Model for Predicting Traffic Signs Functional Service Life – The Republic of Croatia Case Study

Babić, Dario; Babić, Darko; Macura, Dragana · 2017 · Crossref

DOI: 10.7307/ptt.v29i3.2247

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Summary

This study addresses the need for accurate prediction models to estimate the functional service life of traffic signs, aiming to optimize maintenance activities and enhance road safety. Traffic signs rely on retroreflective properties to remain visible to drivers, particularly in low-light conditions. As these properties degrade over time, signs must be replaced when they fall below minimum prescribed retroreflection coefficients. The authors note that previous research often suffered from poor model accuracy due to factors such as varying sign ages, material changes, and the exclusion of replaced signs from datasets. This paper seeks to overcome these limitations by developing robust predictive models based on a consistent dataset from the Republic of Croatia. The research utilized data collected from the City of Zagreb between 2008 and 2016. The study focused on traffic signs manufactured by a single producer and installed simultaneously in 2008, ensuring a controlled age distribution. The authors measured the retroreflection coefficient ($R_A$) of retroreflective materials classified as Class I (white, red, blue), Class II (white, red, blue), and Class III (red, yellow). Measurements were conducted annually using a handheld retroreflectometer (Zehntner ZRS 6060) compliant with European Standard EN 12899-1. Each sign and color was measured four times, with signs cleaned prior to testing to account for dirt accumulation. The analysis treated retroreflection degradation as a function of sign age, excluding external variables like weather or orientation, which prior studies found to be less significant or difficult to isolate. The results indicate a strong negative correlation between sign age and retroreflection performance. The authors developed linear, logarithmic, and exponential regression models for each material class and color. Linear and exponential models demonstrated the highest accuracy, with an average coefficient of determination ($R^2$) of 0.57 and 0.56, respectively. These values represent a significant improvement over previous studies, which often reported $R^2$ values below 0.50. The analysis revealed that red-colored signs are the most time-sensitive, degrading faster than other colors and becoming non-compliant first. Survival analysis further quantified the probability of signs remaining compliant over time. The models effectively predict when specific signs will fall below regulatory thresholds, allowing for precise estimation of functional service life. The significance of this work lies in providing road authorities with a reliable tool for planning traffic sign maintenance. By accurately forecasting when retroreflection levels will drop below minimum standards, agencies can schedule replacements proactively rather than reactively. This optimization reduces waste and ensures that traffic signs consistently meet safety requirements, thereby supporting safer driving conditions. The study confirms that age is the primary driver of retroreflection degradation and that linear and exponential models are the most effective methods for predicting this decline within the specific context of Croatian traffic infrastructure.

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Topics

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• sign visibility legibility
• signage environment
• vehicle conspicuity