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Triple probability density distribution model in the task of aviation risk assessment

    Ivan Ostroumov   Affiliation
    ; Karen Marais   Affiliation
    ; Nataliia Kuzmenko   Affiliation
    ; Nicoletta Fala   Affiliation

Abstract

The probability of an airplane deviation from pre-planned trajectory is a core of aviation safety analysis. We propose to use a mixture of three probability density distribution functions it the task of aviation risk assessment. Proposed model takes into account the effect of navigation system error, flight technical error, and occurrence of rare events. Univariate Generalized Error Distribution is used as a basic component of distribution functions, that configures the error distribution model from the normal error distribution to double exponential distribution function. Statistical fitting of training sample by proposed Triple Univariate Generalized Error Distribution (TUGED) is supported by Maximum Likelihood Method. Optimal set of parameters is estimated by sequential approximation method with defined level of accuracy. The developed density model has been used in risk assessment of airplane lateral deviation from runway centreline during take-off and landing phases of flight. The efficiency of the developed model is approved by Chi-square, Akaike’s, and Bayes information criteria. The results of TUGED fitting indicate better performance in comparison with double probability density distribution model. The risk of airplane veering off the runway is considered as the probability of a rare event occurrence and is estimated as an area under the TUGED.

Keyword : risk, airplane, aviation safety, probability density function, Triple Univariate Generalized Error Distribution, deviation, Maximum Likelihood Method, statistics

How to Cite
[1]
Ostroumov, I., Marais, K., Kuzmenko, N. and Fala, N. 2020. Triple probability density distribution model in the task of aviation risk assessment. Aviation. 24, 2 (Jul. 2020), 57-65. DOI:https://doi.org/10.3846/aviation.2020.12544.
Published in Issue
Jul 8, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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