Dynamic multi-graph convolution recurrent neural network for traffic speed prediction

Abstract

 Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines.