Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116449
Title: Exploring the dynamic determinants of general aviation accidents across flight phases and time : a random parameter bivariate probit approach with heterogeneity in means
Authors: Liu, Q 
Song, P 
Li, F 
Issue Date: Sep-2025
Source: Analytic methods in accident research, 2025, v. 47, 100386
Abstract: General aviation experiences significant variation in accident characteristics across flight phases. This study seeks to investigate the phase transferability and temporal stability of determinants influencing general aviation accidents, using the U.S. data (2008–2019) from the National Transportation Safety Board. To achieve this, a random parameter bivariate approach with heterogeneity in means was employed, focusing on two binary outcomes: injury severity (fatal/severe vs. minor/none) and aircraft damage (destroyed vs. non-destroyed). Four flight phases were analyzed: departure, enroute, maneuvering, and arrival. The data were divided into three time periods, 2008–2011, 2012–2015, and 2016–2019, to assess the determinants’ temporal stability. Likelihood ratio tests revealed that pilot injury and aircraft damage risks exhibit phase non-transferability and temporal instability. Out-of-sample predictions indicated a steady rise in fatal or severe injury risk, while aircraft damage risk initially increased before declining over time. A significant positive correlation between pilot injury and aircraft damage was observed through model estimation. Key factors, including pilot, aircraft, flight, and environmental conditions, significantly influenced both outcomes. Moreover, factors such as decision-making errors, adverse physiological conditions, fixed landing gear, and visual meteorological conditions showed both phase transferability and temporal stability. However, most factors were phase- and period-specific. Based on these findings, targeted measures, such as pilot escape and survival training, as well as phase-specific, scenario-based training, are proposed to mitigate general aviation risks.
Keywords: Aircraft damage
Flight phases
General aviation
Pilot injury severity
Random parameters bivariate probit model
Publisher: Elsevier
Journal: Analytic methods in accident research 
ISSN: 2213-6657
DOI: 10.1016/j.amar.2025.100386
Appears in Collections:Journal/Magazine Article

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