现代电子技术2024，Vol.47Issue(8)：31-36,6.DOI:10.16652/j.issn.1004-373x.2024.08.005
基于VMD-ISSA-LSTM的短时交通流预测研究
Research on short-term traffic flow prediction based on VMD-ISSA-LSTM
摘要
Abstract
In allusion to the problems of strong random fluctuation,low reliability and poor prediction accuracy of urban short-term traffic flow,a short-term traffic flow prediction model(VMD-ISSA-LSTM)is established by coupling variational mode decomposition(VMD)and improved sparrow search algorithm(ISSA)with long short-term memory(LSTM).VMD is used to decompose the historical original traffic flow data.Then,the standard SSA algorithm is improved by means of the good-point set,sine function perturbation and Tent chaotic mapping strategy to enhance the optimization ability of ISSA algorithm.Each component is sent to ISSA-LSTM for prediction,and the prediction results are linearly superimposed to obtain the traffic flow prediction value.The model is verified by the historical traffic data from November 1,2018 to November 30,2018 at the intersection of Zhongshan North Road and Caoyang Road in Shanghai.The results show that in comparison with the traditional prediction models such as VMD-SSA-LSTM,LSTM and VMD-LSTM,the average absolute percentage error of the prediction results of the VMD-ISSA-LSTM model is 1.278 4%,which can be better applied to short-term traffic flow prediction.关键词
短时交通流预测/变分模态分解/改进麻雀搜索算法/长短期记忆神经网络/佳点集/正弦函数扰动/Tent混沌映射Key words
short-term traffic flow forecasting/variational mode decomposition/improved sparrow search algorithm/long short-term memory neural network/good-point set/sine function perturbation/Tent chaotic map分类
电子信息工程引用本文复制引用
庞学丽,宋坤,姚红云,李一博,曹志富..基于VMD-ISSA-LSTM的短时交通流预测研究[J].现代电子技术,2024,47(8):31-36,6.基金项目
国家自然科学基金青年科学基金项目(51008321) （51008321）
重庆市教育委员会-青年项目(KJQN202100715) （KJQN202100715）
重庆交通大学研究生科研创新项目(2022S0035) （2022S0035）
