Big data analytics used to reduce airport delays
Posted: 4 October 2016 | International Airport Review | 1 comment
A new system to predict whether passengers will catch their connecting flights has been developed to reduce delays at the world’s most congested airports, by the UCL School of Management…
A new system to predict whether passengers will catch their connecting flights has been developed to reduce delays at the world’s most congested airports, by the UCL School of Management.
Professor Bert De Reyck, Director of the UCL School of Management, is leading a team to accurately predict hours in advance whether passengers will catch their connections to avoid flight delays and better manage queues at security and border control.
By providing access to real-time big data, the study focuses on passenger movement using advanced data analytics and machine learning technology.
A prototype of the system went live at Heathrow on 19 July. Its performance is currently being assessed.
“Heathrow is the most congested airport on the planet,” De Reyck says, “handling 75 million passengers per year with only two runways. Any interruption causes further delays not only throughout the day, but throughout the entire European network as the airport is a major hub for connecting flights.
“With more than a quarter of all passengers landing in Heathrow making a flight transfer, any passengers missing connections can have a major impact on passenger satisfaction and airline delays. That’s why all processes need to be optimised.”
This study is part of a collaborative project to completely overhaul European airspace and its air traffic management. It’s managed by Eurocontrol, the European Organisation for the Safety of Air Navigation.