DSTAGCN: Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting

Capturing complex and dynamic spatial-temporal dependencies of traffic data is of great importance for accurate and real-time traffic forecasting in intelligent transportation systems. The spatial-temporal dependency between traffic locations is often dynamic, which means the correlation between the traffic status of different locations changes jointly over their spatial distance and the time slice they are in. Most of the existing Graph Convolutional Network-based methods usually capture spatial and temporal dependencies separately and then combine them in a parallel or serial mechanism to capture the spatial-temporal features. They always utilize the predefined static graph structure to capture both local correlations and global dependencies in the same time slice. These methods are incapable of directly learning dynamic spatial-temporal dependency across time slices. Meanwhile, it is challenging to learn the spatiotemporal correlation knowledge among traffic locations only by using neural networks. To address these issues, we propose a novel Dynamic Spatial-Temporal Adjacent Graph Convolutional Network (DSTAGCN), which connects the latest time slice with each past time slice to construct the spatial-temporal graph. DSTAGCN can directly learn the global spatial dependency across time and simultaneously capture the spatial-temporal dependencies through graph convolution. Since fuzzy theory make it possible to represent uncertain relationships, a simplified fuzzy neural network that integrates fuzzy systems and neural networks is designed to help generating the graph adjacency matrix representing the dynamic adjacency correlations. Experiments on public datasets show that our method outperforms baselines with fast convergence.