Volume 41 Issue 4
Aug.  2023
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ZHAO Zhenxing, ZENG Wei, TANG Chenjia. A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship[J]. Journal of Transport Information and Safety, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015
Citation: ZHAO Zhenxing, ZENG Wei, TANG Chenjia. A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship[J]. Journal of Transport Information and Safety, 2023, 41(4): 143-153. doi: 10.3963/j.jssn.1674-4861.2023.04.015

A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship

doi: 10.3963/j.jssn.1674-4861.2023.04.015
  • Received Date: 2023-02-10
    Available Online: 2023-11-23
  • A traffic flow prediction model based on dynamic spatio-temporal graph convolutional network (DySTGCN) is developed, to effectively extract the spatio-temporal features of traffic flows and improve the accuracy of traffic flow prediction. DySTGCN models not only Spatio-temporal information of traffic flow but also the influence of temporal information on spatial information. Meanwhile, a spatial structure based on temporal information, a time-varying spatial topology graph (TSG), is proposed and a deep neural network structure to efficiently calculate the TSG is designed. The structure extracts correlation features of traffic flow at different nodes and can reduce the noise through encoding and decoding. TSG reflects the real-time spatial feature of the traffic network, a stable spatial graph (SG) based on the spatial position of nodes reflects the stable spatial feature. The TSG and SG jointly guide the traffic flow prediction and depict the spatio-temporal feature more accurately to improve the prediction precision. To test the prediction effect of the model, experiments are carried out on two authoritative public data sets. The results show that TSG learned by DySTGCN can accurately reflect the correlation between traffic flows at different nodes and MAE, RMSE and WMAPE of DySTGCN are 13.40%, 10.98%, and 16.72% lower than other spatio-temporal graph convolutional network models such as STGCN, ASTGCN, verifying that dynamic spatial relation plays an important role in short-term traffic flow prediction fully. Besides, DySTGCN can extract periodic features of traffic flow and achieve continuous and uninterrupted prediction of traffic flow.

     

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