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This study presents a comprehensive framework for predicting departure delays in U.S. domestic aviation by integrating advanced feature engineering, network analysis, and ensemble learning methods. Using a dataset of 2,638,673 flights across 354 airports from May to August 2024, we engineered predictors using temporal features (cyclical time), operational metrics (airport congestion), and network characteristics (in-/out-degree centrality and cluster labels). We extracted data for the five airlines with the highest number of flights: Southwest (WN), American (AA), Delta (DL), United (UA), and SkyWest (OO). A novel greedy mutual information and correlation-based feature selection method was then applied to each dataset to improve prediction performance. Multiple classifiers, including Random Forest (RF), Extra Trees (ET), XGBoost, and LightGBM, were evaluated. RF and ET consistently outperformed the others, motivating their inclusion in a Voting ensemble. The Voting classifier achieved robust performance across all five airlines, with overall accuracy ranging from 88.9% to 91.8%, F1–scores between 88.5% and 91.4%, and AUC–ROC values all above 95%. DL yielded the highest performance (91.8% accuracy and 96.8% AUC–ROC). These results demonstrate that combining network–cluster information with rich historical features substantially improves delay prediction, providing a scalable approach for airlines and air traffic managers to mitigate operational disruptions.

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Enhancing Flight Delay Prediction with Network-Aware Ensemble Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Mary Dufie Afrane,
  • Yao Xu,
  • Lixin Li

摘要

This study presents a comprehensive framework for predicting departure delays in U.S. domestic aviation by integrating advanced feature engineering, network analysis, and ensemble learning methods. Using a dataset of 2,638,673 flights across 354 airports from May to August 2024, we engineered predictors using temporal features (cyclical time), operational metrics (airport congestion), and network characteristics (in-/out-degree centrality and cluster labels). We extracted data for the five airlines with the highest number of flights: Southwest (WN), American (AA), Delta (DL), United (UA), and SkyWest (OO). A novel greedy mutual information and correlation-based feature selection method was then applied to each dataset to improve prediction performance. Multiple classifiers, including Random Forest (RF), Extra Trees (ET), XGBoost, and LightGBM, were evaluated. RF and ET consistently outperformed the others, motivating their inclusion in a Voting ensemble. The Voting classifier achieved robust performance across all five airlines, with overall accuracy ranging from 88.9% to 91.8%, F1–scores between 88.5% and 91.4%, and AUC–ROC values all above 95%. DL yielded the highest performance (91.8% accuracy and 96.8% AUC–ROC). These results demonstrate that combining network–cluster information with rich historical features substantially improves delay prediction, providing a scalable approach for airlines and air traffic managers to mitigate operational disruptions.