An enhanced dynamic spatiotemporal residual network with multi-scale gridding for network-scale traffic speed prediction
Network-scale traffic speed prediction plays a central role in signal timing, routing, traffic control and congestion management. However, existing methods face challenges in capturing complex spatiotemporal dependencies among road segments, and in achieving computational efficiency in large-scale urban networks. To address these challenges, we propose an enhanced Dynamic Spatiotemporal Residual Network (DST-ResNet) framework for network-scale traffic speed prediction. First, the method employs a multi-scale grid partitioning strategy to segment urban road networks at varying levels of granularity, enabling precise predictions at both local and global scales. Second, a dynamic convolution mechanism is introduced to integrate real-time traffic data with static road network features, allowing the model to adaptively capture the dynamic and heterogeneous characteristics of urban traffic. Then, a spatiotemporal attention mechanism is incorporated to capture sequential dependencies in traffic patterns, improving the accuracy and robustness of long-term traffic speed prediction. Last, we conduct experiments based on GPS trajectory data from taxis in Xi’an city, China. The results demonstrate that DST-ResNet outperforms five baseline models. The model maintains stable performance across five spatial resolutions, and a mask-aware retraining strategy reduces the average MSE by 49.65% under missing observations. Ablation studies confirm the contributions of dynamic convolution and attention mechanisms, while residual units mitigate the vanishing gradient problem, facilitating deeper network training.