Adaptive Graph Fusion Convolutional Recurrent Network for Traffic Forecasting

Abstract

Traffic flow prediction is the foundation of urban traffic guidance and control, as well as the main function of an intelligent transportation system (ITS). Accurate traffic flow prediction is important for road users, traffic management departments and private enterprises. However, traffic flows usually show a high degree of variability, correlation and complex patterns in both temporal and spatial domain, which makes accurate traffic flow prediction a challenging task. How to capture the potential and dynamic spatial-temporal relationships of traffic data has been the bottleneck issue for intelligent transportation researchers. To solve the above problems, this paper proposes an Adaptive Graph Fusion Convolutional Recurrent Network (AGFCRN) to model the temporal and spatial characteristics of traffic flow data dynamically and adaptively. An adaptive graph fusion convolution is proposed to discover the changing relationships between traffic volumes without a priori knowledge. It uses a self-learned node embedding to generate static graphs and combines current and historical states to generate dynamic graphs at each time step. A gated recurrent layer with residual structure is designed to mitigate the decay of prediction effects in long-term modeling. In addition, an attention layer incorporating self-learned node embedding is introduced in the AGFCRN to efficiently adjust the prediction pattern of each node. Experiments on several public datasets demonstrate that AGFCRN can achieve competitive performance compared to other typical and state-of-the-art methods.