Spatiotemporal dynamic graph convolutional network for traffic speed forecasting

Accurate traffic speed forecasting is challenging because of complex spatiotemporal correlations of traffic data. Some studies have recognized that correlations among sensors change gradually and have constructed dynamic graphs to reflect this change. However, they ignored the temporal dependencies among dynamic graphs, resulting in an inability to capture the deep dynamic dependencies among sensors. Few studies have explored the hybrid interaction patterns of static and dynamic graphs, hindering the adequate exploration of spatial dependencies. Therefore, a novel deep learning model is proposed in this study to address these problems and produce accurate traffic speed forecasting. First, a new graph generation method is proposed to capture the deep dynamic dependencies among sensors, which exploits historical information of dynamic graphs to develop temporal dependencies among dynamic graphs. Then, a new fusion strategy is proposed to investigate the hybrid interaction patterns of static and dynamic graphs, which effectively mines hidden information in graphs to fully extract spatial dependencies. Finally, a new spatiotemporal network architecture is proposed, which unifies the proposed graph generation method and fusion strategy into a consistent framework and yields the final forecasting results. Experimental results on two real-world traffic datasets indicate that the proposed model outperforms state-of-the-art models.