基于XGBoost算法的终端区进场航空器飞行时间预测

Flight time prediction for aircraft approaching terminal area based on XGBoost algorithm

为了高效调配进离场航空器,得到进离场航空器的最佳排序顺序,采用机器学习的方法对终端区进场航空器的飞行时间进行预测.分析终端区航空器飞行特点和进场航空器飞行时间的影响因素并且提出了影响飞行时间预测的22个重要特征.引入密度聚类DBSCAN方法,聚类得到交通流的不同路径类别.建立了基于集成机器学习算法XGBoost的飞行时间预测模型,以云南昆明终端区为例,对模型进行了训练、验证和测试,并以平均相对误差和均方误差为评价指标来分析预测结果的误差.结果表明:与线性回归、支持向量机回归和人工神经网络方法相比,本文模型对飞行时间的预测结果最好,±5 min内的预测准确率达到95.18%.

In order to efficiently deploy approaching and departing aircraft and obtain their best sequencing, machine learning methods are used to predict the flight time of the approaching aircraft in the terminal area. The flight characteristics of the aircraft in the terminal area and the factors affecting the flight time of the approaching aircraft are analyzed. Then, 22 important features that affect the flight time prediction are proposed. The density clustering DBSCAN method is introduced to cluster different path categories of traffic flow. This paper establishes a flight time prediction model based on the integrated machine learning algorithm, XGBoost. Taking the Kunming terminal area in Yunnan province as an example, the model is trained, verified and tested, and the mean relative error and mean square error are used as evaluation indicators to analyze the error of the prediction results. The results show that compared with linear regression, support vector machine regression and artificial neural network methods, the proposed model has the best prediction results for flight time, with a prediction accuracy of 95.18% within 5 min.