计量学报2025，Vol.46Issue(6)：802-812,11.DOI:10.3969/j.issn.1000-1158.2025.06.04
基于自适应时空同步建模的交通流预测
Adaptive Spatio-temporal Synchronous Modeling Based Traffic Flow Prediction
摘要
Abstract
In order to accurately capture the spatio-temporal correlation between different traffic nodes in the road network,a traffic flow prediction method based on adaptive spatio-temporal synchronization modeling was proposed.Firstly,the global node embedding and the bias of different sub-graphs were constructed to generate multiple sub-spatiotemporal graphs that are both related and different,and the different sub-spatiotemporal graphs were spliced to generate a static adaptive spatio-temporal graph,which describes the spatio-temporal correlation between different nodes in the road network from different dimensions.In addition,in order to better model the dynamic spatio-temporal relationship between different nodes,a new dynamic adaptive spatio-temporal synchronization graph construction method was designed,which can effectively describe the dynamic spatio-temporal relationship between different traffic nodes and reduce the computational complexity of the dynamic spatio-temporal graph.Finally,three public datasets derived from a real-world road network,were utilized for testing.The results of the experiments demonstrated that:when compared with nine baseline methods,including LSTM,DCRNN,STGCN,ASTGCN,GWN,STSGCN,STFGNN,STGODE,and S2TAT,the proposed method exhibited superior prediction accuracy.Specifically,on the PEMS08 dataset,in comparison with the optimal baseline method S2TAT,the proposed method achieved reductions of 8.65%,9.25%and 6.04%in mean absolute error(MAE)eMAE,mean absolute percentage error(MAPE)eMAPE and root mean square error(RMSE)eRMSE,respectively.关键词
智能交通系统/交通流量预测/图神经网络/自适应时空图/时空同步建模/深度学习Key words
intelligent transportation systems/traffic flow prediction/graph neural network/adaptive spatio-temporal graph/spatio-temporal synchronous modeling/deep learning分类
通用工业技术引用本文复制引用
叶宝林,戴本岙,苗永超,李灵犀,王翔,吴维敏..基于自适应时空同步建模的交通流预测[J].计量学报,2025,46(6):802-812,11.基金项目
嘉兴市应用性基础研究项目(2023AY11034) （2023AY11034）
浙江省自然科学基金(LTGS23F030002) （LTGS23F030002）
国家自然科学基金(61603154) （61603154）
