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Interactive Toolset for Understanding Trade-offs in ATM Performance


Interactive Toolset for Understanding Trade-offs in ATM Performance

Call for proposal: 
Link to CORDIS:
Background & policy context: 

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 KPIs.


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, identify cause-effect relationships between KPIs at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are:

  1. to conduct a systematic characterisation of the ATM performance datasets available at different spatial and temporal scales and evaluate their potential to inform the development of new indicators and modelling approaches;
  2. to propose new metrics and indicators providing new angles of analysis of ATM performance;
  3. to develop a set of visual analytics and machine learning algorithms for the extraction of relevant and understandable patterns from ATM performance data;
  4. to investigate new data-driven modelling techniques and evaluate their potential to provide new insights about cause-effect relationships between performance drivers and performance indicators;
  5. to integrate the newly developed analytical and visualisation functionalities into an interactive dashboard supporting multi-dimensional performance assessment and decision making for both monitoring and management purposes.
Institution Type:
Institution Name: 
European Commission
Type of funding:
Lead Organisation: 

Nommon Solutions And Technologies Sl

Calle Cañas 8 5 4
28043 Madrid
EU Contribution: 
Partner Organisations: 

Transport & Mobility Leuven Nv

3010 Kessel Lo
EU Contribution: 

Fraunhofer Gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Carl-Zeiss-Str. 18-20
55129 Mainz
EU Contribution: 

Universidad Politécnica De Madrid

Avda. Ramiro de Maeztu, 3
28040 MADRID
EU Contribution: 

Advanced Logistics Group Sau

Calle Tanger 98 108 P 3 Pta A
8018 Barcelona
EU Contribution: 
Development phase: