As concrete outcomes, the project delivers machine learning–based algorithms, related research findings, and scientific publications. In parallel, TADA builds practical, experience-based understanding of how responsibilities can be shared between humans and artificial intelligence in air traffic control. The results provide a foundation for further research and enable the future development of decision-support systems.

The project’s objective is to investigate how a human-centred digital assistant could genuinely enhance safety, traffic flow, and airspace capacity management—without seeking to automate or replace the role of the air traffic controller.

Monad’s role

Monad participates in the TADA project as an expert in software development and applied artificial intelligence. Monad’s work focuses on AI research and development, the implementation of a validation user interface, and the development of a simulation environment in which the assistant’s behaviour can be evaluated and tested in a research setting.

Within the project, machine learning models have been trained using extensive historical data on aircraft trajectories and air traffic controllers’ decision-making across different traffic scenarios. The goal is not to automate air traffic control, but to generate decision-support suggestions based on previously observed situations and identified operational patterns. AI acts as a supporting tool—not as an autonomous decision-maker.

Monad has developed a simulation environment that enables realistic modelling of air traffic control scenarios and safe evaluation of the digital assistant during the research phase. The simulations are also used to assess how suggestions are presented to controllers in a clear, practical, and operationally meaningful way through the human–machine interface (HMI).

The simulation environment has been tested in Italy with local air traffic controllers under conditions that closely reflect real operational environments. Feedback from these tests has been used to evaluate and further develop both the AI assistant and the associated user interface solutions.

Concrete development areas in the research phase

Within TADA, solutions are developed and tested in a research environment. From Monad’s perspective, the key focus areas include:

• Use of historical data for training machine learning models and identifying operational patterns in traffic situations.
• Simulation and validation environment for safely modelling air traffic control scenarios and evaluating the assistant’s behaviour prior to any potential operational use.
• Human-centred interface design (HMI), focusing on how decision-support suggestions are presented so that they genuinely assist controllers in real-world decision-making.

TADA is a research and development project, not a finished product. The project explores and validates the potential, limitations, and prerequisites of AI-assisted decision support for the future of safe and efficient aviation.

Towards better decision support

The expected impact of TADA lies in providing air traffic controllers with improved decision support in congested TMA environments. The aim is smoother traffic flows, better predictability, and enhanced safety. More efficient use of airspace also contributes to reduced delays and lower emissions.

International consortium

The project is coordinated by Ingenav (Spain). In addition to Monad, the consortium includes Deep Blue and ENAV from Italy, Frequentis Orthogon from Germany, and the University of Malta.

The international consortium brings together academic research, software development, and hands-on operational expertise. Its goal is to assess the potential of artificial intelligence in a realistic and demanding air traffic control environment.