摘要
准确预测进港航班滑入时间对合理调配航班保障资源和提高机场场面运行效率具有重要意义,可有效克服各大机场粗放式预测航班进港时刻的不足,为此提出一种基于机器学习模型的滑入时间预测方法。以首都机场为具体研究对象,分析进港航班滑入时间的影响因素并构建特征集;将线性回归、K-最近邻、支持向量机、决策树、随机森林和梯度提升回归树6种在滑出时间预测方面得到广泛应用的机器学习模型用于进港航班滑入时间预测。研究结果表明:在误差范围±3 min内6种机器学习模型的预测精度均超过90%,表明特征集的构建和模型的选择是有效的;综合预测性能与模型拟合评估结果,梯度提升回归树模型的预测效果最好;在梯度提升回归树模型上场面流量特征的贡献度最大,新引入的跨区特征对预测模型的贡献度超过了大部分传统特征。
Accurate prediction of flight taxi-in time has a significant meaning in allocating aircraft support resources reasonably and improving airport surface movement efficiency.Therefore,a method of taxi-in time prediction based on machine learning model is proposed.It can effectively overcome the deficiency of extensive aircraft arrival time prediction in major airports currently.Using Beijing Capital International Airport as the research object,we firstly analyzed the factors that influence the taxi-in time and created the feature set.Next,we applied various techniques that are commonly used to predict taxi-out times,such as linear regression,K-nearest neighbor,support vector regression,decision tree,random forest,and gradient boosting regression tree,to predict the taxi-in time.The results show that the prediction accuracy of the six machine learning models is over 90% within ±3 min,which means that the construction of the feature set and the selection of models are effective.The gradient boosting regression tree model has the best performance based on the prediction results and model fitting evaluation results.The prediction results of gradient boosting regression tree show that the surface traffic flow features contribute most to the prediction model,and the newly proposed cross-regional feature contributes more than most traditional features.
作者
唐小卫
丁叶
张生润
任思豫
吴佳琦
TANG Xiaowei;DING Ye;ZHANG Shengrun;REN Siyu;WU Jiaqi(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2024年第7期2218-2224,共7页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家自然科学基金(61603178,U2333204,U2233208)。
关键词
航空运输
机场场面运行
滑行时间预测
机器学习
梯度提升回归树
air transportation
airport surface movement
taxi time prediction
machine learning
gradient boosting regression tree
