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Abstract

Pavement marking retroreflectivity (RL) is a critical characteristic for enhancing road safety and infrastructure compatibility with advanced driver assistance systems. This study investigates factors contributing to RL degradation of thermoplastic markings, focusing on marking line types and rural lane configurations. RL data from the Alabama Department of Transportation (DOT) were analyzed for the Montgomery area in 2020 and 2021. Data from six sources, including the Topologically Integrated Geographic Encoding and Referencing System, Highway Performance Monitoring System, and Critical Analysis Reporting Environment, were used to extract relevant factors featuring road, traffic flow, location, and marking characteristics. Apart from beta regression, paired t-tests were performed to examine RL degradation, particularly statistical variations between curve and adjacent straight segments. Moreover, over 200 locations were manually reviewed using Google Earth Pro Street View to identify additional contributing factors contributed to high RL degradation, not apparent through statistical analysis. The results revealed that centerlines in residential, business, or mixed-use areas experienced greater RL degradation. White lanelines showed statistically significant RL degradation on roadways with high posted speed limits. White right edgelines exhibited higher degradation on curved compared with straight segments with similar road and traffic characteristics and identical RL qualitative states. Additional factors influencing RL degradation included the presence of right-turn lanes near intersections or ramps, U-turn and left-turn facilities, bridges, shoulders, residential driveways, and surrounding landscape features. These findings can support pavement management system in identifying high-priority segments for restriping, optimizing maintenance efforts, and reducing costs.

Keywords

retroreflectivity degradation factors regression models curve segments pavement management

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Exploring Factors Contributing to Retroreflectivity Degradation of Longitudinal Pavement Markings: A Systematic Approach Beyond Regression Modeling

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