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TVGCN: Time-variant graph convolutional network for traffic forecasting

文献类型：期刊论文

作者Wang, Yuhu2,3; Fang, Shen2,3; Zhang, Chunxia1; Xiang, Shiming2,3; Pan, Chunhong3
刊名NEUROCOMPUTING
出版日期2022-01-30
卷号471页码:118-129
关键词Spatial-temporal correlation Graph convolutional network Traffic forecasting
ISSN号0925-2312
DOI10.1016/j.neucom.2021.11.006
通讯作者Wang, Yuhu(wangyuhu2019@ia.ac.cn)
英文摘要Traffic forecasting is a very challenging task due to the complicated and dynamic spatial-temporal correlations between traffic nodes. Most existing methods measure the spatial correlations by defining physical or virtual graphs with distance or similarity measurement, which is constructed with stable edge connections by some prior knowledge. However, the use of such graphs with stable edge connections limits the variations of spatial correlations between traffic nodes at different times, which can not capture the hidden dynamic patterns of traffic graphs. This paper proposes a Time-Variant Graph Convolutional Network (TVGCN) to overcome this limitation. Architecturally, a time-variant spatial convolutional module (TV-SCM) is developed on two graphs without any prior knowledge. One graph is learned to capture the stable spatial correlations of the traffic graph, while the other graph is evolved to model dynamic spatial correlations at different times. Such two graphs are combined hierarchically together under the framework of graph convolutional network (GCN). Moreover, a gated multi-scale temporal convolutional module (GMS-TCM) is designed to extract long-range temporal dependencies within traffic nodes, which are further supplied to the TV-SCM to mutually explore the spatial correlations between traffic nodes. Extensive experiments conducted on three real-world traffic datasets indicate the effectiveness and superiority of our proposed approach. (c) 2021 Elsevier B.V. All rights reserved.
资助项目National Key Research and Development Program of China[2020AAA0104903] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242] ; National Natural Science Foundation of China[61802407]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000761907400002
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/48050]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Wang, Yuhu
作者单位1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yuhu,Fang, Shen,Zhang, Chunxia,et al. TVGCN: Time-variant graph convolutional network for traffic forecasting[J]. NEUROCOMPUTING,2022,471:118-129.
APA Wang, Yuhu,Fang, Shen,Zhang, Chunxia,Xiang, Shiming,&Pan, Chunhong.(2022).TVGCN: Time-variant graph convolutional network for traffic forecasting.NEUROCOMPUTING,471,118-129.
MLA Wang, Yuhu,et al."TVGCN: Time-variant graph convolutional network for traffic forecasting".NEUROCOMPUTING 471(2022):118-129.

入库方式： OAI收割

来源：自动化研究所

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