Bivariate modeling reveals injury severity interdependence in drivers, indicating weather amplifies risk factors.
Existing research on driving under the influence (DUI) crashes predominantly employs independent modeling frameworks that overlook the interdependency between injury outcomes of impaired and sober drivers, potentially leading to biased parameter estimates and an incomplete understanding of crash mechanisms. This study develops a copula-based bivariate ordered response modeling framework to investigate how injury severities of DUI and non-DUI drivers are interdependent and how this dependency varies systematically across weather conditions. Using crash data from the U.S. Crash Report Sampling System (2016–2022), we analyze 3773 two-vehicle crashes involving one alcohol-impaired and one sober driver under clear, rain/snow, and fog conditions. Three key findings emerge from our analysis. First, injury severities between DUI and non-DUI drivers exhibit significant dependency, with both the strength and structure of this association varying systematically across weather conditions. Dependency intensity increases progressively from clear weather (Kendall’s τ = 0.2717) to rain/snow (0.2966) and peaks under fog (0.3239). Moreover, the optimal dependency structure differs by weather conditions. Second, DUI and non-DUI drivers demonstrate markedly differentiated response patterns to risk factors, with the same factor often producing opposite-direction or substantially different magnitude effects on the two parties. Third, weather conditions play a critical moderating role, with most risk factors exhibiting significant amplification effects on crash injury severity under adverse weather. For example, on curved roadways under fog compared to clear weather, severe/fatal injury risk increases from 4.45% to 5.81% for DUI drivers and from 7.99% to 11.36% for non-DUI drivers. These findings highlight the importance of joint dependency modeling in alcohol-related crash research and provide evidence-based insights for weather-sensitive DUI enforcement and targeted safety interventions.
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Huo et al. (2026) studied this question.
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