Risk of mid-air collision in a lateral plane
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F20%3A43896099" target="_blank" >RIV/44555601:13440/20:43896099 - isvavai.cz</a>
Result on the web
<a href="https://ceur-ws.org/Vol-2805/paper22.pdf" target="_blank" >https://ceur-ws.org/Vol-2805/paper22.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Risk of mid-air collision in a lateral plane
Original language description
Mid-air collision in air transportation is one of the most dangerous safety categories. The risk of mid-air collision assessment is an important component of aviation safety estimation. Due to the low number of accidents happened, risk of mid-air collision within limited airspace may be estimated by evaluation of its main components. Paper is more focused on assessing the risk of air traffic separation lost in lateral plane based on air traffic deep learning within predefined airspace. Statistical analysis of current air traffic data and geometrical configuration of routes network are used for probability distribution function fitting. Position of airspace users is obtained from location reports coded by Automatic Dependent Surveillance-Broadcast data format, which is received by ground-based software defined radio. Risk of separation lost in the lateral plane is estimated based on density probability distribution function of airplane unintentional deviations. Finally, the risk of a mid-air collision in the lateral plane is estimated by Reich formula for Ukrainian airspace. Copyright (C) 2020 for this paper by its authors.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CEUR Workshop Proceedings
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
11
Pages from-to
297-307
Publisher name
Elsevier
Place of publication
London
Event location
Kherson
Event date
Oct 15, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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