A Benefit/Cost/Deficit (BCD) model for learning from human errors
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 a...
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| Veröffentlicht in: | Reliability engineering & system safety 2011-07, Vol.96 (7), p.757-766 |
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| container_title | Reliability engineering & system safety |
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| creator | Vanderhaegen, Frédéric Zieba, Stéphane Enjalbert, Simon Polet, Philippe |
| description | 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. |
| doi_str_mv | 10.1016/j.ress.2011.02.002 |
| format | Article |
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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.</description><identifier>ISSN: 0951-8320</identifier><identifier>EISSN: 1879-0836</identifier><identifier>DOI: 10.1016/j.ress.2011.02.002</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Applied sciences ; Automatic ; BCD model ; Biological and medical sciences ; Car driving ; Case-based reasoning ; Cognition. Intelligence ; Computer Science ; Criteria ; Decision making. Choice ; Drivers ; Driving ; Engineering Sciences ; Exact sciences and technology ; Fundamental and applied biological sciences. Psychology ; Human ; Human error ; Human error prediction ; Learning ; Learning process ; Machine Learning ; Mathematical models ; Neural network ; Operational research and scientific management ; Operational research. Management science ; Psychology. Psychoanalysis. Psychiatry ; Psychology. Psychophysiology ; Reliability theory. 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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.</description><subject>Applied sciences</subject><subject>Automatic</subject><subject>BCD model</subject><subject>Biological and medical sciences</subject><subject>Car driving</subject><subject>Case-based reasoning</subject><subject>Cognition. Intelligence</subject><subject>Computer Science</subject><subject>Criteria</subject><subject>Decision making. Choice</subject><subject>Drivers</subject><subject>Driving</subject><subject>Engineering Sciences</subject><subject>Exact sciences and technology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Human</subject><subject>Human error</subject><subject>Human error prediction</subject><subject>Learning</subject><subject>Learning process</subject><subject>Machine Learning</subject><subject>Mathematical models</subject><subject>Neural network</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Reliability theory. Replacement problems</subject><subject>Safety</subject><subject>Violation</subject><issn>0951-8320</issn><issn>1879-0836</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kMFO4zAQhq0VK21heYE95YKAQ9IZ22liiUspu4BUiQucLdcZL66SGOwUibfHVRFHTjMaff8_0sfYH4QKARfzbRUppYoDYgW8AuA_2AzbRpXQisURm4GqsWwFh1_sOKUtAEhVNzO2XBbXNJLz03wV0jS_yav1U3Fxvbq5LIbQUV-4EIueTBz9-L9wMQzF824wY0Exhph-s5_O9IlOP-cJe_r393F1V64fbu9Xy3VppYSpFMY5dLYWndpsJEpbb0BshG2E4AIVkum46qyolZFOSeQKrULlhMugzbkTdnnofTa9fol-MPFdB-P13XKt9zcQCyk5ijfM7PmBfYnhdUdp0oNPlvrejBR2Sbdt_lADqkzyA2ljSCmS-6pG0Hu1eqv3avVerQaus9ocOvusN8ma3kUzWp--klxi09QLyNzVgaPs5c1T1Ml6Gi11PpKddBf8d28-AJtVjFU</recordid><startdate>20110701</startdate><enddate>20110701</enddate><creator>Vanderhaegen, Frédéric</creator><creator>Zieba, Stéphane</creator><creator>Enjalbert, Simon</creator><creator>Polet, Philippe</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-9229-5285</orcidid><orcidid>https://orcid.org/0000-0002-1938-752X</orcidid></search><sort><creationdate>20110701</creationdate><title>A Benefit/Cost/Deficit (BCD) model for learning from human errors</title><author>Vanderhaegen, Frédéric ; Zieba, Stéphane ; Enjalbert, Simon ; Polet, Philippe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-3aff1fc53d9bb414c5b03b3c73323191ead29dc359a4f941291c919f3f5b0cc53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applied sciences</topic><topic>Automatic</topic><topic>BCD model</topic><topic>Biological and medical sciences</topic><topic>Car driving</topic><topic>Case-based reasoning</topic><topic>Cognition. Intelligence</topic><topic>Computer Science</topic><topic>Criteria</topic><topic>Decision making. Choice</topic><topic>Drivers</topic><topic>Driving</topic><topic>Engineering Sciences</topic><topic>Exact sciences and technology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Human</topic><topic>Human error</topic><topic>Human error prediction</topic><topic>Learning</topic><topic>Learning process</topic><topic>Machine Learning</topic><topic>Mathematical models</topic><topic>Neural network</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Reliability theory. Replacement problems</topic><topic>Safety</topic><topic>Violation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vanderhaegen, Frédéric</creatorcontrib><creatorcontrib>Zieba, Stéphane</creatorcontrib><creatorcontrib>Enjalbert, Simon</creatorcontrib><creatorcontrib>Polet, Philippe</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Reliability engineering & system safety</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vanderhaegen, Frédéric</au><au>Zieba, Stéphane</au><au>Enjalbert, Simon</au><au>Polet, Philippe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Benefit/Cost/Deficit (BCD) model for learning from human errors</atitle><jtitle>Reliability engineering & system safety</jtitle><date>2011-07-01</date><risdate>2011</risdate><volume>96</volume><issue>7</issue><spage>757</spage><epage>766</epage><pages>757-766</pages><issn>0951-8320</issn><eissn>1879-0836</eissn><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. 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| identifier | ISSN: 0951-8320 |
| ispartof | Reliability engineering & system safety, 2011-07, Vol.96 (7), p.757-766 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Applied sciences Automatic BCD model Biological and medical sciences Car driving Case-based reasoning Cognition. Intelligence Computer Science Criteria Decision making. Choice Drivers Driving Engineering Sciences Exact sciences and technology Fundamental and applied biological sciences. Psychology Human Human error Human error prediction Learning Learning process Machine Learning Mathematical models Neural network Operational research and scientific management Operational research. Management science Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Reliability theory. Replacement problems Safety Violation |
| title | A Benefit/Cost/Deficit (BCD) model for learning from human errors |
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