Traffic Speed Prediction Based on Spatial-Temporal Dynamic and Static Graph Convolutional Recurrent Network

Traffic speed prediction based on spatial-temporal data plays an important role in intelligent transportation. The time-varying dynamic spatial relationship and complex spatial-temporal dependence are still important problems to be considered in traffic prediction. In response to existing problems, a Dynamic and Static Graph Convolutional Recurrent Network (DASGCRN) model for traffic speed prediction is proposed to capture the spatial-temporal correlation in the road network. DASGCRN consists of Spatial Correlation Extraction Module (SCEM), Dynamic Graph Construction Module (DGCM), Dynamic Graph Convolution Recurrent Module (DGCRM) and residual decomposition. Firstly, the improved traditional static adjacency matrix captures the relationship between each time step node. Secondly, the graph convolution captures the overall spatial information between the road networks, and the dynamic graph isomorphic network captures the hidden dynamic dependencies between adjacent time series. Thirdly, spatial-temporal correlation of traffic data is captured based on dynamic graph convolution and gated recurrent unit. Finally, the residual mechanism and the phased learning strategy are introduced to enhance the performance of DASGCRN. We conducted extensive experiments on two real-world traffic speed datasets, and the experimental results show that the performance of DASGCRN is significantly better than all baselines.

@article{Wenxi2024,
  title     = {Traffic Speed Prediction Based on Spatial-Temporal Dynamic and Static Graph Convolutional Recurrent Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {YANG Wenxi and WANG Ziling and CUI Tao and LU Yudong and QU Zhijian},
  doi       = {10.14569/IJACSA.2024.0151254},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151254}
}