Imagine a digital assistant for air traffic controllers that acts like a high-tech GPS for landing planes. It looks at past radar data and listens to pilot conversations to predict the perfect moment to turn a plane toward the runway. This helps planes land closer together safely, similar to how a smart traffic light keeps cars moving smoothly during rush hour.
Air traffic controllers struggle to maintain maximum runway capacity during high-density traffic due to the mental effort required for spacing and vectoring aircraft. This leads to increased workload and potential human error.
An AI-based decision support tool trained on radar and voice data to optimize aircraft interception of the final approach localizer.
If you are an airport operator dealing with congested runways during peak hours — this project developed an AI model that optimizes runway throughput. This allows more flights to land per hour by improving spacing between arrivals.
If you are an ANSP dealing with high air traffic controller workload and human error risks — this project developed a decision support tool that minimizes the number of vectoring instructions. This reduces the mental load on controllers during complex TMA operations.
If you are a software vendor dealing with the need for smarter automation in terminal areas — this project developed a machine learning model trained on surveillance and voice data. This can be integrated into existing ATC tools to provide optimized arrival control services.
Based on available project data, no specific pricing or cost information is provided as the project is EU-funded.
The project specifically assessed operations at Barcelona (LEBL) and Lisbon (LPPT) airports, suggesting it can be customized to specific approach procedures and airspace geometries.
Based on available project data, the IP details are not specified, but the project is coordinated by ISA Software France with a consortium of 4 partners.
The solution is designed to use existing radar surveillance data and voice communications, and is validated using the RAMS Plus simulation tool.
The project period runs from 2024-06-01 to 2026-11-30, indicating it is currently in the development and validation phase.
The consortium is highly industry-driven, with a 75% industry ratio consisting of 3 industrial partners and 1 other entity across 3 countries (FR, ES, PT). The presence of an SME as the coordinator (ISA Software France) suggests a focus on agile development and commercial viability rather than purely academic research.
Contact ISA Software France regarding the AI model for TMA automation.
Contact us to explore licensing opportunities for this AI-based ATC tool.
