A Spatiotemporal Multiscale Graph Convolutional Network for Traffic Flow Prediction

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Cao, Shuqin;Wu, Libing;Zhang, Rui;Wu, Dan;Cui, Jianqun;...

通讯作者：

Wu, LB

作者机构：

[Wu, Libing; Cao, Shuqin; Wu, LB] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.

[Wu, Libing; Wu, LB] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.

[Wu, Libing] Guangdong Lab Artificial Intelligence & Digital Ec, Guangzhou 510335, Peoples R China.

[Zhang, Rui] Nanjing Univ Sci & Technol, Sch Cyber Sci & Engn, Nanjing 210094, Peoples R China.

[Wu, Dan] Univ Windsor, Sch Comp Sci, Windsor, ON N9B 3P4, Canada.

通讯机构：

[Wu, LB ] W

Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.

Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China.

语种：

英文

关键词：

Traffic prediction;spatiotemporal correlations;multiscale graph;graph convolutional networks;cross-scale fusion

期刊：

IEEE Transactions on Intelligent Transportation Systems

ISSN：

1524-9050

年：

2024

DOI：

10.1109/TITS.2024.3354802

基金类别：

National Key Research and Development Program of China

机构署名：

本校为其他机构

院系归属：

计算机学院

摘要：

Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management. Existing methods mostly capture spatiotemporal correlations on a fine-grained traffic graph, which cannot make full use of cluster information in coarse-grained traffic graph. However, the flow variation of clusters in the coarse-grained traffic graph is more stable compared with nodes in the fine-grained traffic graph. And the flow variation of a fine-grained node is generally consistent with the trend of the cluster to which the node belongs. Thus information in ...

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A Spatiotemporal Multiscale Graph Convolutional Network for Traffic Flow Prediction

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