Pavement markings provide useful visual and navigational guidance information to motorists. Current warrants for the application of pavement markings in the United States are based on traffic volume, traveled way width, and number of travel lanes. To be effective, pavement markings must be visible to drivers, particularly at night. The purpose of this paper is to perform an exploratory analysis to determine if a relationship between pavement marking retroreflectivity and crash frequency exists. First, models of pavement marking retroreflectivity degradation were developed from selected highways in North Carolina using artificial neural networks. Monthly estimates of pavement marking retro-reflectivity levels were then appended to roadway inventory and crash frequency data. Generalized estimating equations were used to estimate the monthly target crash frequency. The results indicate that the regression parameter estimates for yellow and white edgeline pavement markings were negative, but neither was statistically significant for the two-lane highway nighttime target crash frequency model. For multilane highways, all of the pavement marking retro-reflectivity parameter estimates were statistically significant. The white pavement marking retroreflectivity parameter estimates were negative, as expected. The yellow pavement marking retroreflectivity parameter estimate was positive.
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