  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.
