Risk of mid-air collision in a lateral plane

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Risk of mid-air collision in a lateral plane

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Risk of mid-air collision in a lateral plane

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CEUR Workshop Proceedings

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

11

Strana od-do

297-307

Název nakladatele

Elsevier

Místo vydání

London

Místo konání akce

Kherson

Datum konání akce

15. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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