Traffic Flow Prediction Based on Decoupled Dynamic Spatio-Temporal Convolutional Recurrent Network

This paper introduces a traffic flow prediction model using a Decoupled Dynamic Spatio-Temporal Convolutional Recurrent Network (DDSTCRN) to improve the exploration of complex spatio-temporal correlations in existing traffic flow prediction models. First, the data-decoupling module uses gating and residual decomposition mechanisms to separate the two hidden time-series signals in the traffic data: diffusion and independent signals. Second, separate models are applied to these two signals to improve prediction accuracy. Local diffusion convolution captures the diffusion process between traffic data points, whereas dynamic recurrent graph convolution captures the global spatio-temporal correlations in the independent signals, addressing the accuracy issues of single-model approaches. Third, a dynamic graph constructor, using a prior-free dynamic graph construction method, captures the dynamically changing spatial dependencies in traffic data. Finally, an external component module predicts the impact of factors such as weather conditions on traffic data, thereby enhancing the robustness of the model. Experiments on five public traffic flow datasets (METR-LA, PEMS-BAY, PEMS04, PEMS08, and NE-BJ) show that the proposed model reduces Mean Absolute Error (MAE) by 1.2%—4.6% compared to the top-performing D2STGNN and by 3.7%—10.5% compared to the second-best DGCRN across different prediction lengths. The proposed model exhibits lower prediction errors than the other representative models. The experimental results suggest that the model effectively captures complex spatio-temporal correlations in traffic data and delivers superior performance in traffic flow prediction tasks.