Proper maintenance of road traffic signs is vital for safety, as their low visibility can cause accidents and fatalities. Many countries, including Sweden, lack a systematic approach for replacing signs due to the risky, costly, and complex methods needed to measure their color and retroreflectivity.
This thesis introduces a predictive maintenance method for road traffic signs to ensure their visibility day and night. The proposed data-driven models predict sign degradation, helping maintain optimal visibility, decreasing accidents, and enhancing safety, and environmental sustainability by reducing material consumption and waste reduction.
This thesis suggests using machine learning methods to predict the values of retroreflectivity (coefficient of retroreflection) and color (daylight chromaticity), and to estimate the status (rejected/accepted) and longevity according to color and retroreflectivity. Datasets collected in Sweden, Denmark, and Croatia were used in this research.
Regression and classification models, employing Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) utilized to predict the degradation of road traffic signs. ANN showed the highest performance, 94% R2 for retroreflectivity predictions and up to 94% accuracy for color and retroreflectivity status. SVM and RF also achieved acceptable accuracies.
Statistical methods, including linear and logarithmic regression, were also applied to examine the impact of age on the retroreflectivity values and status, chromaticity, and color status of road traffic signs. Findings revealed age as a significant factor, with a generally linear relationship between chromaticity values and age, except for yellow signs which displayed non-linear patterns between 8 and 22 years. Logarithmic regression models achieved R2 values of 50% and 95%, which are more accurate than those from previous studies. These models reveal an annual decrease in retroreflectivity of 4-5% and a negative correlation with the sign's direction, indicating that signs facing south and west degrade faster due to more solar exposure.
Logistic regression and Kaplan-Meier survival analyses were used to assess road traffic signs' longevity. The longevity based on retroreflectivity and color durability varies depending on color, retroreflective sheeting classes, direction, and location.
In Sweden, the median lifespan of road traffic signs estimated based on retroreflectivity lasts up to 25 years for red, 20 for yellow, 20 for white, and 35 for blue sheeting. In Croatia, the lifespan is shorter, 12 years for red, 16 for yellow, and 17 for white, 20 for blue.
Considering color degradation, the median lifespan of yellow road traffic signs is 45 years, 35 years for white, and blue signs, while red signs have a shorter lifespan. However, the red signs deteriorate in color before retroreflectivity with a median lifespan of 16 years, whereas other signs maintain their color longer. This emphasizes the effect of factors like pigment choice and environmental conditions on the durability of road traffic signs.