From 2016 to 2018
Performance of ATM (air traffic management) providers is typically evaluated on a few key performance areas (KPAs): safety, capacity, cost-efficiency and environment. ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions. Trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. While a lot of effort has traditionally been devoted to the development of microscopic performance models, there is a lack of useful macro approaches able to translate local improvements or specific regulations into their impact on high-level, system-wide performance.
The goal of INTUIT was to explore the potential of visual analytics, machine learning and systems modelling techniques to improve our understanding of the trade-offs between ATM KPAs. The aim was to identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. These tools will be deployed to support the work of Eurocontrol’s performance review unit.

The role of TML is in the INTUIT project is:
  •  to support the work in the qualitative analysis of performance drivers
  • to support the suitability assessment of data science techniques
  •  to develop a system dynamic simulation model to analyze trade-offs between performance areas in a structural way.


From 2016 to 2018


SESAR Joint Undertaking and the European Union as part of the H2020 research program


Nommon Solutions and Technologies, Advanced logistics group, Fraunhofer IAIS, Universidad politecnica de Madrid

Our team

Griet De Ceuster, Eef Delhaye, Rodric Frederix, Sven Maerivoet, Thomas Blondiau
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