Probing Traffic Trend Forecasting via Spatial-Temporal Aware Learning-Graph Attention

Abstract

Traffic forecasting plays an extremely important role in many applications such as intelligent transportation and smart cities. However, due to the hidden and complex dynamic spatio-temporal correlations and heterogeneity, achieving high-precision traffic prediction is a challenging task. This paper proposes a new spatio-temporal aware learning graph neural network (STALGNN) for traffic prediction. First, a temporal-aware graph generation module is designed to exploit the spatial-temporal features that the spatial graph may not be able to present. Then, a spatio-temporal joint module is designed to more effectively capture local spatio-temporal correlations. Next, a multi-scale gated convolutions module is proposed to capture gloable dynamic spatio-temporal correlations. Furthermore, STALGNN further learns explicit spatio-temporal correlations through integrated attention mechanisms and stacked graph convolutional networks to handle long-term prediction. Extensive experiments on several real traffic datasets show that the proposed method can achieve the superior performance compared with other baselines.