INTUIT
15052
From 2016 to 2018
TML explored the potential of visual analysis techniques and system modelling methods to better understand the trade-offs between key performance areas (KPAs) in air traffic management. TML supported qualitative analyses, assessed data science techniques, and developed a system dynamics simulation model for structuring performance analyses.
Performance of air traffic management (ATM) service providers is usually assessed on a number of key performance areas (KPAs): safety, capacity, cost-efficiency, and environment. However, this performance is the result of complex interactions between policies and regulations, stakeholders, technologies, and market conditions. Therefore, trade-offs arise between different KPAs, but also between stakeholders, as well as between short-term and long-term goals. While much attention is paid to the development of microscopic performance models, there is a lack of useful macro approaches that can translate local improvements or specific legislations into their impact at a higher, systemic level.
The aim of INTUIT was to explore the potential of visual analysis techniques, machine learning, and systems modelling techniques to better understand trade-offs between ATM KPAs. We identified cause-and-effect relationships between performance at different levels and developed new decision tools to support ATM performance monitoring and management. These tools will be deployed to support the work of Eurocontrol's performance review unit (PRU).
TML's role in the INTUIT project consisted of the following tasks:
- Provide support in the qualitative analysis of performance drivers.
- Provide support in assessing the suitability of data science techniques.
- Develop a system dynamics simulation model to analyse trade-offs between performance areas in a structural manner.
