Traffic Sign Retroreflectivity Condition Assessment and Deterioration Analysis Using Lidar Technology

Abstract

Traffic sign retroreflectivity is critical for nighttime visibility, an important factor in driver safety. Current methods of sign retroreflectivity assessment are expensive, time-consuming, dangerous, or subjective. There is an urgent need to explore an alternative method that is cost-effective, safe, objective, and can be operated during daytime or nighttime. One such method is mobile lidar. However, a methodology utilizing lidar cloud data for practical retroreflectivity condition assessment is still lacking because of the inability to numerically correlate lidar retro-intensity readings to the retroreflectivity standard set by the Manual on Uniform Traffic Control Devices (MUTCD). In addition, there is also a need to explore sign deterioration behavior using real-world lidar data. In this study, we (1) propose a practical, categorical traffic sign condition assessment using lidar data; (2) establish a preliminary correlation between the retro-intensity and retroreflectivity readings to determine the minimum retro-intensity thresholds for condition assessment of different sheeting types and colors; (3) validate the accuracy of the assessment by comparing it with standard nighttime visual inspection outcomes; (4) demonstrate the practical implementation through a feasibility study at Georgia Interstate 285; and (5) reveal the retro-intensity deterioration trends using historical lidar cloud data. The results show that the proposed methodology can reliably yield results comparable to manual measurements, potentially reducing sign retroreflectivity condition assessment effort, increasing the transportation agency’s productivity, and filling gaps where manual assessment is not possible. Additionally, the retro-intensity deterioration trends can help transportation agencies to understand the long-term behavior of sign retro-intensity and predict the optimal timing for sign replacement.