本文選題：航班撤輪擋時(shí)刻預(yù)測(cè)　切入點(diǎn)：因子分析　出處：《中國(guó)民航大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：航班撤輪擋時(shí)刻是所有艙門(mén)關(guān)閉,完成撤廊橋,推車(chē)可用,收到許可后立即推出的時(shí)刻,是航班進(jìn)程監(jiān)控的重要里程碑事件,能指導(dǎo)空管提前預(yù)判航班預(yù)起飛隊(duì)列,以及機(jī)場(chǎng)和航空公司進(jìn)行地面保障服務(wù)的重要時(shí)間節(jié)點(diǎn)。因此,航班撤輪擋時(shí)刻預(yù)測(cè)逐漸成為民航熱點(diǎn)研究問(wèn)題。現(xiàn)有預(yù)測(cè)方法采取經(jīng)驗(yàn)統(tǒng)計(jì)方法得到航班撤輪擋時(shí)刻,其核心思想是首先統(tǒng)計(jì)歷史數(shù)據(jù)得到平均最小過(guò)站時(shí)間,然后將預(yù)計(jì)降落時(shí)間、預(yù)計(jì)滑入時(shí)間和平均最小過(guò)站時(shí)間求和,以作為航班撤輪擋時(shí)刻的估計(jì)值。對(duì)于大型樞紐機(jī)場(chǎng),不同的機(jī)型、航班時(shí)刻和地勤保障單位,航班過(guò)站時(shí)間有較大差異。因此,使用統(tǒng)一的過(guò)站時(shí)間預(yù)測(cè)航班撤輪擋時(shí)刻會(huì)產(chǎn)生較大誤差,影響機(jī)場(chǎng)、航空公司和空管的協(xié)同運(yùn)行效率。針對(duì)此問(wèn)題,本文開(kāi)展航班撤輪擋時(shí)刻預(yù)測(cè)研究,創(chuàng)新性采用機(jī)器學(xué)習(xí)方法構(gòu)建航班撤輪擋時(shí)刻預(yù)測(cè)模型,而不再是歷史數(shù)據(jù)的估計(jì)。直觀上,可直接利用航班撤輪擋時(shí)刻的前序所有里程碑事件構(gòu)建多元線性回歸模型,但是不相關(guān)或弱關(guān)聯(lián)的里程碑事件會(huì)對(duì)航班撤輪擋時(shí)刻預(yù)測(cè)產(chǎn)生偏差。針對(duì)此問(wèn)題,本文提出了一個(gè)基于因子分析的航班撤輪擋時(shí)刻預(yù)測(cè)模型。首先,開(kāi)展航班里程碑事件的因子分析,分析各里程碑事件間的相關(guān)性,通過(guò)計(jì)算因子載荷、因子旋轉(zhuǎn)確定與航班撤輪擋時(shí)刻相關(guān)的關(guān)鍵里程碑事件。其次,將關(guān)鍵里程碑事件作為特征變量,利用多元線性回歸的方法建立預(yù)測(cè)模型。實(shí)驗(yàn)表明,誤差范圍±5和±10分鐘內(nèi),模型平均預(yù)測(cè)準(zhǔn)確率分別可達(dá)70%和90%以上,高于A-CDM經(jīng)驗(yàn)方法、基于主成分分析和基于支持向量回歸(SVR)等航班撤輪擋時(shí)刻預(yù)測(cè)算法。由于基于因子分析的航班撤輪擋時(shí)刻預(yù)測(cè)模型本質(zhì)上是融入1L正則化約束的多元線性回歸問(wèn)題。模型僅考慮前序里程碑事件對(duì)航班撤輪擋時(shí)刻的影響,忽略了航班登機(jī)口、天氣、車(chē)輛調(diào)度及機(jī)場(chǎng)是否處于繁忙時(shí)刻等諸多難以量化因素的間接影響,無(wú)法更準(zhǔn)確地預(yù)測(cè)航班撤輪擋時(shí)刻。針對(duì)上述情況,本文提出一個(gè)基于隱藏變量的航班撤輪擋時(shí)刻預(yù)測(cè)模型,將上述無(wú)法量化因素的影響通過(guò)隱藏變量在模型中體現(xiàn)。在訓(xùn)練階段,由于最大化數(shù)據(jù)似然概率的優(yōu)化目標(biāo)耦合了模型參數(shù),導(dǎo)致傳統(tǒng)梯度上升等算法無(wú)法直接使用,為此采用變分EM算法求解模型參數(shù),其中期望計(jì)算旨在優(yōu)化求解近似分布的模型參數(shù),而期望最大則是最大化似然概率求解回歸預(yù)測(cè)模型參數(shù)。在基準(zhǔn)數(shù)據(jù)集合上的實(shí)驗(yàn)表明,該模型比基于因子分析的航班撤輪擋時(shí)刻預(yù)測(cè)模型能夠取得更好的均方誤差以及準(zhǔn)確率。
[Abstract]:The departure time is the time when all the doors are closed, the bridge is removed, the trolley is available, and immediately after receiving the permission, it is an important milestone in the monitoring of the flight process. It can guide the air traffic control to predict the flight pre-departure queue in advance. And the important time node for the ground support service of the airport and the airline. Therefore, the prediction of the departure time of the flight is gradually becoming a hot research issue in civil aviation. The existing forecasting methods adopt the empirical statistical method to get the time of the flight withdrawal. Its core idea is to first get the average minimum transit time by statistics of historical data, and then sum up the estimated landing time, the expected slide in time and the average minimum stop time to be used as the estimated value of the departure time of the flight. For a large hub airport, Different aircraft types, flight times and ground handling support units, and flight transit times are quite different. Therefore, the use of unified transit time to predict the departure time of a flight will result in greater errors, which will affect the airport. Aiming at this problem, this paper develops the research on the prediction of flight withdrawal time, and innovatively uses machine learning method to construct the model of flight withdrawal time prediction. It is not the estimation of historical data. Intuitively, multiple linear regression models can be built directly by using all the milestone events in the pre-order of the departure time of the flight. However, irrelevant or weakly related milestone events can cause deviation to the prediction of the departure time. In order to solve this problem, this paper proposes a prediction model based on factor analysis. Carries on the factor analysis of the flight milestone event, analyzes the correlation between each milestone event, through the calculation factor load, the factor rotation determines the key milestone event related to the flight withdrawal time. Secondly, Taking the key milestone events as characteristic variables, the prediction model is established by using multiple linear regression method. The experimental results show that the average prediction accuracy of the model can reach more than 70% and 90% within 鹵5 and 鹵10 minutes, respectively, which is higher than that of A-CDM empirical method. Based on principal component analysis (PCA) and support vector regression (SVR) algorithm, the prediction model of flight withdrawal time is essentially a multivariate linear regression model with 1L regularization constraint. The model only considers the influence of the pre-order milestone event on the departure time of the flight. Ignoring the indirect effects of many difficult factors, such as flight gate, weather, vehicle scheduling and whether the airport is in busy hours, it is impossible to predict more accurately the departure time of the flight. In this paper, we propose a prediction model of flight withdrawal time based on hidden variables. The influence of the above unquantifiable factors is reflected in the model by hidden variables. Because the optimization objective of maximizing the likelihood probability of the data coupled the model parameters, the traditional gradient rise algorithm can not be used directly, so the variational EM algorithm is used to solve the model parameters. Among them, the expected calculation is aimed at optimizing the model parameters for solving approximate distribution, while the maximum expectation is to maximize the likelihood probability to solve the regression prediction model parameters. The experiments on the set of datum data show that, This model can obtain better mean square error and accuracy than the prediction model based on factor analysis.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V355;O212.1

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