Pavement Marking Retroreflectivity: Exploring Degradation Factors and Relationship with Road Safety

Abstract

The evaluation of pavement marking materials is primarily based on their retroreflectivity (RL), which refers to how well they reflect vehicle headlights back toward the driver. This study focuses on rural roadways and thermoplastic markings to i) identify key factors contributing to RL degradation, ii) understand the relationship between subjective ratings and measured RL by marking line types, iii) develop RL prediction models without initial RL and marking age information as inputs, and iv) explore the statistical relationship between RL and road crashes. Two years of RL data (2020 and 2021) were collected from ALDOT for the Montgomery area. The associated traffic flow, road, location, and crash information were extracted from six different data sources. Beta regression, multiple linear regression, and binary logit models were employed to uncover statistically significant relationships. The results showed that the effects of factors contributing to RL degradation vary by marking line type. For example, yellow centerlines in residential, business, and mixed-use areas exhibited statistically significant RL degradation. RL degradation for white right edgeline (WREL) was significantly higher on curve segments compared to adjacent straight segments. In addition, the analyses found evidence supporting the hypothesis that officers may struggle to assign accurate subjective ratings, particularly for yellow markings. Moreover, this research demonstrated an effective approach for examining the statistical relationship between RL and road crashes by increasing the sample size through developed RL prediction models. The safety analyses identified that lower RL levels of WREL (below 250 mcd/m²/lux) statistically increase the likelihood of single-vehicle run-off-road (ROR) crashes on curve segments and in dark conditions. The findings can help ALDOT's pavement management system in identifying locations with high RL degradation, enabling more targeted restriping at these specific segments. Moreover, the developed RL prediction models offer local transportation agencies a useful tool for forecasting RL with only one year RL measurement and associated traffic flow information. The results from crash data analyses highlight the importance of maintaining adequate RL levels for highway safety. Overall, the findings of this research align with one of the core principles of the Safe System Approach: ‘Making Our Roads Safer’.