Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic forecasting is essential for transportation services such as traffic control and route planning. However, accurate traffic prediction is challenging due to complex characteristics of traffic data. Existing solutions may not adequately capture dynamic and nonlinear spatial-temporal correlations in traffic network. In this paper, we propose a novel Multi-Hierarchical Spatial-Temporal Graph Convolutional Networks (MH-GCN) to solve traffic flow forecasting problem. It adopts an attention-based encoder-decoder structure. Firstly, MH-GCN uses a spatial-temporal attention mechanism in encoder to model dynamic spatial and nonlinear temporal correlations. Then, a transformer attention layer is positioned between encoder and decoder, which is used to model the correlation of historical and future time. Finally, the decoder utilizes Convolution Group, Pooling Group, and Dilation Group to extract different hierarchical of characteristics from the already modeled features, and then the fused results are used for predicting future traffic conditions. Experiments on two real traffic datasets demonstrate that the proposed MH-GCN obtains improvements over the state-of-the-art baselines.