文献类型：期刊文献

英文题名：Spatial-temporal correlation graph convolutional networks for traffic forecasting

作者：Huang, Ru[1,5];Chen, Zijian[1];Zhai, Guangtao[2];He, Jianhua[3];Chu, Xiaoli[4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Jiao Tong Univ, Inst Image Commun & Informat Proc, Shanghai, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Colchester, England;[4]Univ Sheffield, Dept Elect & Elect Engn, Sheffield, England;[5]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：17

期号：7

起止页码：1380

外文期刊名：IET INTELLIGENT TRANSPORT SYSTEMS

收录：;EI(收录号：20230313390004);WOS:【SCI-EXPANDED(收录号:WOS:000911512800001)】；

基金：Natural Science Foundation of Shanghai, Grant/Award Number: 20ZR1413800; Marie Sklodowska-Curie Actions, Grant/Award Numbers: 101022280, 824019; National Natural Science Foundation of China, Grant/Award Numbers: 61673178, 61922063

语种：英文

外文关键词：management and control; neural net architecture; network topology; traffic modeling

摘要：Traffic forecasting, as a fundamental and challenging problem of intelligent transportation systems (ITS), has always been the focus of researchers. Nevertheless, accurate traffic forecasting still exists some problems due to the complex spatial-temporal dependencies and irregularities of traffic flows. Most of the existing methods typically use the spatial adjacency matrix and complicated mechanism to model spatial-temporal relationships separately, while ignoring the latent spatial-temporal correlations. In this paper, a novel architecture is proposed named spatial-temporal correlation graph convolutional networks (STCGCN) for traffic prediction. First, an informative fused graph structure is constructed to better learn the complex spatial-temporal correlations, which breaks the limitation that the general spatial adjacency matrix cannot reflect temporal correlations. Moreover, spatial-temporal correlation graph convolution and gated temporal convolution are performed in parallel and they are integrated into a unified layer, which enables capturing both local and global spatial-temporal dependencies simultaneously. By stacking multiple layers, STCGCN can learn more long-range spatial-temporal dependencies. Experimental results on five public traffic datasets demonstrate the effectiveness and robustness of the proposed STCGCN in urban traffic forecasting.