An efficient airspace configuration forecast

David Gianazza (1) , Cyril Allignol (1) , Nicolas Saporito (1)

1 DGAC - Direction Générale de l'Aviation Civile

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.

Mots clés: air traffic complexity; airspace configuration; forecasting; neural networks; branch & bound; CASSOS

Domaines: Optimisation et contrôle [math.OC]

David Gianazza, Cyril Allignol, Nicolas Saporito. An efficient airspace configuration forecast. ATM 2009, 8th USA/Europe Air Traffic Management Research and Developpment Seminar, Jun 2009, Napa, United States. pp xxxx. ⟨hal-01020720⟩