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

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dc.contributor.authorBarry, David J.
dc.date.accessioned2021-06-04T09:26:19Z
dc.date.available2021-06-04T09:26:19Z
dc.date.issued2020-05-23
dc.description.abstractRisk assessments in airline operations are mostly qualitative, despite abundant data from programmes such as flight data monitoring (FDM) and flight operations quality assurance (FOQA). In this paper, features relating to runway excursion causal factors are extracted from flight data from over 310,448 flights from Airbus A320 series aircraft flown on a European network. The data is combined with meteorological data to provide additional features. Bayesian networks are then learnt from the feature set, and two network learning algorithms are compared, Bayesian Search and Greedy Thick Thinning (GTT). Cross-validation of the resulting networks shows both algorithms produce similarly performing networks, and a subjective analysis concludes that the GTT algorithm is marginally preferred. The resulting networks produce relative probabilities, which airlines can use to quantitatively assess runway veer-off risk under different scenarios, such as different meteorological conditions and unstable approaches. This paper's main finding is that by utilising existing data sources, such as FDM and weather databases, airlines can create and use Bayesian networks alongside their existing qualitative risk assessment methods to provide quantitative risk assessment and understand the effect of different conditions on those risks. This is not possible with current methods in use by airlines. The method described can be extended to other operational safety risks, such as runway overrun.en_UK
dc.identifier.citationBarry DJ. (2021) Estimating runway veer-off risk using a Bayesian network with flight data. Transportation Research Part C: Emerging Technologies, Voume 128, July 2021, Article number 103180en_UK
dc.identifier.issn0968-090X
dc.identifier.urihttps://doi.org/10.1016/j.trc.2021.103180
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16732
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRunway veeroffen_UK
dc.subjectRunway excursionen_UK
dc.subjectFlight operations quality assurance (FOQA)en_UK
dc.subjectRisk assessment with Bayesian networksen_UK
dc.subjectFlight data monitoring (FDM)en_UK
dc.subjectAirline operational safetyen_UK
dc.titleEstimating runway veer-off risk using a Bayesian network with flight dataen_UK
dc.typeArticleen_UK

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