# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 2 Related Work  
Prediction of traffic sign functional service life and factors mainly responsible for retro-reflectivity deterioration are crucial for planned maintenance schemes to ensure a satisfactory level of service. In the past, several studies were initiated to explore factors contributing to sign retro-reflectivity. For example, [PERSON] et al. were pioneers in investigating the effect of factors such as signs elevation, orientation, solar radiation levels, temperature,precipitation, and age on sign retro-reflection [6]. A total of over 5000 signs were surveyed. It was reported that precipitation, sign age, elevation, and the temperature had a strong positive association with sign degradation patterns while there was no statistical evidence for the effect of sign direction on the same. However, this study does have few limitations. The majority of sign samples surveyed were of ASTM Type-I sheeting. The Sample size of type-III sheeting with an age greater than 15 years was low. Moreover, the developed deterioration models had \(R^{2}\) values below 0.5, indicating that age could not explain the variability in sign degradation. [PERSON] et al. conducted a study to analyze factors affecting sign retro-reflection [5]. The authors noted that a sign's age did not cause significant reduction in retro-reflectivity, whereas orientation (sign direction) was identified as a significant predictor variable. [PERSON] et al. conducted a study to analyze the degradation of retro-reflectivity of different sign sheeting [7]. The authors developed 12 models to predict the functional service life of traffic sign according to their retro-reflectivity. The 12 models were proposed for 2 sheeting types, 3 colors, and the conditions for whether signs were cleaned or not. It was found that from the three variables (age, orientation, and the distance between the sign and the road), only age has a positive correlation with the degradation of retro-reflectivity. In another study, [PERSON] and [PERSON] collected data for 1341 in-service traffic signs to examine retro-reflection degradation trends of white, yellow, and red-yellow colored signs [8]. Study findings showed that white and yellow signs had an average service life of 12 years, while red sign sheeting on average had 10 years of service life. The study further reported that sign orientation has a weak correlation with retro-reflectivity. This study collected three measurements on each sign, whereas the ASTM standard procedure requires four measurements. [PERSON] et al. investigated the role of age in the degradation of sign retro-reflectivity for 1000 service signs surveyed in the state of North Carolina, USA [9]. Different types of regression models were developed. \(R^{2}\) for the majority of models were less than 0.5, indicating that there may be other factors such as exposure, temperature, weather conditions, and air pollution that can significantly influence the rate of deterioration. In their study, [PERSON] et al. proposed a series of mathematical models (linear mathematical model, second-order polynomial model, and cubic regression models) to predict the retro-reflectivity deterioration pattern of ASTM Type-I, Type-II, and Type-III sheeting deployed along the Jiangxi Yongwu expressway in China [10]. Results showed that both quadratic and cubic regression models were better predictors of sign retro-reflectivity. This study considered only two sheeting types and three colors. A lower sample size (\(N=230\)) and low model predictions are the main issues of this study. Over the years, different methods were proposed to forecast sign retro-reflectivity as a function of explanatory variables such as sign aging, sheeting type, sign sheeting orientation, temperature, precipitation, dew, etc. [1, 7, 11, 12, 13]. In his thesis, [PERSON] compared the performance of the existing regression models and a newly developed neural network model in predicting the retro-reflectivity for Type-I and Type-II sign sheeting [12].