Re-Assessment of Road Accident Data-Analysis Policy: Applying Theory from Involuntary, High-Consequence, Low-Probability Events like Nuclear Power Plant Meltdowns to Voluntary, Low-Consequence, High-Probability Events like Traffic Accidents.

摘要

This report examines the literature on involuntary, high-consequence, low-probability (IHL) events like nuclear power plant meltdowns to determine what can be applied to the problem of voluntary, low-consequence high-probability (VLH) events like traffic accidents. Five closely related literatures on IHL events are examined: 'normal' accident theory, system reliability theory, highly 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. Then, we characterize and describe the models that traffic safety official 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 in order to investigate how to improve the collection and use of road traffic safety data based on analysis of the existing data and its use.