Air traffic controllers are tasked with ensuring that aircraft maintain safe distances, a complex duty that involves anticipating trajectory paths. This complexity often leads to conservative safety margins, which, while necessary, can adversely affect runway throughput and overall airport efficiency.

By utilising system-wide information management technical infrastructure (SWIM TI) purple profile, an air-ground data-sharing technology that connects aircraft with ground operations, this solution aims to enhance the prediction of aircraft trajectories. It utilises artificial intelligence and machine learning algorithms to accurately forecast aircraft trajectories. The development of this solution follows the EASA guidance for the development and deployment of AI-based systems, along with the standardisation being developed by Eurocae WG114.

The solution consists of several predictive models, including one that forecasts the landing and runway vacating times for arriving aircraft, and another that predicts the four-dimensional path of departing aircraft. These predictions are shared via the SWIM network, enabling the development of applications that assist air traffic controllers in optimising the spacing between aircraft. This optimisation not only adheres to required safety distances but also enhances runway usage efficiency. 

By making flight operations more predictable, our system increases safety and reduces flight times, leading to significant gains in fuel efficiency.

 
BENEFITS

• Increased runway throughput 

• Increased safety 

• Increased predictability 

• Decrease in fuel burn