DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting

doi:10.1109/JIOT.2024.3380746

	DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting
	Qin, Yanjun 1; Tao, Xiaoming 1; Fang, Yuchen 2; Luo, Haiyong 3; Zhao, Fang 2; Wang, Chenxing 2
	2024-06-15
发表期刊	IEEE INTERNET OF THINGS JOURNAL
ISSN	2327-4662
卷号	11 期号:12 页码:22208-22219
摘要	Traffic forecasting belongs to intelligent transportation systems and is helpful for public property and life safety. Therefore, to forecast traffic accurately, researchers pay great attention to dealing with complex problems by mining intricate spatial and temporal dependencies of the traffic. However, some challenges still hold back traffic forecasting: 1) Most studies mainly focus on modeling correlations of traffic time series of close distances on the road network and ignore correlations of remote but similar traffic time series; 2) Previous static graph-based methods failed to reflect the dynamic changed spatial relations of multiple time series in the evolving traffic system. To tackle the above issues, we design a new dynamic multigraph spatio-temporal convolution network (DMGSTCN) in this article, which utilizes the gated causal convolution with the dynamic multigraph convolution network (DMGCN) to simultaneously extract spatial and temporal information. Specifically, DMGCN uses not only distance-based graphs but also structure-based graphs to obtain spatial information from nearby and remote but similar traffic time series, respectively. Moreover, to dynamically model spatial correlations, DMGCN first splits neighbors of each traffic time series into different regions according to relative position relationships. Then DMGCN assigns different weights to different regions at different time slices. Empirical evaluations on four traffic forecasting benchmarks reveal that DMGSTCN outperforms existing methods.
关键词	Time series analysis Convolution Forecasting Correlation Task analysis Roads Predictive models Graph convolution network (GCN) spatial-temporal data traffic forecasting
DOI	10.1109/JIOT.2024.3380746
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001242362600105
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39911
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tao, Xiaoming
作者单位	1.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qin, Yanjun,Tao, Xiaoming,Fang, Yuchen,et al. DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting[J]. IEEE INTERNET OF THINGS JOURNAL,2024,11(12):22208-22219.
APA	Qin, Yanjun,Tao, Xiaoming,Fang, Yuchen,Luo, Haiyong,Zhao, Fang,&Wang, Chenxing.(2024).DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting.IEEE INTERNET OF THINGS JOURNAL,11(12),22208-22219.
MLA	Qin, Yanjun,et al."DMGSTCN: Dynamic Multigraph Spatio-Temporal Convolution Network for Traffic Forecasting".IEEE INTERNET OF THINGS JOURNAL 11.12(2024):22208-22219.