sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencie...

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Autori principali:	Shiyuan Zhang, Yanni Ju, Weishan Kong, Hong Qu, Liwei Huang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-01-01
Serie:	Mathematics
Soggetti:	traffic flow prediction spatiotemporal dependency sLSTM attention graph convolutional network
Accesso online:	https://www.mdpi.com/2227-7390/13/2/185
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