Traffic Flow Forecasting Model Based on Spatio-temporal Probability SparseAdaptive Hybrid Graph Convolution Network

Traffic flow forecasting is the basis for the dynamic control and application of Intelligent Transportation Systems (ITS), which is of significant practical importance in reducing road congestion. The intricate spatial and temporal connections of traffic flow continue to provide a significant obstacle to accurate traffic flow forecasting. To capture the dynamic spatio-temporal features of traffic flow simultaneously, this paper proposes a novel Spatiotemporal Probability Sparse Adaptive Hybrid Graph Convolution Network (STPASHG) for traffic flow prediction, which is mainly composed of a gated temporal convolution network (Gated TCN), an adaptive hybrid graph convolution module (AHGCM), a spatio-temporal convolution block (ST-Conv block), and a Probability sparse self-attention mechanism (ProbSSAtt block). Among them, Gated TCN utilizes dilated causal convolutional networks at different granularity levels to capture the temporal dependence of traffic flow, and AHGCM utilizes static adaptive graph learning (SAGL), dynamic graph learning (DGL), and spatial gate fusion mechanisms to synchronize and adequately capture dynamic spatiotemporal features. The ProbSSAtt block combines the dynamic temporal and spatial features and promotes the STPASHG model to effectively make medium- and long-term predictions. The experimental results demonstrate that the STPASHG model suggested in this research can successfully extract the dynamic spatio-temporal aspects of the traffic flow and has the best prediction performance compared to the widely used baseline approaches.