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A New Representation of Air Traffic Data Adapted to Complexity Assessment
- Source :
- ALLDATA 2018, The Fourth International Conference on Big Data, Small Data, Linked Data and Open Data, 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, 2018, ALLDATA 2018 Proceedings, 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
- Publication Year :
- 2018
- Publisher :
- HAL CCSD, 2018.
-
Abstract
- International audience; 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.
- Subjects :
- [ MATH ] Mathematics [math]
[STAT.AP]Statistics [stat]/Applications [stat.AP]
manifold valued images
[STAT.ME] Statistics [stat]/Methodology [stat.ME]
Riemannian manifold
[ STAT.AP ] Statistics [stat]/Applications [stat.AP]
[MATH] Mathematics [math]
non-parametric estimation
covariance function estimation
[ STAT.ME ] Statistics [stat]/Methodology [stat.ME]
[STAT.AP] Statistics [stat]/Applications [stat.AP]
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
spatial data
Air traffic complexity
[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]
[MATH]Mathematics [math]
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-61208-631-6
- ISBNs :
- 9781612086316
- Database :
- OpenAIRE
- Journal :
- ALLDATA 2018, The Fourth International Conference on Big Data, Small Data, Linked Data and Open Data, 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, 2018, ALLDATA 2018 Proceedings, 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
- Accession number :
- edsair.dedup.wf.001..8ff699c092000bd12f76ca7d6c82feb1