Effect of Socioeconomic and Demographic Factors on Crash Occurrence

Abstract

The number of crashes and fatalities rate in the Commonwealth of Kentucky, U.S., have been higher than the national average for the past decades. It has been hypothesized that the distinct socioeconomic conditions of the state could be contributing to and explaining these trends. This study investigated the influence of socioeconomic characteristics on highway safety in Kentucky and attempted to identify the high-risk driver groups, based on crash data and the socioeconomic and demographic features of their residence zip codes. The quasi-induced exposure technique and binary logistic regression were employed to develop a predictive modeling approach for determining the probability of being the at-fault driver in a single- and two-unit crashes, based on socioeconomic characteristics of the driver residence zip code. The study identified that socioeconomic features such as income, poverty level, employment, age, gender, rurality, and number of traffic-related convictions of a driver’s zip code influence their likelihood to be at fault in a two-unit crash, while for single-unit crashes, in addition to these variables, educational attainment had also an impact. Younger and older drivers living in zip codes with low socioeconomic conditions have a higher probability to be the at-fault driver in both single- and two-unit crashes. The conclusions of the study can be used to determine the regions (zip codes) and driver groups with higher likelihood to be the at-fault driver in a crash and develop effective safety programs for the target groups.

GIS techniques to analyze factors associated with crash occurrence

“…Regardless of age and gender, drivers in low-income counties have higher crash involvement, which concur to the findings the previous research (Sagar et al, 2021). Moreover, since most of the Appalachian counites are low income counties, drivers in these areas are more likely to cause crashes.…”

“…Hence, it is more reasonable to examine the socioeconomic characteristics of the driver to address this lack of information (Lee et al, 2014;Chandraratna et al, 2005;Blatt & Furman, 1998;Cooper et al, 1995). To gain such an insight, the socioeconomic characteristics associated with the residence of the drivers involved in the crash rather than examining only the location characteristics are considered (Sagar et al, 2021;Chen et al, 2010). This is more salient since the drivers involved are more likely to come from a different zone than that of the crash location.…”

Socioeconomic and Demographic Factors Effect in Association with Driver’s Medical Services after Crashes

“…Deaths per 100 million vehicle miles traveled (VMT) in Kentucky have exceeded the national average since 1986 [ 1 , 2 ]. Previous research has demonstrated that socioeconomic and demographic characteristics of the zip codes in which drivers reside influence their involvement in crashes [ 3 , 4 ]. Past research investigated the effect of socioeconomic and demographic factors associated with the residence location of the drivers involved in a crash, using them as a surrogate descriptor [ 3 , 4 , 5 ] and using the socioeconomic data from the drivers’ residence zip codes [ 6 ].…”

“…Previous research has demonstrated that socioeconomic and demographic characteristics of the zip codes in which drivers reside influence their involvement in crashes [ 3 , 4 ]. Past research investigated the effect of socioeconomic and demographic factors associated with the residence location of the drivers involved in a crash, using them as a surrogate descriptor [ 3 , 4 , 5 ] and using the socioeconomic data from the drivers’ residence zip codes [ 6 ]. Income, poverty, employment, education, rurality, and driver age all appear to influence crash propensity [ 4 , 7 , 8 , 9 , 10 , 11 ].…”

“…Past research investigated the effect of socioeconomic and demographic factors associated with the residence location of the drivers involved in a crash, using them as a surrogate descriptor [ 3 , 4 , 5 ] and using the socioeconomic data from the drivers’ residence zip codes [ 6 ]. Income, poverty, employment, education, rurality, and driver age all appear to influence crash propensity [ 4 , 7 , 8 , 9 , 10 , 11 ]. Previous research showed a well-defined relationship between educational attainment and crashes [ 3 , 7 , 12 ].…”