Improving Flight Delay Prediction Using Integrated Departure Disruption, Airline-Route Stability, and Schedule Intensity Features

In the aviation industry, flight delays represent a major challenge due to their economic impact, operational disruptions, and adverse effects on passenger satisfaction and transportation efficiency. This study proposes an integrated feature engineering framework for flight delay prediction that combines three groups of engineered features, namely Departure Disruption Features (DDFs), Airline-Route Stability Features (ARSFs), and Schedule Intensity Features (SIFs), with the original flight delay dataset. These feature groups are designed to capture complementary operational, historical, and scheduling characteristics that are not directly represented in the original data. Five machine learning classifiers were evaluated across five experimental scenarios. These included the original feature set, the original feature set combined separately with each engineered feature group (DDFs, ARSFs, and SIFs), and the original feature set combined with all three engineered feature groups. This design enabled assessment of the individual and complementary contributions of the proposed features to flight delay prediction. The results indicated that the engineered features improved prediction performance across the evaluated configurations. In particular, combining all three engineered feature groups with the original feature set outperformed the baseline feature set and each individually combined feature-set configuration, supporting the complementary predictive value of the proposed engineered features. The complete feature set with LightGBM and SMOTE-Tomek achieved the best performance, with an accuracy of 0.9820 and an AUC of 0.9884, compared with 0.9487 and 0.9663, respectively, for the baseline feature set.

@article{Aljawazneh2026,
  title     = {Improving Flight Delay Prediction Using Integrated Departure Disruption, Airline-Route Stability, and Schedule Intensity Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {8},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Huthaifa Aljawazneh},
  doi       = {10.14569/IJACSA.2026.0170882},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170882}
}