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A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning

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  • Bojia Ye

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Bo Liu

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yong Tian

    (College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Lili Wan

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

This paper proposes a new methodology for predicting aggregate flight departure delays in airports by exploring supervised learning methods. Individual flight data and meteorological information were processed to obtain four types of airport-related aggregate characteristics for prediction modeling. The expected departure delays in airports is selected as the prediction target while four popular supervised learning methods: multiple linear regression, a support vector machine, extremely randomized trees and LightGBM are investigated to improve the predictability and accuracy of the model. The proposed model is trained and validated using operational data from March 2017 to February 2018 for the Nanjing Lukou International Airport in China. The results show that for a 1-h forecast horizon, the LightGBM model provides the best result, giving a 0.8655 accuracy rate with a 6.65 min mean absolute error, which is 1.83 min less than results from previous research. The importance of aggregate characteristics and example validation are also studied.

Suggested Citation

  • Bojia Ye & Bo Liu & Yong Tian & Lili Wan, 2020. "A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2749-:d:339629
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    References listed on IDEAS

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