EFFECT OF SOCIOECONOMIC AND DEMOGRAPHIC FACTORS ON KENTUCKY CRASHES

The goal of this research was to examine the potential predictive ability of socioeconomic and demographic data for drivers on Kentucky crash occurrence. Identifying unique background characteristics of at-fault drivers that contribute to crash rates and crash severity may lead to improved and more specific interventions to reduce the negative impacts of motor vehicle crashes. The driver-residence zip code was used as a spatial unit to connect five years of Kentucky crash data with socioeconomic factors from the U.S. Census, such as income, employment, education, age, and others, along with terrain and vehicle age. At-fault driver crash counts, normalized over the driving population, were used as the dependent variable in a multivariate linear regression to model socioeconomic variables and their relationship with motor vehicle crashes. The final model consisted of nine socioeconomic and demographic variables and resulted in a R-square of 0.279, which indicates linear correlation but a lack of strong predicting power. The model resulted in both positive and negative correlations of socioeconomic variables with crash rates. Positive associations were found with the terrain index (a composite measure of road curviness), travel time, high school graduation and vehicle age. Negative associations were found with younger drivers, unemployment, college education, and terrain difference, which considers the terrain index at the driver residence and crash location. Further research seems to be warranted to fully understand the role that socioeconomic and demographic characteristics play in driving behavior and crash risk

Effect of Socioeconomic Factors on Kentucky Truck Driver Crashes

Kentucky crash data for the 2015-2016 period reveal that per capita crash rates and increases in crash-related fatalities in the state outpaced the national average. To explain why the U.S. Southeast sees higher crash rates than other regions of the country, previous research has argued the region’s unique socioeconomic conditions provide a compelling explanation. Taking this observation as a starting point, this study uses an extensive crash dataset from Kentucky to examine the relationship between highway safety and socioeconomic and demographic characteristics. 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

Effect of Socioeconomic Factors on Crash Occurrence

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