详情

原文传递 Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

题名：	Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections
正文语种：	英文
作者：	Wan Li;Xuegang "Jeff" Ban;Jianfeng Zheng;Henry X. Liu;Cheng Gong;Yong Li
作者单位：	Energy and Transportation Science Division;Dept, of Civil and Environmental Engineering, Univ;Senior Algorithm Engineer, Didi Chuxing, 10 Xibeiwang Rd., Haidian District, Beijing;Dept, of Traffic Police, Jinan Public Security Bureau
摘要：	The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.
出版日期：	2020.01
出版年：	2020
期刊名称：	Journal of Transportation Engineering
卷：	Vol.146
期：	No.08
页码：	04020081