基于聚类与贝叶斯网络的航班离港延误预测模型

Flight departure delay prediction model based on clustering and Bayesian network

航班离港延误可能会导致到达延误以及后续航班的延误,有效地预测离港航班延误时间和概率可以最大程度的降低航班延误给旅客、航空公司和机场带来巨大的损失.针对航班离港延误预测问题,设计了一种基于K-means聚类和贝叶斯网络结合的离港航班延误预测模型.采用K-means聚类的方法将影响离港延误各个变量划分成若干区间.在此基础上建立基于贝叶斯网络的航班离港延误预测模型.运用混淆矩阵计算出该模型的预测精度.实例计算对比结果表明,该预测模型能够计算出离港航班延误时间的等级和概率.并且,对比传统的贝叶斯网络预测模型,采用K-means聚类方法对变量区间进行划分,可以明显提高模型的预测精度.

Flight departure delay may result in delays in arrival and delays in subsequent flights,effectively predicting the delays and probabilities of departing flights to minimize flight delays and causing significant losses to passengers,airlines and airports. Aiming at the problem of flight departure delay prediction,a departure flight delay prediction model based on K-means clustering and Bayesian network was designed. First,the K-means clustering method was used to divide the variables affecting departure delay into several intervals.Based on this,a Bayesian network-based flight departure delay prediction model was established. Finally,the confusion matrix was used to calculate the prediction accuracy of the model. The example calculation comparison results showed that the prediction model can calculate the grade and probability of the departure flight delay time. Moreover,compared with the traditional Bayesian network prediction model,the K-means clustering method was used to divide the variable interval,which can significantly improve the prediction accuracy of the model.