The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

本研究解决了航空业日益严重的航班延误问题，通过结合深度学习与经典机器学习技术，提出了一种混合方法。研究发现，该模型在评估指标如准确率、精确度、召回率和F1分数上的表现优异，能够为美国航空公司提供有价值的分析和洞察，以有效应对航班延误的挑战。

利用混合机器学习方法预测航班延误：美国主要航空公司的案例研究