Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting

How to model the spatial-temporal graph is a crucial problem for the accuracy of traffic forecasting. Existing GNN-based work mostly captures spatial dependencies by using a pre-defined graph for close nodes and a self-adaptive graph for distant nodes. However, the pre-defined graphs cannot accurately represent the genuine spatial dependency due to the complexity of traffic conditions. Furthermore, existing methods cannot effectively capture the spatial heterogeneity and temporal periodicity in traffic data. Additionally, small errors in each time step will greatly amplify in the long sequence prediction for a sequence-to-sequence model. To address these issues, we propose a novel framework, MASTRNN, for traffic forecasting. Firstly, a novel mask-adaptive matrix is proposed to enhance the pre-defined graph, which is learned through node embedding. Secondly, we assign identity embeddings to each node and each time step in order to capture the spatial heterogeneity and temporal periodicity, respectively. Thirdly, a multi-head attention layer is employed between the encoder and decoder to alleviate the problem of error propagation. Experimental results on three real-world traffic network datasets demonstrate that MASTRNN outperforms the state-of-the-art baselines.