Conditional and unconditional safety performance forecasts for aviation predictive risk management

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F18%3A00322388" target="_blank" >RIV/68407700:21260/18:00322388 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/AERO.2018.8396648" target="_blank" >http://dx.doi.org/10.1109/AERO.2018.8396648</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/AERO.2018.8396648" target="_blank" >10.1109/AERO.2018.8396648</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Conditional and unconditional safety performance forecasts for aviation predictive risk management

Popis výsledku v původním jazyce

This paper deals with safety performance predictions in the aviation, which address the long-term global efforts to achieve predictive risk management by the year 2028. Predictive risk management regards timely and accurate detection of risk, well before some incident or accident takes place so that effective control actions can be provided. To assure achieving such diagnosis, it is necessary that mathematically well-founded predictions will become part of existing safety management systems with the capability to predict key performance indicators. From current safety metrics and with respect to the data available in the aviation, overall safety performance was selected as suitable candidate for predictions. To obtain the performance signal, Aerospace Performance Factor methodology was utilized. Due to confidentiality restrictions with regard to aviation safety data, this study relies on public data sets from the domain of European Air Traffic Management. Dedicated resampling method was used to fill in the gaps of real data sets by transforming expert knowledge into mathematical functions. This enabled the possibility to build and test mathematical models for predicting safety performance. Because the identified data sources included some data, which are not necessary for computing safety performance but relevant in its context, conditional forecasts were made possible. With respect to this, the goal of this paper was to research and evaluate possibilities for both conditional and unconditional forecasts in the context of future risk management. Time-series analysis of the computed safety performance was conducted using ordinary least squares and maximum likelihood estimation. Each of the methodology led to different mathematical model and different predictions. Specific aspects of each methodology were identified.

Název v anglickém jazyce

Conditional and unconditional safety performance forecasts for aviation predictive risk management

Popis výsledku anglicky

This paper deals with safety performance predictions in the aviation, which address the long-term global efforts to achieve predictive risk management by the year 2028. Predictive risk management regards timely and accurate detection of risk, well before some incident or accident takes place so that effective control actions can be provided. To assure achieving such diagnosis, it is necessary that mathematically well-founded predictions will become part of existing safety management systems with the capability to predict key performance indicators. From current safety metrics and with respect to the data available in the aviation, overall safety performance was selected as suitable candidate for predictions. To obtain the performance signal, Aerospace Performance Factor methodology was utilized. Due to confidentiality restrictions with regard to aviation safety data, this study relies on public data sets from the domain of European Air Traffic Management. Dedicated resampling method was used to fill in the gaps of real data sets by transforming expert knowledge into mathematical functions. This enabled the possibility to build and test mathematical models for predicting safety performance. Because the identified data sources included some data, which are not necessary for computing safety performance but relevant in its context, conditional forecasts were made possible. With respect to this, the goal of this paper was to research and evaluate possibilities for both conditional and unconditional forecasts in the context of future risk management. Time-series analysis of the computed safety performance was conducted using ordinary least squares and maximum likelihood estimation. Each of the methodology led to different mathematical model and different predictions. Specific aspects of each methodology were identified.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE Aerospace Conference Proceedings

ISBN

978-1-5386-2014-4

ISSN

1095-323X

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE Xplore

Místo vydání

—

Místo konání akce

Big Sky

Datum konání akce

3. 3. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—