Modeling Retroreflectivity Performance of Thermoplastic Pavement Markings in Alabama

Abstract

Pavement markings play an important role in the roadway system because they provide information for drivers to follow the road. Markings must be replaced when their retroreflectivity falls below an acceptable level. The rate of degradation of marking retroreflectivity can be influenced by many factors, such as type of material and traffic volume. The efficiency of pavement marking maintenance programs could be improved with statistical models that estimate the degradation of retroreflectivity over time. A case study based on data collected by the Alabama DOT was undertaken to address these issues. The purpose of the research reported in this paper was to observe how existing models fit the data and develop new statistical models that could predict retroreflectivity over time for Alabama conditions. Modeling of retroreflectivity performance over time for thermoplastic markings was executed for 15 projects that had measurements of retroreflectivity for the same locations in years 2007–2010. The modeling of retroreflectivity considered initial retroreflectivity, age, and annual average daily traffic as candidate independent variables. A linear model well-represented the data in Alabama and variable selection methods showed that initial retroreflectivity is not significant for retroreflectivity estimation. The models developed in the research reported in this paper perform better than most of the models found in the literature as shown by a comparison of correlation coefficient values. The linear model based on age and traffic volume, with correlation coefficient values of 0.448 and 0.488 for white and yellow markings, respectively, is recommended for prediction of future retroreflectivity performance and scheduling of maintenance activities.