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Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy.

Sarah Ahmed A AlBassam1, Dhafir N AlShahrani1

  • 1Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

Plos One
|December 8, 2025
PubMed
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This summary is machine-generated.

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Accurate flight delay prediction is crucial for airlines. Machine learning models, especially Random Forest and Decision Tree with resampling techniques, show high performance in predicting delays, even with imbalanced data.

Area of Science:

  • Aviation Operations Research
  • Machine Learning Applications
  • Data Science

Background:

  • Flight delays significantly impact airline efficiency, resource management, and passenger satisfaction.
  • Accurate prediction of flight arrival delays is essential for operational optimization and improved customer experience.
  • Class imbalance in flight delay datasets presents a significant challenge for predictive modeling.

Purpose of the Study:

  • To systematically evaluate the predictive performance of six machine learning classifiers for flight delay prediction.
  • To investigate the effectiveness of various resampling techniques in mitigating class imbalance.
  • To identify optimal model and resampling combinations for accurate flight delay forecasting.

Main Methods:

  • Six machine learning classifiers were evaluated: Decision Tree, Random Forest, Support Vector Classifier (SVC), Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes.

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  • Resampling techniques including Random Oversampling, Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) were applied to address class imbalance.
  • Stratified 10-fold cross-validation and a hold-out test set were used for rigorous performance assessment with metrics like Accuracy, F1-score, MCC, and ROC-AUC.

    • Main Results:

    • Random Forest with Random Oversampling and Decision Tree with SMOTE achieved the highest predictive performance (accuracy 0.90, F1-score 0.90, MCC 0.73, ROC-AUC 0.87).
    • Resampling strategies significantly improved model performance on the imbalanced flight delay dataset.
    • Simpler models like Naive Bayes showed competitive results when data was balanced, indicating their continued utility.

    Conclusions:

    • Resampling techniques are critical for developing reliable predictive models for imbalanced flight delay data.
    • Ensemble methods like Random Forest and tree-based methods like Decision Tree, when combined with appropriate resampling, offer superior predictive accuracy.
    • The study provides actionable insights for airlines to improve operational efficiency and decision-making through data-driven delay prediction.