Effectiveness of roadside alcohol testing in reducing fatal accidents and fatal drinking-driving accidents: A multi-city study in China

Abstract

This study examines the dynamic relationship between roadside alcohol check rates and traffic mortality across 248 cities in mainland China from 2014 to 2020. Using a dataset comprising 365,753 roadside check arrests, 227,896 traffic deaths, and 21,036 DUI-related fatalities, we applied both traditional time series analysis (Vector Autoregression and Impulse Response Functions) and machine learning techniques (XGBoost) to explore temporal and nonlinear patterns. The time series analysis revealed an initial positive association between enforcement intensity and mortality rates, likely reflecting reactive increases in checks following fatality rises. However, a longer-term beneficial effect emerged after approximately eight months, particularly pronounced in smaller cities. Machine learning models revealed that roadside check rates are more strongly associated with overall traffic mortality than with DUI-specific deaths. Enforcement was found to have a greater impact in smaller cities compared to larger ones. Notably, the patterns reveal diminishing returns in enforcement effectiveness, with the marginal benefits tapering off around 0.002 per 100,000 people per month in large cities, while remaining evident up to about 0.006 in small cities. These findings suggest that increasing roadside checks in low population density areas may lead to the most significant reductions in traffic fatalities at the national level. However, limitations include data exclusions due to non-disclosure and the inability to determine causal mechanisms. Overall, the study offers valuable insights for optimizing DUI enforcement strategies, highlighting the importance of tailored approaches based on city size and enforcement thresholds.