基于贝叶斯网的离港航班滑行时间动态估计
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摘要
为提升离港航班运行效率,根据机场协同决策规范(A-CDM)中关于离港航班可变滑行时间(EXOT)的有关规定,分析了相关影响因素。根据数据分析处理和民航专家知识建立了一种基于贝叶斯网的离港航班滑行时间动态估计模型。贝叶斯网是一种将概率统计应用于复杂领域、进行不确定性推理和数据分析的工具。应用其增量学习特点对模型进行动态调整,实现了对场面实时变化的把控。以国内某大型枢纽机场为例,使用期望优化(EM)算法实现了对随机缺失数据的处理,并验证了模型的有效性。对实验结果与该机场实际运行数据对比表明,所建模型能有效地估计离港航班滑行时间且具有较高的置信度。
Estimated Taxi-Out Time(EXOT)is defined by the Airport-Collaborative Decision Making(A-CDM), and for improving the efficiency of aircraft department, the influencing factors related to EXOT are analyzed by relevant provisions in A-CDM. A dynamic estimation model of EXOT based on Bayesion network is established according to the analysis of historical data and suggestion of civil aviation experts. Bayesion network is an effective tool to do the data analysis and uncertainty inference by using probability statistics knowledge in complex areas. The control of airport surface operation can be achieved by dynamically modulating the Bayesion network model with incremental learning capacity. Taking a large domestic hub airport as an example, the Expectation Maximization(EM)algorithms can be used to solve the problem of data missing at random, verifying the validity of this model. After comparing the experimental results with the data of actual surface operation, this model can estimate EXOT effectively in a high confidence.
Estimated Taxi-Out Time(EXOT)is defined by the Airport-Collaborative Decision Making(A-CDM), and for improving the efficiency of aircraft department, the influencing factors related to EXOT are analyzed by relevant provisions in A-CDM. A dynamic estimation model of EXOT based on Bayesion network is established according to the analysis of historical data and suggestion of civil aviation experts. Bayesion network is an effective tool to do the data analysis and uncertainty inference by using probability statistics knowledge in complex areas. The control of airport surface operation can be achieved by dynamically modulating the Bayesion network model with incremental learning capacity. Taking a large domestic hub airport as an example, the Expectation Maximization(EM)algorithms can be used to solve the problem of data missing at random, verifying the validity of this model. After comparing the experimental results with the data of actual surface operation, this model can estimate EXOT effectively in a high confidence.
引文
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[2]Lee H,Balakrishnan H.Optimization of airport taxiway operations at detroit metropolitan airport(DTW)[C]//Aviation Technology,Integration,and Operations,2013.
[3]Sogno X,Roling P C,Maan R,et al.Evaluation of a dynamic taxi-time estimation model using process-based segmentation in an A-CDM environment[C]//Aviation Technology,Integration&Operations Conference,2013.
[4]Sandberg M,Simaiakis I,Balakrishnan H,et al.A decision support tool for the pushback rate control of airport departures[J].IEEE Transactions on Human-Machine Systems,2014,44(3):416-421.
[5]Rathinam S,Montoya J,Jung Y.An optimization model for reducing aircraft taxi times at the dallas fort worth international airport[C]//26th International Congress of the Aeronautical Sciences.Anchorage:AIAA,2008:1-14.
[6]Chatterji G.Wheels-off time prediction using surface traffic metrics[J].AIAA Journal,2012:1-14.
[7]Newman D J.Modeling and control of airport departure processes for emissions reduction[D].Massachusetts Institute of Technology,2009.
[8]Balakrishnan H,Jung Y.A framework for coordinated surface operations planning at Dallas-fort worth international airport[C]//AAAI Guidance,Navigation and Control Conference and Exhibit,2007.
[9]Roling P C,Visser H G.Optimal airport surface traffic planning using mixed-integer liner programming[J].International Journal of Aerospace Engineering,2008(1):1-11.
[10]Alligier R,Gianazza D,Durand N,et al.Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights[J].Transportation Research Part C:Emerging Technologies,2013,36(13):45-60.
[11]冯兴杰,岳鹏涛.基于动态优先级的机场滑行道调度优化算法[J].计算机工程与设计,2016,37(4):999-1003.
[12]郭艳军.贝叶斯网学习方法及应用研究[D].武汉:华中科技大学,2009.
[13]Lee H.Airport surface traffic optimization and simulation in the presence of uncertainties[D].Massachusetts Institute of Technology,2014.
[14]Simaiakis I,Khadilkar H,Balakrishnan H,et al.Demonstration of reduced airport congestion through pushback rate control[C]//9th USA/Europe Air Traffic Management R&D Seminar,Berlin,Germany,June 2011.
[15]Visser H G,Roling P C.Optimal airport surface traffic planning using mixed integer linear programming[C]//AIAA Aviation Technology,Integration and Operations(ATIO)Conference,Denver,CO,2003.
[16]潘浩.机场协同决策系统的设计与实现[D].大连:大连理工大学,2014.
[17]文光.民航局发布2015年全国民航航班运行效率报告[N].中国航空报,2016-07-19(1).