Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting
Author:
Affiliation:
1. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China
Funder
Shanghai Science and Technology Innovation Action Plan Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6979/10582787/10440184.pdf?arnumber=10440184
Reference55 articles.
1. Graph Neural Networks for Intelligent Transportation Systems: A Survey
2. Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey
3. A Comprehensive Survey on Graph Neural Networks
4. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
5. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
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1. Interactive dynamic diffusion graph convolutional network for traffic flow prediction;Information Sciences;2024-08
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