摘要
燃油成本是航空公司的最主要成本之一,对航空公司的利润率影响巨大。航空燃油中携带量最多的是航程油。合理地预测和优化航程油量可以有效地降低航班额外燃油携带量,并提升机队燃油经济性和有效商业载荷。然而,以往的燃油预测研究数据来源较为单一、适用的运行场景和机型较为有限,从而导致难以得到推广应用。更重要的是,以往研究也未解决航班对安全风险的偏好问题。因此,本研究结合航班航程油量预测的问题特征,构建了基于风险偏好调整的随机森林算法航班航程油量预测模型。该模型根据航班计划、运行环境、飞机构型和性能等选取了特征项,并在数学特性的基础上增加了反映经济性和安全性的模型评价指标。然后,使用某航司的实际航班运行数据对模型进行拟合和测试。数据实验结果显示,模型能够完成符合预期精度和具有实际意义的预测,相较于以往的模型有着更好的表现。本研究成果已经在某航司得到应用,为航班飞行计划制作、签派员加注航班燃油和节能减排分析提供了参考。
Aviation fuel cost is one of the most important cost expenditures for airline companies,exerting huge impact on the profit margin.Trip fuel accounts for the highest proportion of aviation fuel;therefore,reasonable prediction and optimization of the trip fuel volume can effectively reduce the extra fuel carrying volume of the flight,thereby improving the fuel economy and effective commercial consumption of the fleet.However,some limitations exist in previous fuel prediction studies.For example,the data source,the applicable operation scenarios and aircraft models are relatively limited,making them difficult to popularize.More importantly,previous studies have not solved the problem of flight safety risk preference.Therefore,combined with the problem characteristics of flight trip fuel volume prediction,a model for flight trip fuel volume prediction based on the random forest algorithm considering risk preference adjustment is proposed.The model selects the characteristic items according to the flight plan,operation environment,aircraft configuration and performance,and adds the model evaluation index reflecting economy and safety on the basis of mathematical characteristics.Then,the model is fitted and tested with the actual operation data from the routes of an airline company.The experimental results show that the model can complete the prediction with expected accuracy and practical significance,and has better performance than the previous commonly used models.The research results have been applied in an airline company,providing important reference for flight plan making,dispatcher refueling,as well as energy saving and emission reduction analyses.
作者
吴子轩
张宁
高凯烨
彭锐
WU Zixuan;ZHANG Ning;GAO Kaiye;PENG Rui(Digital Committee,Xiamen Airlines,Xiamen 361006,China;School of Economics&Management,Beijing Information Science&Technology University,Beijing 100192,China;Academy of Mathematics&Systems Science,Chinese Academy of Sciences,Beijing 100190,China;School of Economics&Management,Beijing University of Technology,Beijing 100124,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2022年第2期287-296,共10页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(72001027)
北京市教委科技一般项目(KM202111232007)
厦门市重大科技项目(3502Z20201019)。
关键词
预测
机器学习
航程油量
风险偏好
航空运输
航空大数据
prediction
machine learning
trip fuel volume
risk preference
air transportation
aviation big data
