IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v50y2020i5p325-339.html
   My bibliography  Save this article

London Heathrow Airport Uses Real-Time Analytics for Improving Operations

Author

Listed:
  • Xiaojia Guo

    (UCL School of Management, University College London, London WC1E 6BT, United Kingdom)

  • Yael Grushka-Cockayne

    (Harvard Business School, Cambridge, Massachusetts 02163; Darden School of Business, University of Virginia, Charlottesville, Virginia 22903)

  • Bert De Reyck

    (UCL School of Management, University College London, London WC1E 6BT, United Kingdom)

Abstract

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. In this paper, we describe the process of how we chose the subject of the pilot, namely the improvement of transfer-passenger flows through the airport, and how we helped Heathrow move from its existing legacy system for managing passenger flows to an advanced machine learning–based approach using real-time inputs. The system, which is now in operation at Heathrow, can predict which passengers are likely to miss their connecting flights, reducing the likelihood that departures will incur delays while waiting for delayed passengers. This can be done by off-loading passengers in advance, by expediting passengers through the airport, or by modifying the departure times of aircraft in advance. By aggregating estimated passenger arrival time at various points throughout the airport, the system also improves passenger experiences at the immigration and security desks by enabling modifications to staffing levels in advance of expected surges in arrivals. The nine-stage framework we present here can support the development and implementation of other real-time, data-driven systems. To the best of our knowledge, the proposed system is the first to use machine learning to model passenger flows in an airport.

Suggested Citation

  • Xiaojia Guo & Yael Grushka-Cockayne & Bert De Reyck, 2020. "London Heathrow Airport Uses Real-Time Analytics for Improving Operations," Interfaces, INFORMS, vol. 50(5), pages 325-339, September.
    • Handle: RePEc:inm:orinte:v:50:y:2020:i:5:p:325-339
    DOI: 10.1287/inte.2020.1044
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2020.1044
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2020.1044?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. Victor Richmond R. Jose & Robert L. Winkler, 2009. "Evaluating Quantile Assessments," Operations Research, INFORMS, vol. 57(5), pages 1287-1297, October.
    2. Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose & Robert L. Winkler, 2017. "Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs," Operations Research, INFORMS, vol. 65(3), pages 712-728, June.
    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. Jiang, Yirui & Tran, Trung Hieu & Williams, Leon, 2023. "Machine learning and mixed reality for smart aviation: Applications and challenges," Journal of Air Transport Management, Elsevier, vol. 111(C).
    2. Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.

    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. Mohammed Abdellaoui & Han Bleichrodt & Emmanuel Kemel & Olivier l’Haridon, 2021. "Measuring Beliefs Under Ambiguity," Operations Research, INFORMS, vol. 69(2), pages 599-612, March.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    4. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    5. Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "Evaluating quantile forecasts in the M5 uncertainty competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1531-1545.
    6. Thomas W. Keelin & Bradford W. Powley, 2011. "Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 8(3), pages 206-219, September.
    7. repec:cup:judgdm:v:14:y:2019:i:4:p:395-411 is not listed on IDEAS
    8. Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico, 2021. "Forecasting macroeconomic risks," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1173-1191.
    9. Yael Grushka-Cockayne & Victor Richmond R. Jose & Kenneth C. Lichtendahl Jr., 2017. "Ensembles of Overfit and Overconfident Forecasts," Management Science, INFORMS, vol. 63(4), pages 1110-1130, April.
    10. Sonsino, Doron & Regev, Eran, 2013. "Informational overconfidence in return prediction – More properties," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 72-84.
    11. Steffen Keck & Wenjie Tang, 2018. "Gender Composition and Group Confidence Judgment: The Perils of All-Male Groups," Management Science, INFORMS, vol. 64(12), pages 5877-5898, December.
    12. Julia P. Prims & Don A. Moore, 2017. "Overconfidence over the lifespan," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(1), pages 29-41, January.
    13. Sulian Wang & Chen Wang, 2021. "Quantile Judgments of Lognormal Losses: An Experimental Investigation," Decision Analysis, INFORMS, vol. 18(1), pages 78-99, March.
    14. Victor Richmond R. Jose, 2017. "Percentage and Relative Error Measures in Forecast Evaluation," Operations Research, INFORMS, vol. 65(1), pages 200-211, February.
    15. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    16. Karl Schlag & James Tremewan & Joël Weele, 2015. "A penny for your thoughts: a survey of methods for eliciting beliefs," Experimental Economics, Springer;Economic Science Association, vol. 18(3), pages 457-490, September.
    17. Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose & Robert L. Winkler, 2017. "Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs," Operations Research, INFORMS, vol. 65(3), pages 712-728, June.
    18. Justin W. Eggstaff & Thomas A. Mazzuchi & Shahram Sarkani, 2014. "The Development of Progress Plans Using a Performance‐Based Expert Judgment Model to Assess Technical Performance and Risk," Systems Engineering, John Wiley & Sons, vol. 17(4), pages 375-391, December.
    19. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Phillip E. Pfeifer, 2013. "The Wisdom of Competitive Crowds," Operations Research, INFORMS, vol. 61(6), pages 1383-1398, December.
    20. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    21. Saemi Park & David V. Budescu, 2015. "Aggregating multiple probability intervals to improve calibration," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(2), pages 130-143, March.

    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:inm:orinte:v:50:y:2020:i:5:p:325-339. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.