Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow
  Prediction
Haiyang Liu Chunjiang Zhu Detian Zhang Qing Li
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.