Accident modelling using social network analysis for complex socio-technical systems

Prevention of safety occurrences has long been tied to accident modelling, in an attempt to understand how the event happened. Accident modelling has traditionally being based on understanding accidents as a linear sequence of events, however today modern software programs, particularly those being used for Social Network Analysis (SNA), offer the possibility to understand accidents as a complex network of contributing factors, perhaps for the first time. Aim: The aim of this research was to understand reoccurring patterns of contributing factors to major safety occurrences through the use of SNA. Method: Major railway accident reports were analysed and data collected on the contributing factors for high risk railway accidents for a five year period. The contributing factors were then modelled using SNA software programs. Results: The use of SNA has enabled the investigation and understanding of how safety occurrence contributing factors are interlinked (networked) and the contribution that each of the factors has on accident phenomenology in complex socio-technical systems. Discussion: The outcome of this project has been the development of a new method of accident modelling that moves beyond traditional linear models and better reflects modern day safety science thinking about complex systems. This type of modelling can be used as a proactive risk management tool by providing the knowledge of which contributing factors need to be controlled or mitigated for accident prevention.