Applying theory from involuntary, high consequence, low probability events like nuclear power plant meltdowns to voluntary, low consequence, high probability events like traffic accidents: A re-assessment of road accidents data analysis policy

This paper examines the literature on involuntary, high consequence, low probability events like nuclear power plant meltdowns to determine what can be applied to the problem of voluntary, low consequence, high probability events like traffic accidents. Five closely related literatures on IHL events are examined: "normal" accident theory, system reliability theory, high reliable organizations theory, complexity and tight coupling theory, and a theory of feedback and learning (band of accident theory). Based on this literature we develop and test a series of propositions to explain traffic injuries and fatalities. To explain motor vehicle accidents, we carry out logistic regression analyses, examining driving conditions and decisions drivers make as factors that can lead to fatalities and injuries. We then characterize and describe the models that traffic safety officials use for understanding fatalities and injuries. These models are found in state crash data publications. We compare these models with the instructional material that is used in state driving educational manuals, the aim being to investigate how to improve the collection and use of road traffic safety data based on analysis of the existing data and its use.