Graph dropout self-learning hierarchical graph convolution network for traffic prediction

Abstract

Traffic prediction is a challenging topic in urban traffic construction and management due to its complex dynamic spatial–temporal correlations. Currently, graph neural network achieves good results in traffic prediction, but the existing methods usually do not take into account the multi-layer information of the graph and the redundant transmission of information. Moreover, they do not consider the over-smoothing of the graph. In this paper, we propose a novel graph dropout self-learning hierarchical graph convolution network (DHGCN). Firstly, we design a self-learning hierarchical graph convolution network, which captures spatial features at different layers through multiple self-learning dynamic graphs and avoids redundant information transmission. Secondly, a novel graph dropout structure is proposed to sparsify the graph and avoid the over-smoothing of the graph. Meanwhile, an encoder–decoder architecture with gated residuals is designed to capture dynamic temporal features. In addition, this paper addresses two important traffic forecasting tasks: traffic flow and traffic speed. Extensive experiments with six real-world datasets verify that our method achieves state-of-the-art performance in both traffic flow and traffic speed and consistently outperforms baselines.