 A Benefit/Cost/Deficit (BCD) model for learning from human errorsVanderhaegen, Frédéric, Zieba, Stéphane, Simon Enjalbert and Philippe Polet Reliability Engineering and System Safety, 2011, vol. 96, issue 7, 757-766 Abstract: This paper proposes an original model for interpreting human errors, mainly violations, in terms of benefits, costs and potential deficits. This BCD model is then used as an input framework to learn from human errors, and two systems based on this model are developed: a case-based reasoning system and an artificial neural network system. These systems are used to predict a specific human car driving violation: not respecting the priority-to-the-right rule, which is a decision to remove a barrier. Both prediction systems learn from previous violation occurrences, using the BCD model and four criteria: safety, for identifying the deficit or the danger; and opportunity for action, driver comfort, and time spent; for identifying the benefits or the costs. The application of learning systems to predict car driving violations gives a rate over 80% of correct prediction after 10 iterations. These results are validated for the non-respect of priority-to-the-right rule. Keywords: BCD model; Violation; Human error; Neural network; Case-based reasoning; Learning process; Human error prediction; Car driving (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:7:p:757-766 DOI: 10.1016/j.ress.2011.02.002 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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