ST2SNet: Spatial–Temporal Two-Layer Synchronous Network for Traffic Flow Prediction

As a crucial component of intelligent transportation systems (ITS), accurate traffic flow prediction has attracted considerable attention. Numerous popular models, such as recurrent neural networks (RNN) and graph convolution networks (GCN), have been extensively applied to this task. However, due to the constraints of the topology of urban road network and the law of dynamic change with time, the single-layer network model cannot capture the dynamic correlation between the spatial–temporal and the traditional road network characteristics for traffic flow. In this article, we propose a spatial–temporal twolayer synchronous network (ST2SNet) for traffic flow prediction. First, we introduce a novel network structure comprising a spatial–temporal fusion layer and a situation awareness layer, where temporal and spatial correlations are fused synchronously using a transformer architecture. Next, we incorporate the residual gated GCN structure and a 2-D convolution network to effectively integrate road network information and temporal trends for spatial–temporal traffic flow prediction. Finally, we evaluate ST2SNet on two benchmark datasets, PeMSD4, and PeMSD8. Compared to baseline methods, our model achieves MAE improvements ranging from 4.00% to 48.30% on PeMSD4 and from 4.88%to 54.13%on PeMSD8. These outstanding experimental results demonstrate that ST2SNet significantly enhances prediction performance.