Capturing Local and Global Spatial-Temporal Correlations of Spatial-Temporal Graph Data for Traffic Flow Prediction

Abstract

Traffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.