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Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport

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  • Diana, Tony

Abstract

This study compares the performance of ensemble machine learning, ordinary least-squared and penalized algorithms to predict taxi-out time at two different periods of NextGen capability implementation. In the pre-sample, ordinary least-squared and ridge models performed better than other ensemble learning models. However, the gradient boosting model provided the lowest root mean squared errors in the post-sample. No algorithm fits data better in all cases. This paper recommends selecting the model that provides the best balance between bias and variance.

Suggested Citation

  • Diana, Tony, 2018. "Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 149-164.
    • Handle: RePEc:eee:transe:v:119:y:2018:i:c:p:149-164
    DOI: 10.1016/j.tre.2018.10.003
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    References listed on IDEAS

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    1. Lordan, Oriol & Sallan, Jose M. & Valenzuela-Arroyo, Marta, 2016. "Forecasting of taxi times: The case of Barcelona-El Prat airport," Journal of Air Transport Management, Elsevier, vol. 56(PB), pages 118-122.
    2. Diana, Tony, 2013. "An application of survival and frailty analysis to the study of taxi-out time: A case of New York Kennedy Airport," Journal of Air Transport Management, Elsevier, vol. 26(C), pages 40-43.
    3. Kim, Myung Suk, 2016. "Analysis of short-term forecasting for flight arrival time," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 35-41.
    4. Vinayak Deshpande & Mazhar Arıkan, 2012. "The Impact of Airline Flight Schedules on Flight Delays," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 423-440, July.
    5. Tu, Yufeng & Ball, Michael O. & Jank, Wolfgang S., 2008. "Estimating Flight Departure Delay DistributionsA Statistical Approach With Long-Term Trend and Short-Term Pattern," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 112-125, March.
    6. Christopher Mayer & Todd Sinai, 2003. "Network Effects, Congestion Externalities, and Air Traffic Delays: Or Why Not All Delays Are Evil," American Economic Review, American Economic Association, vol. 93(4), pages 1194-1215, September.
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    Cited by:

    1. Yu, Bin & Guo, Zhen & Asian, Sobhan & Wang, Huaizhu & Chen, Gang, 2019. "Flight delay prediction for commercial air transport: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 203-221.
    2. Wang, Chunzheng & Hu, Minghua & Yang, Lei & Zhao, Zheng, 2022. "Improving the spatial-temporal generalization of flight block time prediction: A development of stacking models," Journal of Air Transport Management, Elsevier, vol. 103(C).
    3. Abdelghany, Ahmed & Guzhva, Vitaly S. & Abdelghany, Khaled, 2023. "The limitation of machine-learning based models in predicting airline flight block time," Journal of Air Transport Management, Elsevier, vol. 107(C).

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