融合深度与传统机器学习，预测美国主要航司航班延误。
Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
- 结合深度学习与经典算法构建混合预测模型。
- 在真实航班数据上达到92.3%准确率，F1-score达0.89。
- 适合航空调度优化与机场运营决策参考。
自1978年美国航空业放松管制以来，航空交通持续增长，航班延误问题日益严重，对航空公司和乘客造成显著影响。延误导致燃料、人力和资本等有限资源消耗增加，未来预计将进一步加剧。为应对这一挑战，本文提出一种混合机器学习方法，融合深度学习与经典机器学习技术。在真实航班数据上应用多种算法进行验证，通过准确率、精确率、召回率、F1分数及ROC/AUC曲线评估模型性能。研究还对数据与各模型进行了深入分析，为美国航空公司提供具有洞察力的预测结果。
原文摘要 · Abstract (English)
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.
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