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Traffic crashes are one of the most severe problems that affect communities worldwide. According to the World Health Organization, the annual number of global fatalities due to traffic crashes is about 1.35 million. Many factors affect the likelihood and the severity of a crash. In this paper, different factors are studied to evaluate their impact on the frequency, the severity, and the type of crashes at intersections, in order to boost road safety. California crash data have been obtained from the Highway Safety and Information System (HSIS) database and have been analyzed statistically. The analysis has included several factors such as severity, type of intersection, and the characteristics of the driver, roadway, environment, and vehicle. A negative binomial regression has been conducted by using the Statistical Analysis System (SAS®) Software for building three models. As a result, different parameters have been considered significant factors in each model. From all the models, the type of intersection and collision, the area of the crash, the type of surface type and its condition, the number of lanes, the age, and the gender have been considered significant factors for crashes at intersections. The result of this study could help reduce the likelihood and severity of crashes. [ABSTRACT FROM AUTHOR]