南京航空航天大学学报2023，Vol.55Issue(6)：1016-1024,9.DOI:10.16356/j.1005-2615.2023.06.008
基于UKDE和XGBoost的航班过站时间动态预测
Dynamic Prediction of Flight Transit Time Based on UKDE and XGBoost
摘要
Abstract
To improve the accuracy and reliability of flight transit time prediction during airport peak periods,a dynamic flight transit time prediction method combining the unbiased kernel density estimation(UKDE)and the extreme gradient boosting(XGBoost)models is proposed.Firstly,considering the continuous and uncertain changes in the flight density of the model input variables,the UKDE method is used to estimate the flight density of the airport as a dynamic indicator.Secondly,the quantum particle swarm optimization(QPSO)method is introduced to optimize the XGBoost model.Finally,the changes in the input characteristics before and after the occurrence of the preceding flight delays are considered,and the initial prediction results are modified to obtain the two-stage prediction results.The final results verify that the average absolute error of the prediction in peak hours is 7.365 min,which is better than those of random forest(RF),particle swarm optimization(PSO)-XGBoost and XGBoost,and the average absolute error of the modified prediction results is reduced by 3.373 min.The model input parameters,in descending order of sensitivity,are flight density,early arrival time of preceding flights and delayed arrival time.关键词
航空运输/时间预测/极端梯度提升决策树/航班过站保障/核密度估计Key words
air transportation/time prediction/extreme gradient boosting(XGBoost)/flight ground service/nuclear density estimation分类
航空航天引用本文复制引用
吴薇薇,熊奥萍,唐红武..基于UKDE和XGBoost的航班过站时间动态预测[J].南京航空航天大学学报,2023,55(6):1016-1024,9.基金项目
国家自然科学基金(U2033205,U1933118) （U2033205,U1933118）
南京航空航天大学校企协同育人平台工程实践计划项目(2022QYGCSJ59) （2022QYGCSJ59）
南京航空航天大学科研与实践创新计划(xcxjh20220712). （xcxjh20220712）
