D-MGDCN-CLSTM: A Traffic Prediction Model Based on Multi-Graph Gated Convolution and Convolutional Long–Short-Term Memory

Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fluctuations and long-term trends, this paper presen...

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Hlavní autoři:	Linliang Zhang, Shuyun Xu, Shuo Li, Lihu Pan, Su Gong
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-01-01
Edice:	Sensors
Témata:	intelligent transportation systems traffic prediction deep learning graph convolutional networks spatio-temporal feature
On-line přístup:	https://www.mdpi.com/1424-8220/25/2/561
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