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Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks

Author

Listed:
  • Mazhar Arıkan

    (University of Kansas, Lawrence, Kansas 66045)

  • Vinayak Deshpande

    (University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Milind Sohoni

    (Indian School of Business, Gachibowli, Hyderabad 500 032, India)

Abstract

Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines' on-time performance at its worst level in 2007 since 1995. A recent report by the Joint Economic Committee of the U.S. Congress chaired by Senator Charles E. Schumer has estimated that the total cost to the U.S. economy because of flight delays was as much as $41 billion in 2007. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the U.S. air-travel infrastructure (i.e., airports that cause most delay propagation)? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight (and, hence, deserves managerial attention)? Flight delays are typically attributed to two factors: (i) the randomness in the intrinsic travel time for a scheduled flight (which is the travel time excluding propagated delays), and (ii) the propagation of this randomness through the air-travel network and infrastructure. We model both of these factors that cause travel delays. The contribution of this paper is twofold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Our stochastic models allow us to develop three important robustness measures for airline networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure, which, if addressed, could lead to a significant improvement in air-travel reliability.

Suggested Citation

  • Mazhar Arıkan & Vinayak Deshpande & Milind Sohoni, 2013. "Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks," Operations Research, INFORMS, vol. 61(1), pages 45-64, February.
  • Handle: RePEc:inm:oropre:v:61:y:2013:i:1:p:45-64
    DOI: 10.1287/opre.1120.1146
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    References listed on IDEAS

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    4. Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2022. "Airline delay propagation: A simple method for measuring its extent and determinants," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 55-71.
    5. Hutter, Leonie & Jaehn, Florian & Neumann, Simone, 2019. "Influencing factors on airplane boarding times," Omega, Elsevier, vol. 87(C), pages 177-190.
    6. Jane Lee & Lavanya Marla & Alexandre Jacquillat, 2020. "Dynamic Disruption Management in Airline Networks Under Airport Operating Uncertainty," Transportation Science, INFORMS, vol. 54(4), pages 973-997, July.
    7. Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2021. "Airline schedule buffers and flight delays: A discrete model," Economics of Transportation, Elsevier, vol. 26.
    8. Brueckner, Jan K. & Czerny, Achim I. & Gaggero, Alberto A., 2021. "Airline mitigation of propagated delays via schedule buffers: Theory and empirics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    9. Martina Zámková & Stanislav Rojík & Martin Prokop & Radek Stolín, 2022. "Factors Affecting the International Flight Delays and Their Impact on Airline Operation and Management and Passenger Compensations Fees in Air Transport Industry: Case Study of a Selected Airlines in ," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    10. Kamalini Ramdas & Jonathan Williams & Marc Lipson, 2013. "Can Financial Markets Inform Operational Improvement Efforts? Evidence from the Airline Industry," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 405-422, July.
    11. Tan, Xinlong & Jia, Rongwen & Yan, Jia & Wang, Kun & Bian, Lei, 2021. "An Exploratory analysis of flight delay propagation in China," Journal of Air Transport Management, Elsevier, vol. 92(C).
    12. Sujeevraja Sanjeevi & Saravanan Venkatachalam, 2021. "Robust flight schedules with stochastic programming," Annals of Operations Research, Springer, vol. 305(1), pages 403-421, October.
    13. 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.
    14. Mariana Nicolae & Mazhar Arıkan & Vinayak Deshpande & Mark Ferguson, 2017. "Do Bags Fly Free? An Empirical Analysis of the Operational Implications of Airline Baggage Fees," Management Science, INFORMS, vol. 63(10), pages 3187-3206, October.
    15. Lang, Hao & Czerny, Achim I., 2022. "Airport pricing versus (grandfathered) slots: A generalization," Economics of Transportation, Elsevier, vol. 29(C).
    16. Wang, Yanjun & Li, Max Z. & Gopalakrishnan, Karthik & Liu, Tongdan, 2022. "Timescales of delay propagation in airport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    17. Liang, Zhenglin & Liu, Bin & Xie, Min & Parlikad, Ajith Kumar, 2020. "Condition-based maintenance for long-life assets with exposure to operational and environmental risks," International Journal of Production Economics, Elsevier, vol. 221(C).

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