This publication is the continuation of previous research which aims at improving the predictability and the flexibility of the airspace management
process by computing realistic forecasts of the airspace configurations in En-route ATC centers. In previous papers, we selected relevant complexity
metrics to predict the controllers workload, using neural networks trained on historical data. We also introduced new algorithms to build optimally balanced
airspace configurations, exploring all possible combinations of elementary sectors. These workload prediction model and airspace partitioning algorithms
were tested on real recorded traffic.
In this paper, airspace configurations are forecast from planned traffic, using the CATS/OPAS simulator to compute trajectories from flight plans. The
efficiency of the resulting airspace configurations is assessed by comparing to the actual FMP (Flow Management Position) prediction. Some preliminary developments of an experimental HMI that will be used to test and tune our algorithms are also presented.
Theme: Dynamic Airspace and Capacity Management
Keywords: air traffic complexity, airspace configuration, controller workload, forecasting, neural network
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