Graph Convolutional Networks with Multi-Scale Dynamics for Traffic Speed Forecasting

Accurate traffic speed forecasting remains challenging due to complex and variable road conditions. Prior research often overlooks both coarse-grained and fine-grained features in traffic data, hindering a comprehensive capture of traffic data's temporal dependencies. While graph convolutional networks (GCNs) are commonly employed to extract spatial dependencies in traffic networks, they typically view these networks from a static standpoint, failing to consider the dynamic nature of traffic network structures. This limitation restricts their effectiveness in modeling traffic networks. To address these issues, this study introduces a novel deep learning-based spatial-temporal model for precise traffic speed forecasting. This model incorporates a newly developed multi-scale transformation method, which enhances the coarse-grained and fine-grained features in traffic speed data by transforming and fusing traffic speed data, and enabling a more thorough modeling of its temporal dependencies. Additionally, we propose an innovative graph interaction strategy, combines the generated graphs with a dynamic graph convolutional network, to effectively mine the dynamic characteristics of traffic network structures, thereby enhancing the model's accuracy. Extensive experiments on two real-world datasets confirm the robustness and superior performance of the proposed model.