Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction

Author:

Sun Yanfeng1ORCID,Jiang Xiangheng1ORCID,Hu Yongli1ORCID,Duan Fuqing2,Guo Kan1ORCID,Wang Boyue1ORCID,Gao Junbin3ORCID,Yin Baocai1ORCID

Affiliation:

1. Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China

2. School of Artificial Intelligence, Beijing Normal University, Beijing, China

3. Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Camperdown, NSW, Australia

Funder

Beijing Natural Science Foundation

National Natural Science Foundation of China

Research and Deveopment Program of Beijing Municipal Education Commission

Beijing Talents Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

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4. HyperGCN: A new method for training graph convolutional networks on hypergraphs;yadati;Proc Adv Neural Inf Process Syst,2019

5. Structured Sequence Modeling with Graph Convolutional Recurrent Networks

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