Literature DB >> 25142360

Estimating the over-involvement of suspended, revoked, and unlicensed drivers as at-fault drivers in California fatal crashes.

Sukhvir S Brar1.   

Abstract

INTRODUCTION AND
METHOD: Quasi-induced exposure analysis was used to estimate annual fatal crash involvement rates for validly licensed, suspended or revoked (S/R), and unlicensed drivers in California from 1987 through 2009 using fatal crash data obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System and crash culpability determinations from the California Highway Patrol's Statewide Integrated Traffic Records System.
RESULTS: Although there was some year-to-year fluctuation in the annual estimates, S/R and unlicensed drivers were over-involved as at-fault drivers in fatal crashes during every year of the 23-year time period relative to validly licensed drivers. The fatal crash involvement ratios combined across all years were 0.86 for validly licensed drivers, 2.23 for S/R drivers, and 2.34 for unlicensed drivers. Hence, compared to validly licensed drivers, the odds of being at-fault for a fatal crash were 160% higher for S/R drivers (involvement ratio=2.60) and 173% higher for unlicensed drivers (involvement ratio=2.73). The excess risks of S/R and unlicensed drivers are somewhat lower than estimates found in a prior study using the same technique, but the results nonetheless provide evidence that S/R and unlicensed drivers are much more hazardous on the road than are validly licensed drivers and emphasize the importance of using strong countermeasures-including vehicle impoundment-to reduce their high crash risk. These findings support interventions to help reduce driving among S/R and unlicensed drivers. Published by Elsevier Ltd.

Entities:  

Keywords:  California; Fatal crash rates; Quasi-induced exposure; Suspended/revoked drivers; Unlicensed drivers

Mesh:

Year:  2014        PMID: 25142360     DOI: 10.1016/j.jsr.2014.03.010

Source DB:  PubMed          Journal:  J Safety Res        ISSN: 0022-4375


  2 in total

1.  Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

Authors:  Chen Wang; Lin Liu; Chengcheng Xu; Weitao Lv
Journal:  Int J Environ Res Public Health       Date:  2019-01-25       Impact factor: 3.390

2.  Unlicensed driving among young drivers in North Carolina: a quasi-induced exposure analysis.

Authors:  Yudan Chen Wang; Robert D Foss; Arthur H Goodwin
Journal:  Inj Epidemiol       Date:  2022-08-16
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.