DSTF: A Diversified Spatio-Temporal Feature Extraction Model for traffic flow prediction

Traffic flow prediction forms a critical foundation for the management and planning of urban transportation systems. However, the complex spatial interactions among road segments and the dynamic patterns of traffic flow variations across multiple time scales pose significant challenges to improving forecasting accuracy. To address these complexities, this paper introduces a Diversified Spatio-Temporal Feature Extraction (DSTF) Model, designed to effectively mine temporal, spatial, and spatio-temporal cross-correlations. Specifically, in the temporal dimension, a gated convolution-enhanced Res2Net architecture is employed to capture diverse traffic flow patterns across varying time scales. In the spatial dimension, the model integrates global and local perspectives by employing spatial attention mechanisms and a dual-view Geom-GCN, enabling it to capture global spatial dependencies, local geographic neighborhood relationships, and semantic similarity-driven spatial correlations among nodes within the urban road network. For spatio-temporal cross-correlations, a dynamic synchronization aggregation module is developed using spatio-temporal attention, effectively capturing the evolving interactions between nodes or regions across different time slices. Experimental evaluations conducted on four real-world highway traffic datasets demonstrate that DSTF outperforms PDFormer, achieving average improvements of 1.35%, 1.61% and 0.81% in MAE, MAPE and RMSE metrics, respectively. These results highlight the model’s superior capability in extracting and leveraging temporal and spatial features for traffic flow prediction. • Addresses issues related to dynamic traffic flow data. • Captures multi-scale time features of traffic flow temporally. • Models the dynamism of traffic flow in both temporal and spatial dimensions. • Surpasses state-of-the-art methods.