标题： 使用混合机器学习方法的航班延误预测：美国主要航空公司案例研究 显示英文标题

标题： Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States

摘要： 自1978年美国航空公司 deregulation（ deregulation）以来，航空业经历了持续增长的航空交通量。 因此，航班延误已成为航空公司和乘客关注的重大问题，从而引发了对影响航班延误的因素（如出发、到达和总延误）的大量研究。 航班延误导致有限资源（如燃料、劳动力和资本）的消耗增加，并且预计在未来几十年内会增加。 为了解决航班延误问题，本研究提出了一种结合深度学习和经典机器学习技术特征的混合方法。 此外，还应用了几种机器学习算法对航班数据进行验证，以验证所提出的模型的结果。 为了衡量模型的性能，计算了准确性、精确度、召回率和F1分数，并生成了ROC和AUC曲线。 该研究还包括对航班数据和每个模型的广泛分析，以获得对美国航空公司有洞察力的结果。 显示英文摘要

摘要： 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.