FLIGHT DELAY STATE-SPACE MODEL BASED ON GENETIC EM ALGORITHM

基于遗传EM算法的航班延误状态空间模型(英文)

Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.

航班运行过程的高度动态性和随机性,航班延误因素的复杂性和不确定性导致航班延误实时预测成为难题。控制领域的动态数据驱动方法为该问题提供了一种解决方案。然而,要想运用动态数据驱动方法,首先必须建立航班延误状态空间模型来表示系统状态之间、状态与系统输入输出之间的关系。本文对单机延误事件序列进行了分析,创建了一种航班延误状态空间模型,并对其中的输入控制量进行了重点建模。在历史航班运行数据集上,采用遗传EM算法对模型参数进行了极大似然估计,并同时验证了遗传EM算法在优化参数估计和提高计算效率方面的优势。最后,采用Kol-mogorov-Smirnov方法对模型实例进行了假设检验,检验结果表明,所选模型具有较好的拟合优度。