More data, less stress: the future of air travel - Harvard Business School Digital Initiative

As Grushka-Cockayne explains, there was enthusiasm among key stakeholders at Heathrow to upgrade existing data systems at the airport, and a consensus about the opportunity to better leverage data to improve the experience of connecting passengers--who account for roughly one-third of all travelers who pass through Heathrow annually. The question was how to do this. "People want to use machine learning and big data--all of these buzz words," says Grushka-Cockayne, "but if they don't know how to focus in on a very specific task that can generate predictions, it is difficult to use the technology to actually improve decision-making." Grushka-Cockayne and her team spent several months working with partners at Heathrow to define the scope of their research--the development of a machine learning model that could predict a passenger's journey through Heathrow in route to his or her connecting flight. The goal was to be able to anticipate the number of people passing through immigration in real time (enabling more efficient staff allocation at immigration lines), and also to predict whether a passenger would be late for his or her flight (allowing the airport to proactively offer supporting services). But it wasn't easy to capture the complexity of a passenger's journey through an airport in a statistical model.