Motivation and Goals

European airspace capacity is near reaching its limit, with all-causes ATFM (Air Traffic Flow Management) delays doubling from 2014 to 2018. While ATM services’ cost-efficiency improved since the adoption of the European performance schemes, the capacity of the system remains a challenge. It becomes critical for network management to address traffic demand and airspace capacity balancing in an efficient way, so as the associated measures actually contribute to minimize delays at network and local levels.

Network prediction and performance is very sensitive to weather and the uncertainty in its prediction. In addition, current ATFCM operations are not evaluated from a systematic perspective. These two factors together lead to a strong dependency on the experience of human operators. ISOBAR addresses these challenges through the contribution to an Artificial Intelligence (AI)-based Network Operations Plan, by including in its scope an enhanced weather prediction tailored to ATFCM, ATM and weather data integration, demand and capacity (DC) imbalance characterization and imbalance mitigation prescription.

Aviation provides the only rapid worldwide transportation network, which makes it essential for global business. It generates economic growth, creates jobs, and facilitates international trade and tourism. The project contributes to a more efficient air transport system, which consequently promotes an improved quality of life and helps to improve living standards.

To achieve this vision, four objectives are set:

a) Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells.

b) Characterisation of demand and capacity imbalances at pre-tactical level [-1D, -30min] depending on the input of probabilistic weather cells by using applied AI methods and ATM and weather data integration.

c) User-driven mitigation plan considering AUs priorities (and fluctuations in demand based on weather forecasts) and predicted effectiveness of ATFCM regulations, considering flow constraints and network effects.

d) Develop an operational and technical roadmap for the integration of ancillary services (providing AI-based hotspot detection and adaptative mitigation measures) into the NM platform, by defining interfaces, functional and performance requirements.