Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting-Reference-Cited by-同舟云学术

Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting

Published:2023-04 Issue:4 Volume:24 Page:3855-3867
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Huo Guangyu¹^ORCID,Zhang Yong¹^ORCID,Wang Boyue¹^ORCID,Gao Junbin²^ORCID,Hu Yongli¹^ORCID,Yin Baocai¹^ORCID

Affiliation:

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

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

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10086684/10012451.pdf?arnumber=10012451

Reference49 articles.

1. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

2. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;li;Proc Int Conf Learn Represent,2018

3. Graph WaveNet for Deep Spatial-Temporal Graph Modeling

4. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

5. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

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