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

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.