The project

 

​The ongoing ATM modernisation programmes, including SESAR, build on ICAO Global ATM Operational Concept, one of whose cornerstones is performance orientation. ICAO identifies four key elements of a performance-based approach:

• defining policy in terms of qualitative performance objectives;

• making the objectives measurable by defining appropriate indicators;

• developing the data and methodologies necessary to calculate the indicators; and

• having the expertise to maintain data quality and assessing the link between indicator trends and management actions.

 

Realising these elements is not an easy task: ATM performance results from the complex interaction of interdependent policies and regulations, stakeholders, technologies and market conditions, and trade-offs arise not only between KPAs, but also between stakeholders, as well as between short-term and long-term objectives. To effectively steer the performance of ATM operations, metrics and indicators shall therefore be capable of capturing the full range of economic, social and environmental impacts of the ATM system, both on the different stakeholders and society at large, at different temporal and geographical scales. Performance modelling techniques shall be able to grasp the interdependencies between different KPAs and KPIs and allow the assessment of the possible future impacts of a range of policies and trends.
 

The need for improved indicators and modelling methodologies meeting these conditions has been acknowledged by the ATM stakeholders and the research community. 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 indicators at different scales, and develop new decision support tools for ATM performance monitoring and management. The specific objectives of the project are the following:

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 multidimensional performance assessment and decision making for both monitoring and management purposes.

 

To find more about the project, download the ​​​INTUIT Position Paper​.