Spatial-Temporal Dynamic Fusion Graph Convolutional Network for Traffic Forecasting
Author:
Affiliation:
1. University of Science and Technology of China,College of Information Science and Technology,Hefei,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10502394/10502376/10503226.pdf?arnumber=10503226
Reference17 articles.
1. Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction
2. Data Driven Congestion Trends Prediction of Urban Transportation
3. Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting
4. Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models
5. Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation
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