Attention based spatiotemporal graph attention networks for traffic flow forecasting

Traffic flow forecasting is a crucial task in transportation and necessary for congestion mitigation, traffic control, and intelligent traffic management. Deep learning models can aid in high-accuracy traffic flow forecasting; however, the current research focuses only the ability of the model to capture dynamic spatiotemporal features, and studies on the effect of deeper network layers on spatiotemporal features—a critical factor affecting traffic flow forecasting accuracy—are limited. In this paper, we propose an attention-based spatiotemporal graph attention network (ASTGAT) model designed for network degradation and over-smoothing problems to investigate in-depth spatiotemporal information. Compared to other networks, ASTGAT can capture dynamic spatiotemporal correlations in data and deepen the network to improve prediction accuracy through multiple residual convolution and high-low feature concat. ASTGAT comprises three components that separately model the temporal relationships of the recent, daily, and weekly periods. Each component stacks multiple spatiotemporal blocks constructed using the attention mechanism, dilated gated convolution, and graph attention network. The graph and temporal attention layers capture spatiotemporal information dynamically, and the graph attention layer alleviates the over-smoothing phenomenon to deepen the network. The combined utilization of the attention mechanism and dilated gated convolution layer improves the medium and long temporal span prediction ability. We validated ASTGAT using two open highway datasets, and the results demonstrated that our ASTGAT model effectively extracts in-depth spatiotemporal information and the prediction results outperform those predicted by the current eight baselines. Our research is dedicated to establishing a better scientific basis for intelligent traffic management that can assist in decision making.