Spatial-temporal correlation graph convolutional networks for traffic forecasting

Abstract

Traffic forecasting, as a fundamental and challenging problem of intelligent transportation systems (ITS), has always been the focus of researchers. Nevertheless, accurate traffic forecasting still exists some problems due to the complex spatial-temporal dependencies and irregularities of traffic flows. Most of the existing methods typically use the spatial adjacency matrix and complicated mechanism to model spatial-temporal relationships separately, while ignoring the latent spatial-temporal correlations. In this paper, a novel architecture is proposed named spatial-temporal correlation graph convolutional networks (STCGCN) for traffic prediction. First, an informative fused graph structure is constructed to better learn the complex spatial-temporal correlations, which breaks the limitation that the general spatial adjacency matrix cannot reflect temporal correlations. Moreover, spatial-temporal correlation graph convolution and gated temporal convolution are performed in parallel and they are integrated into a unified layer, which enables capturing both local and global spatial-temporal dependencies simultaneously. By stacking multiple layers, STCGCN can learn more long-range spatial-temporal dependencies. Experimental results on five public traffic datasets demonstrate the effectiveness and robustness of the proposed STCGCN in urban traffic forecasting.