INTUIT

Interactive Toolset for Understanding Trade-offs in ATM Performance

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 is 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 is 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.

The official project website: https://www.intuit-sesar.eu/

reports

No report available yet

period

2016-2018

funded by

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

partners

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

researchers

Thomas Blondiau, Eef Delhaye, Sven Maerivoet, Rodric Frederix

contact

Eef Delhaye

+32 16 74.51.22
reference: 15052

   
This project has received funding from the SESAR Joint Undertaking under grant agreement No 699303 under European Union’s Horizon 2020 research and innovation programme