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Traffic forecasting is essential for the traffic construction of smart citiesin the new era. However, traffic data's complex spatial and temporaldependencies make traffic forecasting extremely challenging. Most existingtraffic forecasting methods rely on the predefined adjacency matrix to modelthe Spatio-temporal dependencies. Nevertheless, the road traffic state ishighly real-time, so the adjacency matrix should change dynamically with time.This article presents a new Multi-Spatio-temporal Fusion Graph RecurrentNetwork (MSTFGRN) to address the issues above. The network proposes adata-driven weighted adjacency matrix generation method to compensate forreal-time spatial dependencies not reflected by the predefined adjacencymatrix. It also efficiently learns hidden Spatio-temporal dependencies byperforming a new two-way Spatio-temporal fusion operation on parallelSpatio-temporal relations at different moments. Finally, global Spatio-temporaldependencies are captured simultaneously by integrating a global attentionmechanism into the Spatio-temporal fusion module. Extensive trials on fourlarge-scale, real-world traffic datasets demonstrate that our method achievesstate-of-the-art performance compared to alternative baselines.

Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting