Road traffic crashes are a leading cause of death in the United States. In Kentucky, per capita crash rates and crash-related fatalities have outpaced the national average for over a decade. Wanting to explain why the U.S. Southeast sees higher crash rates than other regions, researchers have argued the region’s unique socioeconomic conditions provide a compelling explanation. Taking this observation as a starting point, this study examined the relationship between highway safety and socioeconomic characteristics using an extensive crash dataset from Kentucky. This research sought to identify at-risk drivers based on the socioeconomic and demographic attributes of the zip codes in which they reside. Using the quasi-induced exposure approach, binary logistic regression was used to develop predictions of driver at-fault probability based on socioeconomic characteristics of their residence zip code. Statistical analysis found that variables such as income, education level, poverty level, employment, age, gender, rurality, and number of traffic-related convictions of a driver’s zip code influence the likelihood of their being at fault in a crash. This finding can be used to identify groups of drivers most likely to be involved in crashes and develop targeted and efficient safety programs. Spatial analysis did not uncover robust correlations between county-level socioeconomic characteristics and at-fault driver involvement across the state. The results can be used to identify target groups for safety improvements and aid in the Kentucky Safety Circuit Rider Program activities.

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