News Release

€2million research project to develop AI system to improve air travel efficiency

A new European research project aims to improve the efficiency and capacity of air travel by using artificial intelligence to redesign flexible airspace sectors

Grant and Award Announcement

Lancaster University

A new European research project aims to improve the efficiency and capacity of air travel by using artificial intelligence (AI) to redesign flexible airspace sectors.

Through machine learning, mathematical modelling and optimisation techniques, experts hope to reduce passenger delays, unlock shorter routes, lower emissions and alleviate the workloads of air traffic controllers by making the dynamic airspace configuration process automated and more flexible.

The €2million SMARTS project, funded by Horizon Europe, is led by Centro de Referencia de Investigación, Desarrollo e Innovación ATM, A.I.E. (CRIDA) with support from researchers from Lancaster University Management School, NATS, the German Aerospace Center, Eurocontrol, Enaire, and Ecole Nationale de l’Aviation Civile (ENAC).

Professor Guglielmo Lulli from Lancaster University Management School said: “There are around 8000 aircraft carrying 600,000 people across UK skies every day – but with no marked lanes above our heads, it’s hard to envisage how complicated the air travel system is, and how complex a challenge it can be for teams to manage on the ground.

“Airspace is divided into sections that are managed by individual air traffic controllers, responsible for their own particular regions. Dividing up these airspace sectors is quite a manual task, and options for how they are divided is limited by a small number of possible configurations. However, getting the sectors right is crucial as the decision impacts passenger delays, air traffic controllers’ workload, and emission savings.

“By redesigning how air space sectors are configured will not only ensure a system that is more flexible to unlock the right amount of capacity at the right moment, with maximum efficiency – but will also ensure air traffic controllers can handle the associated workload comfortably.

“While automation has been explored in this area before, this project is the first to attack the airspace design and configuration problem at its very core – by redesigning the elementary airspace components which make up our airspace sectors.”

Enabling additional airspace capacity is a key priority for the industry to address the significant capacity challenges air travel currently faces and to cope with International Air Transport Association’s projected 3% annual growth in air traffic, estimated to rise to almost 8 billion passenger journeys per year by 2040.

By first conducting thorough research to get a clear understanding of the air traffic system, the SMARTS project team will create accurate predictive models for air traffic demand and capacity provision. Then, using machine learning and designing bespoke algorithms, they will look to develop innovative sector configuration plans that are resilient and dynamic to design, flex and adapt when needed.

Eva Puntero, Research and Development Engineer at CRIDA and SMARTS project coordinator, said: “We aim to devise a solution that is robust and will explicitly consider demand uncertainty. By taking full advantage of airspace potential, our system will require less air traffic controller resources per flight, will increase the productivity of the system and ultimately produce more cost-effective capacity management processes.

“We hope the project will also have indirect benefits to airport users too in the form of more punctual flights, improved reliability of flight schedules and, if it helps reduce service provision costs for airlines, potentially a reduction in fares for passengers in future.”

The SMARTS team hopes to make the project findings available to many nations and hope to improve airspace right across Europe.

The project began at the end of 2023 and will run until 2026.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.