University of Michigan, Ann Arbor, Transportation Research Institute
Abstract
Two key national crash data samples from the National Automotive Sampling System (NASS) program are the General Estimates System (GES) and the Crashworthiness Data System (CDS). The former is a larger (50,000 crashes per year) sample of police-reported crashes and contains only information coded from the police report. The latter is a smaller (~3,000 crashes per year) sample of light-vehicle towaway crashes that are investigated by trained accident investigators. One key advantage of CDS is that it includes estimated crash severity, or delta-V, assigned to each vehicle in a crash. Delta-V is the best single predictor of injury outcome and thus is a key variable in prediction models. However, because it requires special data collection, it is absent from police-report-based datasets.
In contrast to CDS, GES is a much larger sample and includes more than just light-vehicle crashes. Thus, it would be ideal to have delta-V available for GES crashes to improve models of injury outcome from those data. Some attempts to do this for individual crashes were unsuccessful (e.g., Farmer, 2003). However, in some analyses, especially statistical simulations, it is sufficient to have a distribution of delta-V for a given crash mode rather than tying a specific delta-V to a specific crash-involved vehicle.
This report presents a method of estimating a distribution of crash severity using only police-reported crash data. The approach uses an injury risk curve developed from CDS and a parametric distributional assumption for the delta-V distribution. While the distribution can take any parametric form, I use the lognormal in this report. The method uses maximum likelihood to fit parameters to the delta-V distribution based on the observed injury distribution using the police-reported KABCO scale. That is, individuals in crashes fall into one of five injury categories. Each pair of lognormal parameters produces a distribution of injury when multiplied by the injury risk curve. Thus, the parameters that produce the multinomial injury distribution that best fits the observed distribution are chosen for the estimated delta-V distribution. The report includes results from a simulation study as well as a fit to CDS data with known delta-V distribution.National Highway Traffic Safety Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/117575/1/103241.pdfDescription of 103241.pdf : Final repor
