  Traffic flow prediction is one of the most fundamental tasks of intelligent
transportation systems. The complex and dynamic spatial-temporal dependencies
make the traffic flow prediction quite challenging. Although existing
spatial-temporal graph neural networks hold prominent, they often encounter
challenges such as (1) ignoring the fixed graph that limits the predictive
performance of the model, (2) insufficiently capturing complex spatial-temporal
dependencies simultaneously, and (3) lacking attention to spatial-temporal
information at different time lengths. In this paper, we propose a Multi-Scale
Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN,
which consists of two different recurrent neural networks: the single-step gate
recurrent unit and the multi-step gate recurrent unit to fully capture the
complex spatial-temporal information in the traffic data under different time
windows. Moreover, we propose a spatial-temporal synchronous attention
mechanism that integrates adaptive position graph convolutions into the
self-attention mechanism to achieve synchronous capture of spatial-temporal
dependencies. We conducted extensive experiments on four real traffic datasets
and demonstrated that our model achieves the best prediction accuracy with
non-trivial margins compared to all the twenty baseline methods.
