Memory-Augmented Spatio-Temporal Transformer for Robust Traffic Flow Forecasting

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, supporting traffic management, congestion mitigation, and efficient utilization of road resources. Advances in neural network-based methods, particularly graph neural networks (GNNs) and attention-based mod...

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Detalles Bibliográficos

Autores principales:	Puqing Hu, Chunjiang Wu, Chen Wang, Xin Yang, Zhibin Li, Tinghui Chen, Shijie Zhou
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2026-03-01
Colección:	Biomimetics
Materias:	traffic flow forecasting spatio-temporal representation learning graph attention networks long-term temporal dependencies neural prediction models
Acceso en línea:	https://www.mdpi.com/2313-7673/11/3/170
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