Journal of Guangdong University of Technology ›› 2024, Vol. 41 ›› Issue (01): 86-92.doi: 10.12052/gdutxb.230005

• Computer Science and Technology • Previous Articles     Next Articles

Traffic Flow Prediction Based on Recurrent Independent Mechanisms

Wen Wen, Jiang Jian-qiang, Cai Rui-chu, Hao Zhi-feng   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2023-01-09 Online:2024-01-25 Published:2024-02-01

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Abstract: Traffic flow prediction is an important issue of the intelligent traffic control and management systems. However, traffic flow data has nonlinear and complex characteristics in both time and space, making it challenging to accurately predict it. In this regard, this paper proposes a Graph temopral recurrent independent mechanisms (G-tRIM) model, which uses Graph attention networks (GAT) to effectively capture the spatial dependencies of traffic flow data, and uses Recurrent independent mechanisms (RIM) to accurately characterize the latent state of traffic flow data. We conduct experiments on the Beijing and Guizhou datasets, and the experimental results show that our proposed G-tRIM outperforms the baseline models on both datasets in terms of MSE and MAE.

Key words: traffic flow prediction, graph attention networks, recurrent independent mechanisms

CLC Number: 

  • TP183
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