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A New Representation of Air Traffic Data Adapted to Complexity Assessment

Authors:
Georges Mykoniatis
Florence Nicol
Stephane Puechmorel

Keywords: Air traffic complexity, spatial data, manifold valued images, covariance function estimation, non-parametric estimation

Abstract:
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 on 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 sound a theoretical framework is introduced in this work. It will pave the way to a whole bunch of tools dedicated to traffic analyzes. 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.

Pages: 28 to 33

Copyright: Copyright (c) IARIA, 2018

Publication date: April 22, 2018

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-631-6

Location: Athens, Greece

Dates: from April 22, 2018 to April 26, 2018