Transfer Learning With Spatial–Temporal Graph Convolutional Network for Traffic Prediction
Author:
Affiliation:
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
2. School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Chongqing, China
Key Project of Science and Technology Research Program of Chongqing Education Commission of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6979/10202228/10063166.pdf?arnumber=10063166
Reference51 articles.
1. Deep convolutional networks on graph-structured data;henaff;arXiv 1506 05163,2015
2. Learning convolutional neural networks for graphs;niepert;Proc 33rd Int Conf Mach Learn (ICML),2016
3. Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction
4. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
5. Convolutional LSTM network: A machine learning approach for precipitation nowcasting;xingjian;Proc Adv Neural Inf Process Syst,2015
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