Highlights The increased number of reports submitted to the Aviation Safety Reporting System during the onset of COVID-19 highlights a need to evaluate safety issues during periods of disruption. The generalized random forest and difference-in-difference techniques were used to estimate the heterogeneous effects of COVID-19. Certain categories of variables and attributes were identified as contributing to incursions/excursions during COVID-19. The findings provide aviation organizations insights to improve prevention and recovery mechanisms for future pandemics or extended disruptions to aviation operations.

    Abstract Introduction: Disruptions to aviation operations occur daily on a micro-level with negligible impacts beyond the inconvenience of rebooking and changing aircrew schedules. The unprecedented disruption in global aviation due to COVID-19 highlighted a need to evaluate emergent safety issues rapidly. Method: This paper uses causal machine learning to examine the heterogeneous effects of COVID-19 on reported aircraft incursions/excursions. The analysis utilized self report data from NASA Aviation Safety Reporting System collected from 2018 to 2020. The report attributes include self identified group characteristics and expert categorization of factors and outcomes. The analysis identified attributes and subgroup characteristics that were most sensitive to COVID-19 in inducing incursions/excursions. The method included the generalized random forest and difference-in-difference techniques to explore causal effects. Results: The analysis indicates first officers are more prone to experiencing incursion/excursion events during the pandemic. In addition, events categorized with the human factors confusion, distraction, and the causal factor fatigue increased incursion/excursion events. Practical Applications: Understanding the attributes associated with the likelihood of incursion/excursion events provides policymakers and aviation organizations insights to improve prevention mechanisms for future pandemics or extended periods of reduced aviation operations.


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    Titel :

    The effect of COVID-19 on self-reported safety incidents in aviation: An examination of the heterogeneous effects using causal machine learning


    Beteiligte:
    Choi, Youngran (Autor:in) / Gibson, James R. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-12-01


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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