A New Representation of Air Traffic Data Adapted to Complexity Assessment

Georges Mykoniatis (1) , Florence Nicol (1) , Stéphane Puechmorel (1)

1 ENAC - Ecole Nationale de l'Aviation Civile

Air traffic is generally characterized by simple indicators like the number of aircraft flying over a given area or the total distance flown during a time window. As an example, these values may be used for estimating a rough number of air traffic controllers needed in a given control center or for performing economic studies. However, this approach is not adapted to more complex situations such as those encountered in airspace comparison or air traffic controllers training. An innovative representation of the traffic data, relying on a sound theoretical framework, is introduced in this work. It will pave the way to a number of tools dedicated to traffic analysis. Based on an extraction of local covariance, a grid with values in the space of symmetric positive definite matrices is obtained. It can serve as a basis of comparison or be subject to filtering and selection to obtain a digest of a traffic situation suitable for efficient complexity assessment.

Mots clés: Air traffic complexity; spatial data; manifold valued images; covariance function estimation; non-parametric estimation; Riemannian manifold

Domaines: Statistiques [math.ST]; Applications [stat.AP]; Méthodologie [stat.ME]; Mathématiques [math]

Georges Mykoniatis, Florence Nicol, Stéphane Puechmorel. A New Representation of Air Traffic Data Adapted to Complexity Assessment. ALLDATA 2018, The Fourth International Conference on Big Data, Small Data, Linked Data and Open Data, Apr 2018, Athenes, Greece. pp. 28-33/ISBN: 978-1-61208-631-6. ⟨hal-01799373⟩