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1 DGAC - Direction Générale de l-Aviation Civile

Abstract : 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 partitionning 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.

Keywords : air traffic complexity airspace configuration forecasting neural networks branch & bound CASSOS

Autor: David Gianazza - Cyril Allignol - Nicolas Saporito -



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