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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Bibliografiset tiedot

Päätekijät:	Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2024-11-01
Sarja:	Sensors
Aiheet:	graph convolutional networks traffic flow forecasting adaptive graph learning
Linkit:	https://www.mdpi.com/1424-8220/24/21/7086
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