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Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models

Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models

Hans Wienen, Faiza Allah Bukhsh, Eelco Vriezekolk, Roel J. Wieringa
Copyright: © 2018 |Volume: 10 |Issue: 3 |Pages: 21
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781522543848|DOI: 10.4018/IJISCRAM.2018070103
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MLA

Wienen, Hans, et al. "Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models." IJISCRAM vol.10, no.3 2018: pp.42-62. http://doi.org/10.4018/IJISCRAM.2018070103

APA

Wienen, H., Bukhsh, F. A., Vriezekolk, E., & Wieringa, R. J. (2018). Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 10(3), 42-62. http://doi.org/10.4018/IJISCRAM.2018070103

Chicago

Wienen, Hans, et al. "Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 10, no.3: 42-62. http://doi.org/10.4018/IJISCRAM.2018070103

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Abstract

After a risk has manifested itself and has led to an accident, valuable lessons can be learned to reduce the risk of a similar accident occurring again. This calls for accident analysis methods. In the past 20 years, a large number of accident analysis methods have been proposed and it is difficult to find the right method to apply in a specific circumstance. The authors conducted a review of the state of the art of accident analysis methods and models across domains. They classify the models using the well-known categorization into sequential, epidemiological, and systemic methods. The authors find that these classes have their own characteristics in terms of speed of application versus pay-off. For optimum risk reduction, methods that take organizational issues into account can add valuable information to the risk management process in an organization.

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