Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
This work improves traffic forecasting for smart city applications, though it is incremental as it builds on existing graph-based methods with enhancements for dynamic dependencies.
The paper tackles traffic forecasting by addressing the limitations of static adjacency matrices in capturing real-time spatial dependencies, introducing a data-driven weighted adjacency matrix and a two-way spatio-temporal fusion operation, achieving state-of-the-art performance on four large-scale datasets.
Traffic forecasting is essential for the traffic construction of smart cities in the new era. However, traffic data's complex spatial and temporal dependencies make traffic forecasting extremely challenging. Most existing traffic forecasting methods rely on the predefined adjacency matrix to model the Spatio-temporal dependencies. Nevertheless, the road traffic state is highly real-time, so the adjacency matrix should change dynamically with time. This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent Network (MSTFGRN) to address the issues above. The network proposes a data-driven weighted adjacency matrix generation method to compensate for real-time spatial dependencies not reflected by the predefined adjacency matrix. It also efficiently learns hidden Spatio-temporal dependencies by performing a new two-way Spatio-temporal fusion operation on parallel Spatio-temporal relations at different moments. Finally, global Spatio-temporal dependencies are captured simultaneously by integrating a global attention mechanism into the Spatio-temporal fusion module. Extensive trials on four large-scale, real-world traffic datasets demonstrate that our method achieves state-of-the-art performance compared to alternative baselines.