Spatio-temporal fusion graph convolutional network for traffic flow forecasting

doi:10.1016/j.inffus.2023.102196

CORC > 自动化研究所 > 中国科学院自动化研究所 > 脑图谱与类脑智能实验室

	Spatio-temporal fusion graph convolutional network for traffic flow forecasting
	Ma, Ying 1,2; Lou, Haijie 2; Yan, Ming 3; Sun, Fanghui 1; Li, Guoqi 4
刊名	INFORMATION FUSION
	2024-04-01
卷号	104 页码:11
关键词	Graph convolutional network Spatio-temporal data Traffic forecasting
ISSN号	1566-2535
DOI	10.1016/j.inffus.2023.102196
通讯作者	Lou, Haijie(lou_haijie@163.com)
英文摘要	In most recent research, the traffic forecasting task is typically formulated as a spatiotemporal graph modeling problem. For spatial correlation, they typically learn the shared pattern (i.e., the most salient pattern) of traffic series and measure the interdependence between traffic series based on a predefined graph. On the one hand, learning a specific traffic pattern for each node (traffic series) is crucial and essential for accurate spatial correlation learning. On the other hand, most predefined graphs cannot accurately represent the interdependence between traffic series because they are unchangeable while the prediction task changes. For temporal correlation, they usually concentrate on contiguous temporal correlation. Therefore, they are insufficient due to their lack of global temporal correlation learning. To overcome these aforementioned limitations, we propose a novel method named Spatio-Temporal Fusion Graph Convolutional Network (STFGCN). In the spatial aspect, we introduce a node-specific graph convolution operation to learn the node-specific patterns of each node (traffic series). Then, an adaptive adjacent matrix is introduced to represent the interdependence between traffic series. In the temporal aspect, a contiguous temporal correlation learning module is introduced to learn the contiguous temporal correlation of traffic series. Furthermore, a transformer-based global temporal correlation learning module is introduced to learn the global dependence of the traffic series. Experimental results show that our method significantly outperforms other competitive methods on two real-world traffic datasets (PeMSD4 and PeMSD8).
资助项目	National Natural Science Founda-tion of China[61502404] ; Natural Science Foundation of Fujian Province of China[2020J06027]
WOS关键词	SPEED PREDICTION ; DEEP
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:001139361000001
资助机构	National Natural Science Founda-tion of China ; Natural Science Foundation of Fujian Province of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54913]
专题	脑图谱与类脑智能实验室
通讯作者	Lou, Haijie
作者单位	1.Harbin Inst Technol, Fac Comp, Harbin, Peoples R China 2.Xiamen Univ Technol, Fac Comp & Informat Engn, Xiamen, Peoples R China 3.Agcy Sci Technol & Res, Ctr Frontier AI Res, Singapore City, Singapore 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Ma, Ying,Lou, Haijie,Yan, Ming,et al. Spatio-temporal fusion graph convolutional network for traffic flow forecasting[J]. INFORMATION FUSION,2024,104:11.
APA	Ma, Ying,Lou, Haijie,Yan, Ming,Sun, Fanghui,&Li, Guoqi.(2024).Spatio-temporal fusion graph convolutional network for traffic flow forecasting.INFORMATION FUSION,104,11.
MLA	Ma, Ying,et al."Spatio-temporal fusion graph convolutional network for traffic flow forecasting".INFORMATION FUSION 104(2024):11.