TVGCN: Time-variant graph convolutional network for traffic forecasting

doi:10.1016/j.neucom.2021.11.006

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	TVGCN: Time-variant graph convolutional network for traffic forecasting
	Wang, Yuhu 1,2; Fang, Shen1,2 ; Zhang, Chunxia 3; Xiang, Shiming1,2 ; Pan, Chunhong1
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2022-01-30
卷号	471 页码:118-129
通讯作者	Wang, Yuhu(wangyuhu2019@ia.ac.cn)
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.
关键词	Spatial-temporal correlation Graph convolutional network Traffic forecasting
DOI	10.1016/j.neucom.2021.11.006
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000761907400002
出版者	ELSEVIER
七大方向——子方向分类	机器学习
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48051
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
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
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.