title: Development of a method to rate the primary safety of vehicles using linked New Zealand crash and vehicle licensing data.
abstract: OBJECTIVE
  Vehicle safety rating systems aim firstly to inform consumers about safe vehicle choices and, secondly, to encourage vehicle manufacturers to aspire to safer levels of vehicle performance. Primary rating systems (that measure the ability of a vehicle to assist the driver in avoiding crashes) have not been developed for a variety of reasons, mainly associated with the difficult task of disassociating driver behavior and vehicle exposure characteristics from the estimation of crash involvement risk specific to a given vehicle. The aim of the current study was to explore different approaches to primary safety estimation, identifying which approaches (if any) may be most valid and most practical, given typical data that may be available for producing ratings.
  
  
  METHODS
  Data analyzed consisted of crash data and motor vehicle registration data for the period 2003 to 2012: 21,643,864 observations (representing vehicle-years) and 135,578 crashed vehicles. Various logistic models were tested as a means to estimate primary safety: Conditional models (conditioning on the vehicle owner over all vehicles owned); full models not conditioned on the owner, with all available owner and vehicle data; reduced models with few variables; induced exposure models; and models that synthesised elements from the latter two models.
  
  
  RESULTS
  It was found that excluding young drivers (aged 25 and under) from all primary safety estimates attenuated some high risks estimated for make/model combinations favored by young people. The conditional model had clear biases that made it unsuitable. Estimates from a reduced model based just on crash rates per year (but including an owner location variable) produced estimates that were generally similar to the full model, although there was more spread in the estimates. The best replication of the full model estimates was generated by a synthesis of the reduced model and an induced exposure model.
  
  
  CONCLUSIONS
  This study compared approaches to estimating primary safety that could mimic an analysis based on a very rich data set, using variables that are commonly available when registered fleet data are linked to crash data. This exploratory study has highlighted promising avenues for developing primary safety rating systems for vehicle makes and models.
authors.affiliation: a Wellington School of Medicine and Health Sciences, Otago University , Wellington South , New Zealand.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Keall
authors.affiliation: b Monash University Accident Research Centre , Melbourne , Australia.
authors.forename: Stuart
authors.initials: S
authors.lastname: Newstead
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D016208:Databases, Factual; D006801:Humans; D008004:Licensure / Q000706:statistics & numerical data*; D016015:Logistic Models; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D009520:New Zealand; D010067:Ownership / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods; D012449:Safety
keywords: consumer information; crash avoidance; primary safety; ratings system; vehicle safety