Uncovering latent structures of crash typology in narcotic-involved fatal crashes for safe system interventions
本研究利用2018-2022年美国致命事故数据，通过聚类对应分析识别出四种毒品相关致命碰撞类型，并针对每种类型提出安全系统干预措施，对交通政策制定者和安全工程师有参考价值。
Narcotic-impaired driving increases the risk of fatal crashes, yet existing studies rarely provide narcotic-specific crash typologies that link driver impairment to roadway, traffic, and environmental conditions. This gap limits the design of Safe System interventions that can proactively address the most common high-risk configurations. Using Fatality Analysis Reporting System data from 2018 to 2022, this study applies Cramér's V statistic for variable selection and Cluster Correspondence Analysis (CCA) to explore unsupervised crash typologies and latent patterns of narcotics-involved fatal crashes. CCA biplot coordinates group crashes into four clusters: high-speed lane changes on uncontrolled arterials, run-off-road impacts with rollovers, nighttime pedestrian or cyclist strikes on unlit roads, and moderate-speed angle crashes at signalized intersections. Results show that speed and lateral control failures dominate the first two clusters, narcotic-induced sensory and cognitive deficits under low visibility drive the third, and decision-making errors during turn phases characterize the fourth. Key factors such as posted speed limit, lighting condition, and driver age exert cluster-specific influences on incapacitating and fatal injury outcomes. These findings underscore the inadequacy of appropriate countermeasures and point to Safe System-aligned interventions, including dynamic speed management, enhanced roadside clear zones, targeted lighting upgrades, and intersection control strategies.