TVGCN: Time-variant graph convolutional network for traffic forecasting

doi:10.1016/j.neucom.2021.11.006

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	TVGCN: Time-variant graph convolutional network for traffic forecasting
	Wang, Yuhu 1,2; Fang, Shen 1,2; Zhang, Chunxia 3; Xiang, Shiming 1,2; Pan, Chunhong 1
刊名	NEUROCOMPUTING
	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
DOI	10.1016/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
出版者	ELSEVIER
WOS记录号	WOS:000761907400002
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
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.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, 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.