Estimating runway veer-off risk using a Bayesian network with flight data. (July 2021)

• Record Type: Journal Article
• Authors: Barry, David J.
• Is Part Of: Transportation research. Volume 128(2021)
• Publication Date: 2021-07
• Subjects: Airline operational safety -- Flight data monitoring (FDM) -- Risk assessment with Bayesian networks -- Flight operations quality assurance (FOQA) -- Runway excursion -- Runway veeroff; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103180
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Bayesian networks can be learnt from airborne recorded flight data. Bayesian networks can be used to quantify airline risks. Greedy Thick Thinning is marginally preferable to Bayesian Search. Bayesian networks should be used in airline safety management to assess risk. Abstract: Risk 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.