AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors
Author:
Affiliation:
1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China
2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China
3. Alibaba Group, Hangzhou, China
Funder
National Key Research and Development Program of China
Joint Funds of the National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6979/9916643/09768120.pdf?arnumber=9768120
Reference46 articles.
1. License Plate Recognition Data-Based Traffic Volume Estimation Using Collaborative Tensor Decomposition
2. Mining Road Network Correlation for Traffic Estimation via Compressive Sensing
3. Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction
4. How Fast You Will Drive? Predicting Speed of Customized Paths By Deep Neural Network
5. Spatio-Temporal Meta Learning for Urban Traffic Prediction
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