南京航空航天大学学报2023，Vol.55Issue(6)：1033-1043,11.DOI:10.16356/j.1005-2615.2023.06.010
基于VMD-LSSVM的扇区流量短期预测
Short Term Prediction of Sector Traffic Based on VMD-LSSVM
摘要
Abstract
Short term prediction of sector traffic is the premise of accurately implementing sector traffic optimization and management measures.Based on the decomposition integration prediction methodology,a vibrational mode decomposition least square support vector machine(VMD-LSSVM)prediction model is established.Firstly,the VMD method is applied to decompose the traffic into several sectors.Then,the LSSVM model is used to predict the modes.The modal prediction results are added and integrated to obtain the final prediction value.The calculation results show that the prediction accuracy of the VMD-LSSVM model is 0.97 in 1-6 h and 0.94 in 7-12 h.Compared with the first mock exam model of autoregressive integrated moving average model(ARIMA),back propagation(BP)and LSSVM,the prediction accuracy of the VMD-LSSVM model 1-6 h increased by 11.5%,7.8%,4.3%,respectively,and 2.1%,6.6%,5.4%compared with compete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)-LSSVM,CEEMDAN-BP and VMD-BP,respectively.Compared with 30 min and 15 min statistical scales,the prediction accuracy is improved by 6.6%and 19.8%,respectively.For the eight experiments of time universality,the prediction accuracy is more than 0.94.For the experiments of 27 sectors,the prediction accuracy of 24 sectors is more than 0.9.The example results show that the VMD-LSSVM model has good prediction performance and good universality,and it is feasible and effective for short-term prediction of sector traffic.关键词
航空运输/空中交通流量管理/流量短期预测/变分模态分解/最小二乘支持向量机Key words
transport aviation/air traffic flow management/short term flow forecast/variational modal decomposition/least square support vector machine分类
交通工程引用本文复制引用
王飞,孙鹏飞..基于VMD-LSSVM的扇区流量短期预测[J].南京航空航天大学学报,2023,55(6):1033-1043,11.基金项目
天津市应用基础多元投入基金重点项目(21JCZDJC00840) （21JCZDJC00840）
中央高校基本科研业务费专项资金项目(3122019129). （3122019129）
