IDEAS home Printed from https://ideas.repec.org/a/ags/ndjtrf/207326.html
   My bibliography  Save this article

Predicting Block Time: An Application of Quantile Regression

Author

Listed:
  • Diana, Tony

Abstract

Airlines face three types of delay that make it difficult to build robust schedules and to support block time predictability. Block time is the time elapsed from gate departure to gate arrival and refers to the time when blocks are off the wheels at the departure airport to the time they are back on at the destination airport. These delays can be induced (i.e., ground delays), propagated, or stochastic. With capacity constrained at major airports and regulators facing greater public pressure to alleviate congestion and tarmac delays, aviation practitioners have renewed their interest in the predictability of block time. This study presents a methodology based on the case study of the Seattle/Tacoma International (SEA) and Oakland International airport (OAK) city pair to determine the predictability of block time. The methodology based on quantile regression models is appropriate for a skewed distribution where analysts are interested in the impact of selected operational variables on the conditional mean of block times at given percentiles. Quantile regression provides a measure of on-time performance based on the percentile results that show the most significance and best fit.

Suggested Citation

  • Diana, Tony, 2012. "Predicting Block Time: An Application of Quantile Regression," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 51(3).
    • Handle: RePEc:ags:ndjtrf:207326
    DOI: 10.22004/ag.econ.207326
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/207326/files/2012v51n3_03_QuantileRegression.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.207326?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shan Lan & John-Paul Clarke & Cynthia Barnhart, 2006. "Planning for Robust Airline Operations: Optimizing Aircraft Routings and Flight Departure Times to Minimize Passenger Disruptions," Transportation Science, INFORMS, vol. 40(1), pages 15-28, February.
    2. Manoj Lohatepanont & Cynthia Barnhart, 2004. "Airline Schedule Planning: Integrated Models and Algorithms for Schedule Design and Fleet Assignment," Transportation Science, INFORMS, vol. 38(1), pages 19-32, February.
    3. Jeph Abara, 1989. "Applying Integer Linear Programming to the Fleet Assignment Problem," Interfaces, INFORMS, vol. 19(4), pages 20-28, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gabueva, Larisa (Габуева, Лариса) & Pavlova, Natalia (Павлова, Наталья), 2016. "Grounds for Development Prospects of Onerous Health Services Cluster [Обоснование Перспектив Развития Кластера Возмездных Услуг Здравоохранения]," Working Papers 1444, Russian Presidential Academy of National Economy and Public Administration.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kenan, Nabil & Diabat, Ali & Jebali, Aida, 2018. "Codeshare agreements in the integrated aircraft routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 272-295.
    2. Hanif Sherali & Ki-Hwan Bae & Mohamed Haouari, 2013. "A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture," Annals of Operations Research, Springer, vol. 210(1), pages 213-244, November.
    3. Chunhua Gao & Ellis Johnson & Barry Smith, 2009. "Integrated Airline Fleet and Crew Robust Planning," Transportation Science, INFORMS, vol. 43(1), pages 2-16, February.
    4. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    5. João P. Pita & Cynthia Barnhart & António P. Antunes, 2013. "Integrated Flight Scheduling and Fleet Assignment Under Airport Congestion," Transportation Science, INFORMS, vol. 47(4), pages 477-492, November.
    6. Oliver Faust & Jochen Gönsch & Robert Klein, 2017. "Demand-Oriented Integrated Scheduling for Point-to-Point Airlines," Transportation Science, INFORMS, vol. 51(1), pages 196-213, February.
    7. Hanif D. Sherali & Ki-Hwan Bae & Mohamed Haouari, 2013. "An Integrated Approach for Airline Flight Selection and Timing, Fleet Assignment, and Aircraft Routing," Transportation Science, INFORMS, vol. 47(4), pages 455-476, November.
    8. Valentina Cacchiani & Juan-José Salazar-González, 2017. "Optimal Solutions to a Real-World Integrated Airline Scheduling Problem," Transportation Science, INFORMS, vol. 51(1), pages 250-268, February.
    9. Sherali, Hanif D. & Bish, Ebru K. & Zhu, Xiaomei, 2006. "Airline fleet assignment concepts, models, and algorithms," European Journal of Operational Research, Elsevier, vol. 172(1), pages 1-30, July.
    10. Xu, Yifan & Wandelt, Sebastian & Sun, Xiaoqian, 2021. "Airline integrated robust scheduling with a variable neighborhood search based heuristic," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 181-203.
    11. Keji Wei & Vikrant Vaze, 2020. "Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice," Transportation Science, INFORMS, vol. 54(1), pages 139-163, January.
    12. Hanif D. Sherali & Ki-Hwan Bae & Mohamed Haouari, 2010. "Integrated Airline Schedule Design and Fleet Assignment: Polyhedral Analysis and Benders' Decomposition Approach," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 500-513, November.
    13. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    14. Muharrem Enis Ciftci & Vildan Özkır, 2024. "Integrated optimisation model for airline bank structure and fleet assignment problem," Annals of Operations Research, Springer, vol. 342(1), pages 265-285, November.
    15. Glomb, Lukas & Liers, Frauke & Rösel, Florian, 2023. "Optimizing integrated aircraft assignment and turnaround handling," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1051-1071.
    16. Yan, Shangyao & Tang, Ching-Hui & Fu, Tseng-Chih, 2008. "An airline scheduling model and solution algorithms under stochastic demands," European Journal of Operational Research, Elsevier, vol. 190(1), pages 22-39, October.
    17. Okan Örsan Özener & Melda Örmeci Matoğlu & Güneş Erdoğan & Mohamed Haouari & Hasan Sözer, 2017. "Solving a large-scale integrated fleet assignment and crew pairing problem," Annals of Operations Research, Springer, vol. 253(1), pages 477-500, June.
    18. Pita, João Pedro & Antunes, António Pais & Barnhart, Cynthia & de Menezes, António Gomes, 2013. "Setting public service obligations in low-demand air transportation networks: Application to the Azores," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 35-48.
    19. Stefano Coniglio & Mathias Sirvent & Martin Weibelzahl, 2021. "Airport capacity extension, fleet investment, and optimal aircraft scheduling in a multilevel market model: quantifying the costs of imperfect markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(2), pages 367-408, June.
    20. Abdelghany, Ahmed & Abdelghany, Khaled & Azadian, Farshid, 2023. "The airline seat capacity allocation problem: An expected marginal profit approach," Journal of Air Transport Management, Elsevier, vol. 112(C).

    More about this item

    Keywords

    Public Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:ndjtrf:207326. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: http://www.trforum.org/journal/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.