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Discrete choice modelling in airline network management

Author

Listed:
  • Michael Scheidler

    (Faculty of Economics and Business Administration, University of Frankfurt, Germany)

  • Reinhard Hujer

    (Faculty of Economics and Business Administration, University of Frankfurt, Germany)

  • Joachim Grammig

    (Department of Statistics and Econometrics, University of Tübingen, Germany)

Abstract

The task of airline network management is to develop new flight schedule variants and evaluate them in terms of expected passenger demand and revenue. Given the industry's trend towards global cooperation, this is especially important when evaluating the potential synergies with alliance partners. From the econometric point of view, this task represents a discrete choice modelling problem in which one has to account for a large number of dependent alternatives. In this paper we discuss the applicability of recently proposed approaches and introduce a new multinomial probit specification designed for the airline network management task. The superior performance of the new model is demonstrated in a real-world application using airline bookings data. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Michael Scheidler & Reinhard Hujer & Joachim Grammig, 2005. "Discrete choice modelling in airline network management," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 467-486.
    • Handle: RePEc:jae:japmet:v:20:y:2005:i:4:p:467-486
    DOI: 10.1002/jae.799
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    References listed on IDEAS

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    Cited by:

    1. Wen, Chieh-Hua & Huang, Chia-Jung & Fu, Chiang, 2020. "Incorporating continuous representation of preferences for flight departure times into stated itinerary choice modeling," Transport Policy, Elsevier, vol. 98(C), pages 10-20.
    2. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
    3. Judit Guimera Busquets & Eduardo Alonso & Antony D. Evans, 2018. "Air itinerary shares estimation using multinomial logit models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(1), pages 3-16, January.
    4. Lesgourgues, Augustin & Malavolti, Estelle, 2023. "Social cost of airline delays: Assessment by the use of revenue management data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    5. Fukushi, Mitsuyoshi & Delgado, Felipe & Raveau, Sebastián, 2024. "Impact of omitted variable and simultaneous estimation endogeneity in choice-based revenue management systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    6. Koppelman, Frank S. & Coldren, Gregory M. & Parker, Roger A., 2008. "Schedule delay impacts on air-travel itinerary demand," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 263-273, March.
    7. Kölker, Katrin & Lütjens, Klaus & Gollnick, Volker, 2024. "Analyzing global passenger flows based on choice modeling in the air transportation system," Journal of Air Transport Management, Elsevier, vol. 115(C).
    8. Wen, Chieh-Hua & Chen, Po-Hung, 2017. "Passenger booking timing for low-cost airlines: A continuous logit approach," Journal of Air Transport Management, Elsevier, vol. 64(PA), pages 91-99.

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