With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.

中文翻译：

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一种改进的航班延误预测人工神经网络模型

随着空中交通的限制和航班数量的迅速增加，航班延误变得越来越频繁。航班延误会导致乘客的财务和时间损失，并增加航空公司的运营成本。因此，建立准确的航班延误预测模型对于构建高效的航空运输系统至关重要。航空运输系统数据量巨大，运行模式复杂，适合采用机器学习方法进行分析。本文讨论了可能影响航班延误的因素，并提出了一种新的航班延误预测模型。基于航班延误数据库，采用K-means聚类算法定义了五个预警级别。通过灰色关联分析（GRA）算法提取与飞行运行相关的关键因素后，介绍了一种改进的机器学习算法，称为GRA——遗传算法（GA）——反向传播神经网络，GRA-GA-BP，由GA优化。计算结果表明，与优化前的模型和前人的其他两种算法相比，所提出的基于GRA-GA-BP算法的预测模型具有更高的预测精度和稳定性。在运行效率和内存消耗方面，也有不错的表现。本文的分析表明，该模型可以对航班延误提供有效的预警，并可以帮助航空公司提前干预具有异常趋势的航班。计算结果表明，与优化前的模型和前人的其他两种算法相比，所提出的基于GRA-GA-BP算法的预测模型具有更高的预测精度和稳定性。在运行效率和内存消耗方面，也有不错的表现。本文的分析表明，该模型可以对航班延误提供有效的预警，并可以帮助航空公司提前干预具有异常趋势的航班。计算结果表明，与优化前的模型和前人的其他两种算法相比，所提出的基于GRA-GA-BP算法的预测模型具有更高的预测精度和稳定性。在运行效率和内存消耗方面，也有不错的表现。本文的分析表明，该模型可以对航班延误提供有效的预警，并可以帮助航空公司提前干预具有异常趋势的航班。