IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v235y2021i4p637-659.html
   My bibliography  Save this article

Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data

Author

Listed:
  • Salvatore F Greco
  • Luca Podofillini
  • Vinh N Dang

Abstract

Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns†that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

Suggested Citation

  • Salvatore F Greco & Luca Podofillini & Vinh N Dang, 2021. "Crew performance variability in human error probability quantification: A methodology based on behavioral patterns from simulator data," Journal of Risk and Reliability, , vol. 235(4), pages 637-659, August.
  • Handle: RePEc:sae:risrel:v:235:y:2021:i:4:p:637-659
    DOI: 10.1177/1748006X20986743
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X20986743
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X20986743?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:235:y:2021:i:4:p:637-659. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.