DSTSPYN: a dynamic spatial-temporal similarity pyramid network for traffic flow prediction

Abstract Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.