Spatial clustering and route-based analysis of DUI incidents for enhancing traffic enforcement deployment

Driving under the influence (DUI) remains a persistent threat to public safety, especially in dense urban areas where alcohol use and vehicle traffic often intersect. This study develops one of the first integrated frameworks that link DUI origins, travel patterns, crash locations, and enforcement points to inform adaptive, route-based enforcement strategies.

Using 17,075 DUI incidents in Seoul from 2021-2022, the study combines network-based shortest-path estimation and logistic regression to model offender movement, assuming the Place of Last Drink (POLD) as the trip origin. A Monte Carlo sensitivity test confirms the robustness of high-risk corridor identification under spatial uncertainty. The study employs multiscale spatial analysis and hotspot typology, including Global Moran's I, Getis-Ord Gi*, hotspot classification, performance assessment, and enforcement strategies.

DUI travel was highly localized, with nearly half of the trips happening within 5 km of the starting point. High-risk corridors stayed consistent under different origin assumptions, supporting the use of POLD as a reliable proxy. Spatial analysis revealed strong clustering of origins and enforcement close to each other, while crash sites were more spread out. A hotspot classification identified about 3.7% of nodes as high-risk areas with limited enforcement, emphasizing the need for proactive and targeted DUI prevention strategies.

The findings emphasize route-based DUI enforcement along urban networks, combining static checkpoints and dynamic patrols to improve efficiency in high-risk areas. Despite potential biases in self-reported origin data, the framework remains robust and offers practical insights for DUI prevention and policy development.