Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics

Abstract

Trajectory data contains rich spatial and temporal information. Turning trajectories into graphs and then analyzing them efficiently in an AI-empowered way is a representative branch of trajectory analysis in IoV and ITS environments, which is of great significance. This research attempts to project trajectories onto road networks to predict traffic conditions. Extracting accurate spatio-temporal dependencies is the key to improving the analysis. However, two problems exist in the current study. The first one is the focus on the network structure while ignoring node features, and the second one is that the structure cannot be fully utilized. In addition, the static spatial structure may not accurately reflect the dynamic real spatial dependency. In response to these problems, a novel Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics (STFGCN) model is designed. It contains two independent views: the structural view and feature view. The view fusion layer is further designed. It includes an extended graph convolutional module and a causal dilated module. The extended graph convolutional module fully extracts dynamic spatial dependencies, while the causal dilated module captures time tendencies. Stacked view fusion layers and a view fusion module perform fusion operations based on the advantages of the two views, efficiently integrating information from both. Several experiments are performed on two real-world trajectory datasets. The results show that a better prediction performance is obtained, especially on the long-range time prediction task.