Airlines turn to AI to allocate gates and cut waiting times

Given that level of complexity you would think that clever computer software would be handling the job, but think again.

The job of allocating gates has often been carried out using surprisingly basic tech, according to an AeroCloud survey of the challenges faced by senior airport executives.

“You’d be surprised at how many airports globally still manage the process manually,” says Mr Richardson.

Of those airport executives that responded to AeroCloud’s survey, 40% said that Excel and Word documents were used to store and manage information related to their airport operations, including gate management.

But serious investment is going into more advanced systems.

Last year, American Airlines introduced Smart Gating at Dallas Fort Worth International Airport.

The system uses machine learning to assign arriving aircraft to the nearest available gate with the shortest taxi time.

Machine learning is a branch of artificial intelligence, where large amounts of data are used to train a system that can be tweaked to improve its results.

In the case of the American Airlines system real-time flight information and other data is used to choose which gate to send an aircraft to.

“Traditionally, our team members manually assigned gates using a legacy computer system. At Dallas Fort Worth International Airport, our largest hub, this process took around four hours to complete,” says an American Airlines spokesperson.

The new system can complete that process in 10 minutes, which has shortened aircraft taxi times by 20%, saving around 1.4 million gallons of jet fuel each year, the spokesperson adds.