WP1 Enhanced ATFCM process
ISOBAR has proposed an enhancement of the Collaborative ATFCM process both at local (FMP) and network (NM) level by describing a process based on the integration of convective weather probabilistic forecast tailored to the spatial and temporal granularities of the ATFCM process. The enhanced ATFCM process has been described in a SESAR OSED, including the Gate concept, the Netspot concept and the operational integration of new AI components. A global communication prototype has been developed over a real Network Manager dashboard.
WP2 Probabilistic Convection Input to ATFCM
The ISOBAR MetEngine provides meteorological forecasts at network and local levels based on the use of Artificial Intelligence along with Numerical Weather Prediction (NWP). Three different NWP products have been chosen:
• AROME-EPS for France region,
• the AEMET (γSREPS for the Iberian Peninsula,
• and the ECMWF (European Centre for Medium-Range Weather Forecasts) EPS for Western Europe.
A set of web-based ISOBAR MetEngine dashboards has been developed.
WP3 Demand and Capacity Prediction
The work performed has been split in two threads:
• AUs Alternative Trajectories Demand Characterisation. AU preference has been adopted through two following factors: Aircraft Type and Aircraft Operator. The predictions have been obtained for triplets (city-par, aircraft type and aircraft operator)
• Capacity Decay Prediction. Three approaches were considered: Using gates, where a set of gates were defined over the European airspace, and capacity was defined as the volume of traffic crossing a gate. Using a traffic flow approach, where the flight trajectories were used to reconstruct an image of the traffic patterns over a region. Using sectors.
WP4 Automated DCB Solver Suite
The work on DCB solvers has been focused on delivering artificial-intelligence-based solvers based on two different artificial-intelligence paradigms:
• Multi-Agent Reinforcement Learning (MARL);
• Simulated-Annealing-based Hyper-Heuristic.
In addition, 4 non-AI solvers have been developed: Greedy regulations, Cherry-picking, Optimised regulations and Hybrid.
WP5 Data integration and architecture
This work has corresponded to data Extract, Transform and Load (ETL) in the project. Data ETL has served the integration of the new proposed services into a local or a NM platform by processing inputs and providing early architecture analysis. The results have crystallised in a set of diagrams depicting the operational dataflows between the ISOBAR solution components that would form the target operational architecture.
WP5 & WP6 ISOBAR Solution Prototype and Evaluation
The validation of ISOBAR concept and technical developments has been addressed through validation tasks, organised around four activities and two exercises:
• ACT01: to assess the performance of a Machine Learning model providing probabilistic convective weather information.
• ACT02: model providing weather-related capacity reduction and imbalance prediction.
• ACT03: model providing mitigation plans.
• ACT04: model providing AU Preferred Trajectory alternatives.
• EXE01: operational evaluation of ISOBAR Collaborative Framework.
• EXE02: Fast-Time Validation Exercise to assess the performance benefits of the global solution.
The operational evaluation has proven realistic behaviour and logical solutions.
The performance evaluation has addressed four KPAs: capacity, predictability, environment and safety.
