Darden Professor Employs Machine Learning to Improve Airport Passenger Flow, Costs & Satisfaction

Some 80 million passengers travel through London’s Heathrow Airport every year, and 30 million of them are there only to pass through to another city — often trapped in long lines at customs and immigration and scrambling to get to the next flight before it leaves.

Several years ago, Darden Professor Yael Grushka-Cockayne was hired to put her decision analysis expertise into practice at Heathrow, connecting data from siloed systems to help the airport better understand how many passengers were passing through at any given time, where they were going and how they were moving through the airport. By developing a machine learning algorithm based on the data, the airport could better determine staffing needs, reduce costs and increase passenger satisfaction.

“There are all kinds of industries in which you don’t want to overstaff because you will lose money, but you also don’t want to understaff because it will harm customer service. Anywhere that you need to move a lot of people — and increase your ability to predict their behavior — could benefit from this approach.”
– Professor Yael Grushka-Cockayne

Read the full article on Ideas to Action to learn how Yael’s research and successful data-first model has far reaching impact and caught the attention of other airport hubs.