A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Author:

Liu Fuqiang1,Wang Jiawei1,Tian Jingbo1ORCID,Zhuang Dingyi1ORCID,Miranda-Moreno Luis1ORCID,Sun Lijun1ORCID

Affiliation:

1. Department of Civil Engineering, McGill University, Montreal, QC, Canada

Funder

Natural Sciences and Engineering Research Council (NSERC) of Canada

National Natural Science Foundation of China (NSFC)-Fonds de Recherche du Québec-Société et Culture (FRQSC) Research Program on Smart Cities and Big Data

Canada Foundation for Innovation

Fonds de Recherche du Québec-Nature et technologies (FRQNT) through the B2X Doctoral Scholarship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Reference42 articles.

1. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting;perozzi;Proc SIGKDD,2014

2. Residual Attention Network for Image Classification

3. Deep Residual Learning for Image Recognition

4. Long-term urban traffic speed prediction with deep learning on graphs;yu;IEEE Trans Intell Transp Syst,2021

5. A graph-based temporal attention framework for multi-sensor traffic flow forecasting;zhang;IEEE Trans Intell Transp Syst,2021

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