| 基于VMD-MD-Clustering方法的航班延误等级分类 |
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| 引用本文： | 王兴隆,许晏丰,纪君柔.基于VMD-MD-Clustering方法的航班延误等级分类[J].交通信息与安全,2022,40(3):171-178. |
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| 作者姓名： | 王兴隆 许晏丰 纪君柔 |
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| 作者单位： | 1.中国民航大学空中交通管理学院 天津 300300 |
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| 基金项目： | 国家重点研发计划项目2020YFB1600101天津市教育委员会自然科学重点基金项目2020ZD01 |
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| 摘 要： | 针对航班数量逐年增加导致的航班延误日益频繁问题，研究对航班延误等级分类的方法，从而为制定针对性措施，降低航班延误造成的损失提供理论基础。从时间、空间和效率3个方面确定航班延误时间、航班飞行时间、延误影响人数和航程这4个数值属性指标，以及过站是否经停、飞机载客量2个类属性指标，共计6个评估指标构建航班延误等级分类模型。提出了1种基于变分模态分解（VMD）、马氏深度（MD）函数和K-means数据聚类（Clustering）的航班延误等级分类方法（以下简称V-M-C方法）。V-M-C方法将非正态、非平稳的多维航班延误数据视作含噪声的信号序列进行处理，通过VMD降噪获得正态、稳定的多维信号数据；利用MD函数进行降维处理得到一维的稳定信号数据；使用K-means方法对得到的一维数据进行聚类，对航班延误等级分类。为确定航班延误等级分类精确性，采用带惩罚权重的支持向量机（SVM）对分类结果进行分析，可以在一定程度上提高V-M-C方法的普适性。以某大型枢纽机场某月的航班运行数据为例，只使用K-means算法的航班延误等级分类精度为81.9%，而V-M-C方法对航班延误等级分类精度可提升至95.41%。实验结果表明，V-M-C方法的分类准确率更高，能够帮助机场根据相应延误等级制定预案，保障航班整体运行正点率。
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| 关 键 词： | 航空运输 航班延误 变分模态分解 数据深度 聚类算法 支持向量机 |
| 收稿时间： | 2021-12-27 |
Classification of the Level of Flight Delay Based on a VMD-MD-Clustering Method |
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| Affiliation: | 1.College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China2.China Eastern Airlines Corporation Limited, Shanghai 200135, China |
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| Abstract: | Due to the increasing number of flights, the flight delay has been increasing in recent years. To mitigate this problem, a method for classifying flight delays is studied, which provides a theoretical basis for developing relevant measures and reducing the number of flight delays. A classification model is proposed based on six indicators from time, space, and efficiency aspects. These indicators include four numerical indicators, namely"delay time", "flying duration", "number of people affected by the delay", and"voyages affected by the delay", as well as two attribute indicators, i.e., "stopover flight or not"and"passenger capacity of delayed aircraft". Then, a method for classifying levels of flight delays is proposed, which combines the variational mode decomposition(VMD), Mahalanobis depth(MD)function, and K-means clustering, named as"VMD-MD-Clustering"(V-M-C)method. Firstly, non-normal and non-stationary multi-dimensional delay data are treated as a signal sequence with noise. Secondly, the VMD method is used to stabilize and normalize the delay data. Thirdly, the MD function is used to reduce the dimensionality of the data to one dimension(1D). Fourthly, the K-means method is applied to cluster the 1D signal data and output the level of flight delay. Finally, to evaluate the proposed method, a weighted support vector machine(SVM)is applied to analyze the classification results. The operation data collected from an airport in one month are used for validation. The validation results show that the proposed V-M-C method have an accuracy of 95.41%, which outperforms the K-means method with an accuracy of 81.9%. Study results show that the proposed V-M-C method has an enhanced accuracy and therefore, it is potentially useful for formulating flight-delay disposal plans and improving the punctuality of flight operations. |
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