Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction

doi:10.1080/09540091.2022.2061915

	Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction
	Zhao, Chuan 1; Li, Xin 1; Shao, Zezhi 2; Yang, HongJi 3; Wang, Fei 2
	2022-12-31
发表期刊	CONNECTION SCIENCE
ISSN	0954-0091
卷号	34 期号:1 页码:1252-1272
摘要	Metro passenger flow prediction is an essential part of crowd flow forecasting and intelligent transportation management systems. However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. Spatial relation and featural dependence are separately captured by two DMGCN blocks. Each DMGCN block encodes various relationships by constructing multiple graphs consisting of predefined and non-defined topologies. The results of evaluations conducted of the MFST tensor and the DMGCN on the real-world Beijing subway dataset indicate that the prediction performance of the proposed model is superior to that of the existing baselines. The proposed model thus contributes significantly to the improvement of public safety by providing early warnings of large passenger flow and enabling the smart scheduling of resources.
关键词	Metro passenger flow prediction deep learning multi-featured spatial-temporal tensor dynamic multi-graph neural network
DOI	10.1080/09540091.2022.2061915
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71901004] ; General project of Social Sciences of Beijing Municipal Commission of Education[SM202210011004] ; Excellent Youth Training Programme of Beijing Technology and Business University
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000787575900001
出版者	TAYLOR & FRANCIS LTD
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18873
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Fei
作者单位	1.Beijing Technol & Business Univ, Sch E Business & Logist, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Leicester, Sch Comp & Math Sci, Leicester, Leics, England
推荐引用方式 GB/T 7714	Zhao, Chuan,Li, Xin,Shao, Zezhi,et al. Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction[J]. CONNECTION SCIENCE,2022,34(1):1252-1272.
APA	Zhao, Chuan,Li, Xin,Shao, Zezhi,Yang, HongJi,&Wang, Fei.(2022).Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction.CONNECTION SCIENCE,34(1),1252-1272.
MLA	Zhao, Chuan,et al."Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction".CONNECTION SCIENCE 34.1(2022):1252-1272.