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

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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Bibliographic Details
Main Authors: Linliang Zhang, Shuyun Xu, Shuo Li, Lihu Pan, Su Gong
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
intelligent transportation systems
traffic prediction
deep learning
graph convolutional networks
spatio-temporal feature
Online Access:https://www.mdpi.com/1424-8220/25/2/561
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