Exploiting spatio-temporal partial separability of large-scale airspaces

Julien Lavandier (1) , Marcel Mongeau (1) , Supatcha Chaimatanan (1) , Daniel Delahaye (1)

1 OPTIM - ENAC - Equipe Optim

This paper addresses large-scale flight planning via a divide-and-conquer technique that exploits the partial separability feature of the problem. 4D-interaction between flights is used to cluster the flights, and these clusters are then exploited to improve the optimization process. Preliminary computational experiments on the French airspace demonstrate the natural separability of air traffic and yield promising computational improvement for flight planning thanks to the clustering.

Mots clés: Large-scale flight planning; Alternative trajectory; Partial separability; Spatio-temporal clustering

Domaines: Intelligence artificielle [cs.AI]

Julien Lavandier, Marcel Mongeau, Supatcha Chaimatanan, Daniel Delahaye. Exploiting spatio-temporal partial separability of large-scale airspaces. International Workshop on ATM/CNS (IWAC) 2022, Oct 2022, Tokyo, Japan. ⟨hal-03852053⟩