Air traffic management systems are undergoing a comprehensive modernisation process throughout the world. Initiatives like SESAR in Europe and NextGen in the United States are developing and deploying innovative concepts and transformative technologies. The goals of these modernisation programmes include increasing airport and airspace capacity, reducing delays, dealing with the safe integration of drones into airspace, improving safety, reducing the cost of air traffic management services, and minimising the environmental impact of air traffic operations. In this context, artificial intelligence is receiving increasing attention as a tool that can help achieve these goals.
Applying artificial intelligence to air traffic management
The increasing availability of air traffic data enabled by the implementation of technologies like ADS-B and the rapid advances in computing power have triggered a growing interest in the application of artificial intelligence to air traffic management. Artificial intelligence holds enormous potential to address a wide spectrum of air traffic management problems, including 4D trajectory prediction and optimisation, prediction of runway occupancy times, demand forecasting, optimisation of sector configuration, conflict detection and resolution, and air traffic management performance assessment, to mention some examples. Nommon combines big data technologies and machine learning techniques with air traffic simulation to assess new operational concepts and help air navigation service providers, airports and other stakeholders improve the performance of the air traffic management system.