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Abstract

The purpose of this article was to develop an accident prediction model for motor vehicle crashes occurring within Miami-Dade County, Florida during 2008-2011 Motor vehicle crash data were extracted from the Florida Department of Motor Vehicle and Highway Safety dataset for 40 intersections within Miami-Dade County, Florida for development of an accident prediction model. Each intersection was matched at least one of 20 red light camera (RLC) sites using selected geometric variables. In addition, each intersection examined was at least 2 miles away from any RLC site. The dependent variable examined was the number of injury crashes occurring at each intersection between 2008 and 2011. Poisson, negative binomial, and gamma model distributions were compared using the Pearson's chi square ( ) 2 χ , scaled deviance (G 2 ), and Akaike Information Criterion (AIC) goodness of fit tests. Our analysis indicated that the negative binomial distribution was the best fit among the three models. Inspection of the observed data also suggested that the outcome variable's distribution was over dispersed. This study provided guidance on the use of goodness of fit testing (GOF) statistics for Poisson, negative binomial, and gamma models which will allow other researchers to evaluate different models.