Dynamic Graph Convolution and Spatiotemporal Self-Attention Network for Traffic Flow Prediction

Zemu Liu, Zhida Qin, Tianyu Huang, Gangyi Ding

Abstract: As a typical spatiotemporal series prediction task, traffic flow prediction has found wide application in intelligent transportation systems (ITS). Despite some progress, several unresolved issues persist. Many existing works calculate the dependencies between nodes based on stable long-term traffic data. However, the short-term dependencies are dynamically changing over time, and neglecting them would cause a decrease in predictive performance. In this article, we propose a novel dynamic graph convolution and spatiotemporal self-attention (DGSTA) network for traffic flow prediction. Specifically, considering the large amount of short-term and the dynamic dependencies between nodes, we design a new dynamic graph convolution module, which generates adjacency matrices for each time step in a day to dynamically capture the changing short-term dependencies. Additionally, we utilize a multihead spatiotemporal self-attention module to, respectively, extract static spatial and temporal correlations between nodes. Furthermore, we design a sequential embedding to explicitly model the long-term correlation between nodes. Extensive experiments conducted on three real-world datasets demonstrate that DGSTA exhibits high competitiveness. The code and data are available at https://github.com/lzmmm30/DGSTA.

External IDs:dblp:journals/iotj/LiuQHD25