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...

Полное описание

Библиографические подробности

Главные авторы:	Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2024-11-01
Серии:	Sensors
Предметы:	graph convolutional networks traffic flow forecasting adaptive graph learning
Online-ссылка:	https://www.mdpi.com/1424-8220/24/21/7086
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!