Identifying high-risk commercial vehicle drivers using sociodemographic characteristics

Abstract

Crash data, from the state of Kentucky, for the 2015-2016 period, show that per capita crash rates and increases in crash-related fatalities were higher than the national average. In an effort to explain why the U.S. Southeast experiences higher crash rates than other regions of the country, previous research has argued the regions unique socioeconomic conditions provide a compelling explanation. Taking this observation as a starting point, this study examines the relationship between highway safety and socioeconomic and demographic characteristics, using an extensive crash dataset from Kentucky. Its focus is single- and two-unit crashes that involve commercial motor vehicles (CMVs) and automobiles. Using binary logistic regression and the quasi-induced exposure technique to analyze data on the socioeconomic and demographic attributes of the zip codes in which drivers reside, factors are identified which can serve as indicators of crash occurrence. Variables such as income, education level, poverty level, employment, age, gender, and rurality of the driver’s zip code influence the likelihood of a driver being at fault in a crash. Socioeconomic factors exert a similar influence on CMV and automobile crashes, irrespective of the number of vehicles involved. Research findings can be used to identify groups of drivers most likely to be involved in crashes and develop targeted and efficient safety programs.