The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives

► This paper provides a state-of-the-practice review of statistical methods for analyzing crash-injury severity data. ► Methodological issues germane to the analysis of injury severity data are discussed. ► Appropriate techniques for addressing these methodological issues are described. ► These techniques range from simple, binary outcome models to more sophisticated models allowing for heterogeneous effects and correlated error terms. ► Areas of opportunity for future research are highlighted based upon advances in computing and richer data sources. Reducing the severity of injuries resulting from motor-vehicle crashes has long been a primary emphasis of highway agencies and motor-vehicle manufacturers. While progress can be simply measured by the reduction in injury levels over time, insights into the effectiveness of injury-reduction technologies, policies, and regulations require a more detailed empirical assessment of the complex interactions that vehicle, roadway, and human factors have on resulting crash-injury severities. Over the years, researchers have used a wide range of methodological tools to assess the impact of such factors on disaggregate-level injury-severity data, and recent methodological advances have enabled the development of sophisticated models capable of more precisely determining the influence of these factors. This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.