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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Detalhes bibliográficos

Principais autores:	Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2024-11-01
Colecção:	Sensors
Assuntos:	graph convolutional networks traffic flow forecasting adaptive graph learning
Acesso em linha:	https://www.mdpi.com/1424-8220/24/21/7086
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