交通信息与安全2024，Vol.42Issue(6)：112-122,11.DOI:10.3963/j.jssn.1674-4861.2024.06.012
基于时序数据分解重构的短时交通流预测方法
A Short-term Traffic Flow Prediction Method Based on Time Series Data Decomposition and Reconstruction
摘要
Abstract
In order to extract signal components with rich feature information from short-term traffic flow data and further improve the prediction accuracy,a short-term traffic flow prediction method based on temporal data decom-position reconstruction is constructed by combining the parameter optimization based variational mode decomposi-tion(VMD),recurrence quantification analysis(RQA),and bidirectional gated recurrent unit(BIGRU)models.The osprey cauchy sparrow search algorithm(OCSSA),which integrates the osprey and cauchy variants,is used to deter-mine the number of modal components and the penalty factor of the variational modal decomposition,and to obtain the relatively smooth intrinsic modal components.The decomposed modal components are reconstructed into the de-terministic components,fluctuating components,and trend components through the recursive quantitative analysis.Based on this,for each reconstructed component the BIGRU prediction model is constructed,and the predicted val-ues of each reconstructed component are nonlinearly integrated using the BIGRU prediction model to obtain the fi-nal prediction results.The measured data of the flow of Shanghai North-South Expressway and California Express-way network are used for validation,The results show that in the NBDX08(1)dataset,the corresponding mean abso-lute error,root-mean-square error,and mean absolute percentage error are reduced by 29.1%,24.5%,and 46.1%on average,respectively,compared with the other models;and the errors in the dataset of No.760101 are reduced by 19.05%,19.69%,and 16.46%on average.These verify that the proposed method for the decomposition and recon-struction of different components can accurately capture and learn the characteristics of traffic flow components,which further improves the prediction accuracy while controlling the computational complexity of the model.关键词
交通运输规划/短时交通流预测/双向门控循环单元/变分模态分解/递归量化分析Key words
transport planning/short-term traffic flow forecasts/bidirectional gated recurrent unit/variational mode decomposition/recurrence quantification analysis分类
交通工程引用本文复制引用
邴其春,赵盼盼,任参政,王雪倩,赵一鸣..基于时序数据分解重构的短时交通流预测方法[J].交通信息与安全,2024,42(6):112-122,11.基金项目
国家自然科学基金项目(52272311)、山东省重点研发计划项目(2019GGX101038)资助 （52272311）
