南京航空航天大学学报（英文版）2021，Vol.38Issue(6)：959-966,8.
基于支持向量回归机和参数优化的空中交通管制员需求量预测
Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization
摘要
Abstract
As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system.According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR)based on grid search and cross-validation.Then the model predicts the demand for air traffic controllers in seven regions.Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted.The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73％,and the prediction accuracy is higher than traditional regression algorithms.Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase.It is expected that the controller increment will be about 816 by 2028.The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.关键词
空中交通管制员/需求预测/支持向量回归机/网格搜索/交叉验证Key words
air traffic controller/demand forecast/support vector regression(SVR)/grid search/cross-validation分类
社会科学引用本文复制引用
张亚丽,李姗,张洪海..基于支持向量回归机和参数优化的空中交通管制员需求量预测[J].南京航空航天大学学报（英文版）,2021,38(6):959-966,8.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(No.71971114). （No.71971114）
