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Modelling airline flight cancellation decisions

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  • Xiong, Jing
  • Hansen, Mark

Abstract

In order to predict airline responses to Traffic Management Initiatives (TMIs), and reveal the underlying preference structures that shape these responses, we study US domestic airlines’ cancellation decisions in response to the Federal Aviation Administration (FAA)’s TMIs, in particular, to Ground Delay Programs (GDPs). By observing the actual flight-cancellation choices made by airline dispatchers, the airlines’ cancellation utility functions can be inferred through the use of binary choice models. The model captures how delays to a given flight and potential delay savings to other flights affect flight cancellation decisions. We also find larger, fuller, less frequent, shorter-distance, and spoke-bound flights are less likely to be cancelled, and that there is inter-airline variation in flight cancellation behaviour.

Suggested Citation

  • Xiong, Jing & Hansen, Mark, 2013. "Modelling airline flight cancellation decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 64-80.
  • Handle: RePEc:eee:transe:v:56:y:2013:i:c:p:64-80
    DOI: 10.1016/j.tre.2013.05.003
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    References listed on IDEAS

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    1. Clarke, Michael Dudley Delano, 1998. "Irregular airline operations: a review of the state-of-the-practice in airline operations control centers," Journal of Air Transport Management, Elsevier, vol. 4(2), pages 67-76.
    2. Hansen, Mark M. & Gillen, David & Djafarian-Tehrani, Reza, 2001. "Aviation infrastructure performance and airline cost: a statistical cost estimation approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(1), pages 1-23, March.
    3. Nicholas G. Rupp & George M. Holmes, 2006. "An Investigation into the Determinants of Flight Cancellations," Economica, London School of Economics and Political Science, vol. 73(292), pages 749-783, November.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
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    Cited by:

    1. Ding, Yida & Wandelt, Sebastian & Wu, Guohua & Xu, Yifan & Sun, Xiaoqian, 2023. "Towards efficient airline disruption recovery with reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    2. Kim, Myeonghyeon & Park, Sunwook, 2021. "Airport and route classification by modelling flight delay propagation," Journal of Air Transport Management, Elsevier, vol. 93(C).
    3. Lynnette Dray & Isabelle Laplace & Aude Marzuoli & Eric Féron & Antony Evans, 2017. "Using ground transportation for aviation system disruption alleviation," Post-Print hal-01599692, HAL.
    4. Kim, Myeonghyeon & Bae, Jiheon, 2021. "Modeling the flight departure delay using survival analysis in South Korea," Journal of Air Transport Management, Elsevier, vol. 91(C).
    5. Kenan, Nabil & Jebali, Aida & Diabat, Ali, 2018. "The integrated aircraft routing problem with optional flights and delay considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 355-375.
    6. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    7. Sternberg, Alice & Carvalho, Diego & Murta, Leonardo & Soares, Jorge & Ogasawara, Eduardo, 2016. "An analysis of Brazilian flight delays based on frequent patterns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 282-298.
    8. Alderighi, Marco & Gaggero, Alberto A., 2018. "Flight cancellations and airline alliances: Empirical evidence from Europe," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 90-101.
    9. 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.
    10. Skorupski, Jacek & Wierzbińska, Magdalena, 2015. "A method to evaluate the time of waiting for a late passenger," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 79-89.
    11. Khan, Waqar Ahmed & Chung, Sai-Ho & Eltoukhy, Abdelrahman E.E. & Khurshid, Faisal, 2024. "A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis," Journal of Air Transport Management, Elsevier, vol. 114(C).
    12. Miranda, Victor A.P. & Oliveira, Alessandro V.M., 2018. "Airport slots and the internalization of congestion by airlines: An empirical model of integrated flight disruption management in Brazil," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 201-219.
    13. Naz Yeti̇moğlu, Yücel & Selim Aktürk, M., 2021. "Aircraft and passenger recovery during an aircraft’s unexpected unavailability," Journal of Air Transport Management, Elsevier, vol. 91(C).
    14. Lynnette Dray & Isabelle Laplace & Aude Marzuoli & Eric Féron & Antony Evans, 2017. "Using ground transportation for aviation system disruption alleviation," Post-Print hal-01609773, HAL.
    15. Pouget, Lilian & Ribeiro, Nuno Antunes & Odoni, Amedeo R. & Antunes, António Pais, 2023. "How do airlines react to slot displacements? Evidence from a major airport," Journal of Air Transport Management, Elsevier, vol. 106(C).

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