London Heathrow Airport Uses Real-Time Analytics for Improving Operations

30 Pages Posted: 30 Jun 2020

See all articles by Xiaojia Guo

Xiaojia Guo

University of Maryland - Robert H. Smith School of Business

Yael Grushka-Cockayne

University of Virginia - Darden School of Business

Bert De Reyck

UCL School of Management

Date Written: June 5, 2020

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 co-location 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 effciency of Heathrow’s operations. In Guo et al. (2019), we describe the system that we developed for Heathrow to better estimate passenger connection times, which takes advantage of the integration and data sharing that Heathrow’s APOC offers. 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 offloading 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 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

Guo, Xiaojia and Grushka-Cockayne, Yael and De Reyck, Bert, London Heathrow Airport Uses Real-Time Analytics for Improving Operations (June 5, 2020). Available at SSRN: https://ssrn.com/abstract=3619914 or http://dx.doi.org/10.2139/ssrn.3619914

Xiaojia Guo

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States

Yael Grushka-Cockayne (Contact Author)

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Bert De Reyck

UCL School of Management ( email )

London, WC1E 6BT
United Kingdom

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