Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (24): 66-71.DOI: 10.3778/j.issn.1002-8331.1709-0044

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Dynamic estimation about estimated taxi-out time based on Bayesian network

XING Zhiwei1, JIANG Junxian1, LUO Xiao2, LUO Qian2   

  1. 1.College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
    2.Information Filiale, The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, China
  • Online:2018-12-15 Published:2018-12-14


邢志伟1,蒋骏贤1,罗  晓2,罗  谦2   

  1. 1.中国民航大学 电子信息与自动化学院,天津 300300
    2.中国民航局 第二研究所 信息技术分公司,成都 610041

Abstract: 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.

Key words: air transportation, Estimated Taxi-Out Time(EXOT), Bayesion network estimation, department aircraft taxing process, incremental learning, expectation maximization

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。

关键词: 航空运输, 离港航班可变滑行时间, 贝叶斯网估计, 航班离港滑行过程, 增量学习, 期望优化算法