An Ordered Fractional Split Approach for Aggregate Injury Severity Modeling

In crash frequency models, frequency by severity level are examined using multivariate count models. In these multivariate approaches the impact of exogenous variables is quantified through the propensity component of count models. The main interaction among variables across different severity levels is sought through unobserved effects i.e. there is no interaction of observed effects across the multiple count models. While this might not be a limitation per se, it might be beneficial to evaluate the impact of exogenous variables in a framework that directly relates a single exogenous variable to all severity count variables simultaneously. Towards this end, an alternative approach to examine crash frequency by severity is proposed. Specifically, as opposed to modeling the number of crashes, we adopt a fractional split modeling approach, to study the fraction of crashes by each severity level on a road segment. Given the ordered nature of injury severity, we employ an ordered probit fractional split model to study crash proportion by severity levels. The model is estimated for roadways segment data for single vehicle and multi vehicle crashes of Florida for the year 2009 through 2011. The model estimation results clearly highlight the importance of traffic volume, lane width, shoulder width, proportion of divided segments, and speed limit on crash proportion by severity. The model results are employed to predict hot spots for different crash types. The results clearly highlight how the ordered probit fractional split models can be employed for highway safety screening purposes.