Évaluation of air traffic complexity metrics using neural networks and sector status

David Gianazza (1) , Kevin Guittet (2)

1 DGAC - Direction Générale de l'Aviation Civile
2 LEEA - ENAC - Laboratoire d'Economie et d'Econométrie de l'Aérien

This paper presents an original method to evaluate air traffic complexity metrics. Several complexity indicators, found in the litterature, were implemented and computed, using recorded radar data as input. A principal component analysis (PCA) provides some results on the correlations between these indicators. Neural networks are then used to find a relationship between complexity indicators and the actual sector configurations. Assuming that the decisions to group or split sectors are somewhat related to the controllers workload, this method allows to identify which types of complexity indicators are significantly related to the actual workload.

Mots clés: air traffic complexity; dynamic density; neural networks; S2D2

Domaines: Optimisation et contrôle [math.OC]

David Gianazza, Kevin Guittet. Évaluation of air traffic complexity metrics using neural networks and sector status. ICRAT 2006, 2nd International Conference on Research in Air Transportation, Jun 2006, Belgrade, Serbia. pp xxxx. ⟨hal-00938105⟩