Investigating the effect of vehicle color on crash risk

Date of Award


Degree Name

M.S. in Civil Engineering


Department of Civil and Environmental Engineering and Engineering Mechanics


Advisor: Deogratias Eustace


Many studies have been conducted to understand the root causes of traffic crashes so as to put in place measures to prevent them. Most of the studies have attributed vehicle crashes to careless driving, un-roadworthy vehicles, poor road conditions, drunk driving, driving under the influence of banned substances and speeding. Nevertheless, few researchers have shown curious about the relationship between traffic crashes and vehicle color. This thesis study sought to establish whether there is a relationship between vehicle color and a risk of a crash and if there is a relationship, which vehicle color is the safest? Data used in this study were obtained from the Ohio Department of Public Safety (ODPS); these are traffic crashes that occurred in the state of Ohio from 2011 to 2015. The induced exposure method was used where the data were divided into two groups: Color prone group, which puts together different types of crashes where the visibility of a vehicle in terms of color may contribute to the occurrence of a crash. Induced exposure group, this group includes vehicle crashes that occur for other reasons other than vehicle visibility such as vehicles hitting a tree or other fixed objects or overturning. These are single-vehicle crashes. Both the negative binomial (NB) and Poisson distributions were used to fit generalized linear models to the data. Model goodness-of-fit tests were utilized to check which model fits better to the data. Model goodness-of-fit tests indicate that the NB model reflected a better fit to the data due to over-dispersion. Results from the negative binomial model confirm that statistically, besides random variations, no vehicle color was found to be safer or riskier than white, the vehicle color used as a baseline color.


Traffic accidents Statistics, Traffic accidents Forecasting, Vehicles Color, Civil Engineering, Transportation

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