基于数据挖掘的指定航班计划延误预测方法  

Prediction Method of Flight Delay in Designated Flight Plan Based on Data Mining

作　　者：张成伟[1] 罗凤娥[1] 代毅 ZHANG Cheng-wei;LUO Feng-e;DAI Yi(College of Air Traffic Management,Civil Aviation Flight University of China,Guanghan,Sichuan 618300,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618300

出　　处：《计算机科学》2020年第S02期464-470,485,共8页Computer Science

基　　金：民航局安全能力建设项目(OMSA1805);中央高校教育教学改革专项(E20180302);中国民用航空飞行学院青年基金项目(XM4043);航空运行控制技术研究所(JG201935)。

摘　　要：针对现有航班延误预测方法较少从指定航班计划延误预测角度进行分析,提出一种研究离港航班计划中指定某航班计划发生延误情况的预测方法。首先,分析大量航班历史运行数据,挖掘数据内在特征。其次,通过建立航班数据的贝叶斯网络分析模型,得到不同条件下航班延误情况的概率分布;以动态贝叶斯网络(Dynamic Bayesian Networks,DBN)推理为主要建模方法,研究了动态贝叶斯网络推理和仿真过程,提出了一种用于构建航班延误预测模型的新方法,建立了实际航班数据的隐马尔可夫(Hidden Markov Model,HMM)延误预测分析模型,利用隐马尔可夫模型中解码问题Viterbi算法实现了指定航班延误时间的预测。最后,以某航空公司全年航班运行数据为例进行实例仿真及验证,结果表明,该方法实现了航班延误预测对象的精确性。In view of the fact that the existing flight delay prediction methods are rarely analyzed from the perspective of the de-signated flight plan delay prediction,a prediction method to study the delay situation of a specified flight plan in the departure flight plan is proposed.First,analyzing the intrinsic characteristics of a large number of historical flight data mining data.Secondly,this research employs Dynamic Bayesian Network inference as the main modeling method to obtain the probability distribution under different conditions of flight delay.By studying the Dynamic Bayesian Network inference process and simulation,this paper presents a new method for the construction of the flight delay prediction model which is to establish Hidden Markov flight delay prediction model based on the real flight data.Using the Viterbi algorithm of Hidden Markov model decoding problem to predict the flight delay time.Finally,taking an airline’s full-year flight operation data as an example for example simulation and verification,the results show that this method improves the accuracy of flight delay prediction objects.

关 键 词：指定航班计划 数据挖掘 贝叶斯网络 隐马尔可夫模型 延误预测 

分 类 号：F560[经济管理—产业经济]