STGNN transformer multi step traffic flow prediction model based on dynamic adaptive attention graph learning

Abstract

Traffic congestion has become a major obstacle to urban mobility and quality of life due to rapid urbanization and an increase in car ownership. Accurate traffic flow prediction is important for some intelligent transportation systems such as traffic guidance and signal control. The traditional methods including ARIMA and support vector machines cannot deal well with the complicated spatial and temporal correlations in large-scale traffic data. At present, most spatiotemporal graph neural networks (STGNNs) mainly depend on fixed graph structures which are not adaptive to the varying traffic conditions. We suggest a Dynamic Adaptive Attention Graph Learning based STGNN Transformer model (DASTGNN-Transformer) for multi-step traffic flow prediction. The model uses a multi-step Transformer decoder to identify global long-term temporal dependencies and obtains local spatiotemporal features by means of spatiotemporal graph convolutional encoders. Attention maps are generated from real-time data by a dynamic adaptive graph learning module to reflect the changing location relationships. Experiments conducted on PeMSD4 (containing 307 nodes) and PeMSD8 (with 170 nodes) datasets indicate that DASTGNN-Transformer outperforms the mainstream baselines such as STGCN, adaptive graph networks and iTransformer in terms of RMSE of 0.072–0.085, MAE of 0.061–0.079 and MAPE of 8.2%–9.5%. The ablation studies have confirmed the contributions of each module: the RMSE increases to 0.12–0.20 when the dynamic graph module is omitted. Furthermore, the robustness tests have demonstrated consistent performance even in the presence of noise (the RMSE only increases to 0.142 at a noise level of 0.5) and across different datasets. This research provides an integrated approach for dynamic spatiotemporal dependency modeling which significantly enhances the multi-step prediction accuracy.