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Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory

Authors :
Gregor Molan
Marija Molan
Source :
Safety and Health at Work, Vol 12, Iss 1, Pp 42-50 (2021)
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

Introduction: Despite huge investments in new technology and transportation infrastructure, terrible accidents still remain a reality of traffic. Methods: Severe traffic accidents were analyzed from four prevailing modes of today's transportations: sea, air, railway, and road. Main root causes of all four accidents were defined with implementation of the approach, based on Flanagan's critical incident technique. In accordance with Molan's Availability Humanization model (AH model), possible preventive or humanization interventions were defined with the focus on technology, environment, organization, and human factors. Results: According to our analyses, there are significant similarities between accidents. Root causes of accidents, human behavioral patterns, and possible humanization measures were presented with rooted graphs. It is possible to create a generalized model graph, which is similar to rooted graphs, for identification of possible humanization measures, intended to prevent similar accidents in the future. Majority of proposed humanization interventions are focused on organization. Organizational interventions are effective in assurance of adequate and safe behavior. Conclusions: Formalization of root cause analysis with rooted graphs in a model offers possibility for implementation of presented methods in analysis of particular events. Implementation of proposed humanization measures in a particular analyzed situation is the basis for creation of safety culture.

Details

Language :
English
ISSN :
20937911
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Safety and Health at Work
Publication Type :
Academic Journal
Accession number :
edsdoj.5b31218e65b649778f57e9469d7bc54a
Document Type :
article
Full Text :
https://doi.org/10.1016/j.shaw.2020.09.004