A Dynamic Correlation-Information-Fusion-Based Spatiotemporal Network for Traffic Flow Forecasting

Abstract

Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long-term evolutionary traffic trends. Furthermore, most existing methods often solely depend on the single traffic condition and neglect the enhancement of correlated features collected from traffic sensors in prediction. To this end, we propose a dynamic correlation-information-fusion-based (DCIF) spatiotemporal network for TFF, which models the spatiotemporal correlations of road networks, thereby effectively capturing dynamically changing characteristics. Specifically, a spatiotemporal feature enhancement (STFE) mechanism is employed to capture the directional and location-aware characteristics of traffic flow, thereby enhancing the representation of traffic flow and the capability of spatiotemporal feature extraction. Then, a gated attention unit (GAU) is constructed to meticulously extract the deep dynamic trends inherent within traffic data. Finally, a dynamic feature matrix (DFM) is formulated, incorporating spatial graph convolution to provide comprehensive semantic contextual information. The DFM captures the dynamic topology of the deeper feature network in real time by fusing spatial node information and traffic speed features as correlation information. Extensive experiments demonstrate that DCIF significantly outperforms other baselines in prediction accuracy, thereby further substantiating its validity and reliability in TFF.