IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting

Abstract Accurate forecasting of traffic flow in the future period is very important for planning traffic routes and alleviating traffic congestion. However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional...

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Detalles Bibliográficos

Principais autores:	Lianfei Yu, Ziling Wang, Wenxi Yang, Zhijian Qu, Chongguang Ren
Formato:	Artigo
Idioma:	Inglês
Publicado:	Springer 2024-11-01
Series:	Complex & Intelligent Systems
Assuntos:	Traffic flow forecasting Spatial–temporal correlation Graph convolutional networks Information enhancement Self-attention
Acceso en liña:	https://doi.org/10.1007/s40747-024-01663-1
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