详情

原文传递 Traffic Propagation in Road Network from a Data-Driven Analysis Perspective

题名：	Traffic Propagation in Road Network from a Data-Driven Analysis Perspective
正文语种：	eng
作者：	Mengmeng Chang;Zhiming Ding;Limin Guo;Zilin Zhao
作者单位：	Faculty of Information Technology Beijing Univ. of Technology Beijing 100124 China;Institute of Software Chinese Academy of Sciences Beijing 100190 China Faculty of Information Technology Beijing Univ. of Technology Beijing 100124 China;Faculty of Information Technology Beijing Univ. of Technology Beijing 100124 China;Faculty of Information Technology Beijing Univ. of Technology Beijing 100124 China
关键词：	Traffic evolution; Semantic features; Traffic prediction; Spatiotemporal neural network; Dynamic similarity
摘要：	With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topo-logical perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	2
页码：	04022135.1-04022135.13