To tackle this issue, this paper introduces an innovative traffic state estimation (TSE) framework that hybrid regression machine learning techniques (e.g., artificial neural network (ANN), random forest (RF), and support vector machine (SVM)) with a traffic physics model (e.g., second-order macroscopic traffic flow model) using limited information from traffic sensors as inputs to construct accurate and full-field estimated traffic state for freeway systems. To examine the effectiveness of the proposed TSE framework, this paper conducted empirical studies on a real-world data set collected from a stretch of I-15 freeway in Salt Lake City, Utah. Experimental results show that the proposed method has been proved to estimate full-field traffic information accurately. Hence, the proposed method could provide accurate and full-field traffic information, thus providing the basis for the popularization of ITS.
