Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

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第一作者： Guangsi,Shi

第一单位： Department of Chemical & Biological, Faculty of Engineering, Monash University, Clayton, VIC 38000, Australia.

作者： Guangsi,Shi ^[1] ; Linhao,Luo ^[2] ; Yongze,Song ^[3] ; Jing,Li ^[1] ; Shirui,Pan ^[4]

作者单位： Department of Chemical & Biological, Faculty of Engineering, Monash University, Clayton, VIC 38000, Australia. ^[1] Department of Data Science & AI, Faculty of IT, Monash University, Clayton VIC 38000, Australia. ^[2] School of Design and the Built Environment, Curtin University, Perth, WA 6845, Australia. ^[3] School of ICT, Griffith University, South Port, QLD 4215, Australia. ^[4]

关键词 Artificial intelligence Engineering Geography

DOI 10.1016/j.isci.2024.110175

PMID 39109176

发布时间 2024-08-07

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2024年27卷7期

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Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

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Deep transformer-based heterogeneous spatiotemporal graph learning for geographical traffic forecasting.

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