The TADA project is pioneering the integration of Artificial Intelligence (AI) into Air Traffic Management to enhance predictability, efficiency, sustainability, safety, and capacity in Terminal Manoeuvring Areas (TMAs). These high-traffic zones, especially around major airports, face increasing challenges in traffic complexity. TADA will support Air Traffic Control Officers (ATCOs) to better handle incoming aircrafts at the airports.  

TADA makes use of Machine Learning and big data to optimise decision-making in air traffic control (ATC). By analysing ATCO interactions and patterns, the project aims to enhance automation, improve trajectory predictions, and provide innovations to decision support tools, such as Arrival Management Systems (AMAN). Through these advancements, TADA is shaping the future of smarter, more efficient and adaptive air traffic management. 

Develop an AI-powered Digital Assistant based on historical ATC data to support decision-making to achieve the desired arrival sequences. Linked to this objective there is also the development of AI modules to support AMAN in proposing the best sequence. The tool aims to reduce ATCO workloads while increasing capacity, safety and sustainability of operations. 

Develop an innovative Human-Machine Interface (HMI) based on the EASA AI Framework to allow an enhanced Human-AI teaming between ATCOs and TADA’s digital tool. The TADA HMI study will also consider how the TADA Solution’s inputs are presented and apply Explainable AI (XAI).

Validate TADA and the associated HMI concepts by conducting early testing through Human-in-the-loop (HITL) validation with operational staff involved in TMA Air Traffic Control operations.  

Gain further understanding of the best Human-AI teaming in ATC between support for decision, action selection, and autonomous ATC under human monitoring (Automation Lv. 4 maximum) by performing a comparison study during the project and disseminate the results.