Research on Highway Traffic Congestion Prediction Based on SA-GFSTCN

Existing traffic congestion prediction methodologies are based on simplistic definitions of congestion indices and fail to effectively integrate static—adaptive graph information. To address these issues, this paper proposes an innovative Traffic Congestion Index (TCI), and a novel traffic congestion prediction model based on static-adaptive graph fusion called SA-GFSTCN. The TCI is defined based on three metrics, namely average speed, traffic flow, and occupancy rate, which collectively reflect road usage and traffic conditions. The model employs a parallel architecture to process the input data using spatiotemporal convolution and spatiotemporal attention modules to model the static road network structure and extract fixed structural information along with the spatiotemporal characteristics. Concurrently, adaptive graph convolution and gated temporal convolution are used to process adaptive graph data and extract dynamic spatiotemporal associative features. Finally, a cross-attention mechanism effectively fuses the outputs of the adaptive graph convolution and gated temporal convolution. Experiments conducted on two real-world traffic datasets demonstrate that the SA-GFSTCN model outperforms the optimal baseline model in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Specifically, it achieves improvements of 0.27 and 0.20 in MAE, 0.22 and 0.23 percentage points in MAPE, and 0.38 and 0.36 in RMSE, respectively, across the datasets when compared to the baseline model. These results validate the effectiveness of the proposed model.