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Slot Trading Opportunities in Collaborative Ground Delay Programs

Author

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  • Thomas W. M. Vossen

    (Leeds School of Business, University of Colorado, Boulder, Colorado 80309-0419)

  • Michael O. Ball

    (Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742-1815)

Abstract

The Federal Aviation Administration (FAA) and the major airlines in the United States have embraced a new initiative to improve air traffic flow management. This initiative, called collaborative decision making (CDM), is based on the recognition that improved data exchange and communication between the FAA and the airlines will lead to better decision making. In particular, the CDM philosophy emphasizes that decisions with a potential economic impact on airlines should be decentralized and made in collaboration with the airlines whenever possible.The CDM paradigm has led to fundamental changes in the implementation of ground delay programs. A key component has been the introduction of the compression procedure, which allows for the exchange of arrival slots between airlines. In this paper, we consider opportunities for increased airline control by interpreting the compression procedure as a mediated slot trading mechanism. Based on this interpretation, we propose an extension that allows airlines to submit so-called at-least, at-most offers. We develop an efficient integer programming model to solve the mediator’s problem, and show that the resulting mechanism can substantially improve the ability of airlines to optimize their internal cost functions.

Suggested Citation

  • Thomas W. M. Vossen & Michael O. Ball, 2006. "Slot Trading Opportunities in Collaborative Ground Delay Programs," Transportation Science, INFORMS, vol. 40(1), pages 29-43, February.
    • Handle: RePEc:inm:ortrsc:v:40:y:2006:i:1:p:29-43
    DOI: 10.1287/trsc.1050.0121
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    References listed on IDEAS

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    Cited by:

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    3. Ricard Gil & Myongjin Kim & Giorgio Zanarone, 2022. "Relationships Under Stress: Relational Outsourcing in the U.S. Airline Industry After the 2008 Financial Crisis," Management Science, INFORMS, vol. 68(2), pages 1256-1277, February.
    4. Wei, P. & Cao, Y. & Sun, D., 2013. "Total unimodularity and decomposition method for large-scale air traffic cell transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 1-16.
    5. Xu, Yan & Dalmau, Ramon & Melgosa, Marc & Montlaur, Adeline & Prats, Xavier, 2020. "A framework for collaborative air traffic flow management minimizing costs for airspace users: Enabling trajectory options and flexible pre-tactical delay management," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 229-255.
    6. Alexandre Jacquillat & Vikrant Vaze, 2018. "Interairline Equity in Airport Scheduling Interventions," Transportation Science, INFORMS, vol. 52(4), pages 941-964, August.
    7. Kuo, April & Miller-Hooks, Elise, 2012. "Developing Responsive Rail Services through collaboration," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 424-439.
    8. Avijit Mukherjee & Mark Hansen, 2007. "A Dynamic Stochastic Model for the Single Airport Ground Holding Problem," Transportation Science, INFORMS, vol. 41(4), pages 444-456, November.
    9. Schummer, James & Abizada, Azar, 2017. "Incentives in landing slot problems," Journal of Economic Theory, Elsevier, vol. 170(C), pages 29-55.
    10. Michael O. Ball & Robert Hoffman & Avijit Mukherjee, 2010. "Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle," Transportation Science, INFORMS, vol. 44(1), pages 1-14, February.
    11. Dixit, Aasheesh & Jakhar, Suresh Kumar, 2021. "Airport capacity management: A review and bibliometric analysis," Journal of Air Transport Management, Elsevier, vol. 91(C).
    12. Xiangning Dong & Xuhao Zhu & Minghua Hu & Jie Bao, 2023. "A Methodology for Predicting Ground Delay Program Incidence through Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    13. Mukherjee, Avijit & Hansen, Mark, 2009. "A dynamic rerouting model for air traffic flow management," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 159-171, January.
    14. Alexandre Jacquillat & Amedeo R. Odoni, 2015. "An Integrated Scheduling and Operations Approach to Airport Congestion Mitigation," Operations Research, INFORMS, vol. 63(6), pages 1390-1410, December.
    15. Hanif D. Sherali & Justin M. Hill & Michael V. McCrea & Antonio A. Trani, 2011. "Integrating Slot Exchange, Safety, Capacity, and Equity Mechanisms Within an Airspace Flow Program," Transportation Science, INFORMS, vol. 45(2), pages 271-284, May.
    16. Jamie Fairbrother & Konstantinos G. Zografos & Kevin D. Glazebrook, 2020. "A Slot-Scheduling Mechanism at Congested Airports that Incorporates Efficiency, Fairness, and Airline Preferences," Transportation Science, INFORMS, vol. 54(1), pages 115-138, January.

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