Safety and Health at Work Vol. 12 Issue 1 2021
Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory (Original Article)
Dublin Core
Title
Safety and Health at Work Vol. 12 Issue 1 2021
Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory (Original Article)
Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory (Original Article)
Subject
Accident preventions, Behavior, Graph partition, Root cause analysis, Safety
Description
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.
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.
Creator
Gregor Molan , Marija Molan
Publisher
Elsevier Korea LLC
Date
March 2021
Contributor
Sri Wahyuni
Format
PDF
Language
English
Type
Text
Coverage
Safety and Health at Work Vol. 12 Issue 1 2021
Files
Citation
Gregor Molan , Marija Molan, “Safety and Health at Work Vol. 12 Issue 1 2021
Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory (Original Article),” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/2124.
Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory (Original Article),” Repository Horizon University Indonesia, accessed March 14, 2025, https://repository.horizon.ac.id/items/show/2124.