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SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.
Pu, Shilin; Chu, Liang; Hu, Jincheng; Li, Shibo; Li, Jihao; Sun, Wen.
Afiliación
  • Pu S; College of Automotive Engineering, Jilin University, Changchun 130022, China.
  • Chu L; College of Automotive Engineering, Jilin University, Changchun 130022, China.
  • Hu J; Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK.
  • Li S; College of Automotive Engineering, Jilin University, Changchun 130022, China.
  • Li J; Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK.
  • Sun W; College of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China.
Sensors (Basel) ; 22(22)2022 Nov 21.
Article en En | MEDLINE | ID: mdl-36433621
Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China