A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods tend to apply deep learning models; these methods...

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Autores principales:	Kaiqi Chen, Min Deng, Yan Shi
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2021-09-01
Colección:	ISPRS International Journal of Geo-Information
Materias:	traffic flow forecasting Markov chain directed graph convolution transformer structure spatial dependence temporal dependence
Acceso en línea:	https://www.mdpi.com/2220-9964/10/9/624
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