Research on Spatiotemporally Consistent Traffic Flow Prediction Based on Adaptive Dynamic Correlation Matrix
To address the issues of insufficient extraction of spatio-temporal intrinsic correlation and inadequate char-acterization of spatial dynamics in existing traffic flow prediction research,a traffic flow prediction model is proposed based on an adaptive dynamic spatial correlation matrix.The model constructs a spatio-temporal feature consistency extraction module by embedding a spatial feature extraction module and a multi-feature hierarchical fusion module in-side the Transformerencoder model,combined with its multi-head self-attention mechanism,to achieve the full extraction of the spatio-temporal intrinsic correlation of traffic flow.The spatial feature extraction module constructs the adaptive dy-namic spatial correlation matrix to fully characterize the dynamic spatial features of the road network's neighborhood,while the spatial feature filtering network eliminates redundant information and highlights key influential features.Addi-tionally,the multi-feature hierarchical fusion module conducts detailed and comprehensive mining of spatial and tem-poral correlations by hierarchically fusing the original temporal and spatial features of traffic flow.The experiments are tested on the PeMS04 and PeMS08 public datasets of the high-speed road network,and the results show that the proposed model has better prediction results compared to other baseline models.
万方数据
维普
