TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely o...

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Bibliographic Details

Published in:	Sensors
Main Authors:	Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang
Format:	Article
Language:	English
Published:	MDPI AG 2024-11-01
Subjects:	graph convolutional networks traffic flow forecasting adaptive graph learning
Online Access:	https://www.mdpi.com/1424-8220/24/21/7086