Dynamic Spatio-Temporal Graph Fusion Convolutional Network for Urban Traffic Prediction

Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations...

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Bibliographic Details

Published in:	Applied Sciences
Main Authors:	Haodong Ma, Xizhong Qin, Yuan Jia, Junwei Zhou
Format:	Article
Language:	English
Published:	MDPI AG 2023-08-01
Subjects:	traffic prediction dynamic graph structure graph convolutional network spatio-temporal modeling
Online Access:	https://www.mdpi.com/2076-3417/13/16/9304