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Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model.

Pavel Stefanovič1, Rokas Štrimaitis1, Olga Kurasova2

  • 1Faculty of Fundamental Science, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania.

Computational Intelligence and Neuroscience
|November 12, 2020
PubMed
Summary
This summary is machine-generated.

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This study predicts flight time deviations at Lithuania airports using machine learning. Gradient boosted trees demonstrated the highest accuracy in predicting flight delays.

Area of Science:

  • Aviation management
  • Data science
  • Machine learning applications

Background:

  • Flight delays pose significant operational and economic challenges for airports.
  • Accurate prediction of flight time deviations is crucial for efficient air traffic management.
  • Lithuania airports' flight data has not been extensively analyzed for delay prediction.

Purpose of the Study:

  • To analyze flight time deviations at Lithuania airports.
  • To implement and compare supervised machine learning models for predicting flight delay intervals.
  • To identify the most accurate algorithm for flight delay prediction.

Main Methods:

  • Utilized seven supervised machine learning algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines.

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  • Employed grid search for hyperparameter optimization to maximize algorithm accuracy.
  • Evaluated algorithm performance using sensitivity/recall, precision, specificity, F-measure, and accuracy.
  • Applied the SMOTE (Synthetic Minority Over-sampling Technique) technique for dataset balancing.

    • Main Results:

    • Tree model classifiers, including gradient boosted trees, achieved the highest prediction accuracy.
    • Gradient boosted trees were identified as the most effective algorithm for predicting flight delay intervals.
    • Analysis considered both departure and arrival flights separately, incorporating weather data.

    Conclusions:

    • Supervised machine learning, particularly gradient boosted trees, offers a robust solution for predicting flight time deviations at Lithuania airports.
    • The findings provide valuable insights for improving air traffic predictability and operational efficiency.
    • Further research could explore additional features and advanced modeling techniques for enhanced delay prediction.