Crashes involving alcohol-impaired driving or driving under influence (DUI) are more likely to increase crash probability and severity. Many countries have adopted increasingly stringent policies in curbing DUI. Nevertheless, more and more studies show that while the total number of DUIs has reduced as a whole, DUI recidivism remains challenging. As such, this study seeks to research into whether it is possible to identify frequent DUI recidivists based on their traffic offense history, so that effective countermeasure could be put in place and in time. This study proposes using the drivers' traffic offense history and length of duration between two DUIs, the duration of re-offending, to identify frequent DUI recidivists. This approach is not only widely adopted in public health, but is also flexible in accommodating many modeling issues such as data censoring, recurrent events, and the inclusion of time-varying covariates to address questions like whether the probability of recidivism increases or decreases with subsequent DUI offenses or other traffic offenses or violations. Our major results show that: (1) For all drivers caught for a DUI, 10 % of them would be caught for another DUI within a year; (2) In contrast, the same one-year recidivism probability for those who accumulated two DUIs and two run-the-red-light could be as high as 17 %; (3) Each subsequent DUI increased the probability of a further DUI offense by 57 %; and (4) Each additional DUI offense was associated with 45 % increase in probability of being involved in a crash involving DUI. Overall, there are clear links between a driver's history of traffic offenses, DUI recidivism, and crash involvement, which could provide valuable information for authorities to profile potential recidivists and apply preventative measures in advance to reduce DUI-related crashes.


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    Title :

    Is a Driver's history of drunk driving and other traffic offenses linked to subsequent drunk driving and traffic crashes? A case study of Taiwan.


    Contributors:


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





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