ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning.

Jia, Hongwei; Luo, Haiyong; Wang, Hao; Zhao, Fang; Ke, Qixue; Wu, Mingyao; Zhao, Yunyun

Jia, Hongwei; Luo, Haiyong; Wang, Hao; Zhao, Fang; Ke, Qixue; Wu, Mingyao; Zhao, Yunyun.

Afiliação

Jia H; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Luo H; Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 200190, China.
Wang H; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Zhao F; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Ke Q; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Wu M; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Zhao Y; School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel) ; 20(16)2020 Aug 14.

Article em En | MEDLINE | ID: mdl-32824074

Palavras-chave

attention mechanism; forecasting passenger flow; multi-task learning; spatiotemporal dependency; spatiotemporal networks

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China

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