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

文献类型：期刊论文

作者Zhao, Chuan1; Li, Xin1; Shao, Zezhi2; Yang, HongJi3; Wang, Fei2
刊名CONNECTION SCIENCE
出版日期2022-12-31
卷号34期号:1页码:1252-1272
ISSN号0954-0091
关键词Metro passenger flow prediction deep learning multi-featured spatial-temporal tensor dynamic multi-graph neural network
DOI10.1080/09540091.2022.2061915
英文摘要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.
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
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000787575900001
源URL[http://119.78.100.204/handle/2XEOYT63/18872]  
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.

入库方式： OAI收割

来源：计算技术研究所

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