Estimating runway veer-off risk using a Bayesian network with flight data

نویسندگان

چکیده

Risk assessments in airline operations are mostly qualitative, despite abundant data from programmes such as flight monitoring (FDM) and quality assurance (FOQA). In this paper, features relating to runway excursion causal factors extracted over 310,448 flights Airbus A320 series aircraft flown on a European network. The is combined with meteorological provide additional features. Bayesian networks then learnt the feature set, two network learning algorithms compared, Search Greedy Thick Thinning (GTT). Cross-validation of resulting shows both produce similarly performing networks, subjective analysis concludes that GTT algorithm marginally preferred. relative probabilities, which airlines can use quantitatively assess veer-off risk under different scenarios, conditions unstable approaches. This paper's main finding by utilising existing sources, FDM weather databases, create alongside their qualitative assessment methods quantitative understand effect those risks. not possible current airlines. method described be extended other operational safety risks, overrun.

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ژورنال

عنوان ژورنال: Transportation Research Part C-emerging Technologies

سال: 2021

ISSN: ['1879-2359', '0968-090X']

DOI: https://doi.org/10.1016/j.trc.2021.103180