基于贝叶斯网的离港航班滑行时间动态估计

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

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.