## 2 Systematic Literature Review  
A systematic review was conducted in this paper, with Google Scholar used to identify peer-reviewed articles. The keywords used in the literature search were \"retroreflectivity performance\", \"road traffic signs\", \"regression models\", \"retroreflective sheeting\", and \"aging\". Additionally, the synonyms of these keywords were used in the search. A total of 192 articles were collected from 2000 to 2021 but after screening their titles and abstracts, only 17 articles were related. The issue of a controlled study for sign deterioration was addressed in 1983 by [PERSON] et al., who studied the deterioration of road signs in New York [10]. Retroreflectivity deterioration is an interactive process between many factors [6]. The main factors that affect retroreflectivity deterioration were identified by different researchers and were summarized in [6]. Among the important factors were the geographic area, weather conditions, sheeting type, sheeting age, sheeting color, and sunlight exposure [6; 11]. Many researchers identified age as being the most significant factor that affects retroreflectivity deterioration, as shown in Table 1. The orientation of the sheeting in relation to the sun and the distance of the sign from the edge of the pavement were found to be insignificant [6; 12; 13]. Although many studies aiming to understand traffic sign retroreflection degradation have been conducted in the past three decades [1], only a few researchers have developed models that can accurately predict retroreflectivity performance and the durability of sign sheeting. In a 2017 study, developed regression models showed an average coefficient of determination (R-square) of 0.57 for linear models, 0.52 for logarithmic models, and 0.56 for exponential models [1]. Regression models in earlier studies produced R-squared values that were relatively low, in the range of 0.10 to 0.30 [12]. [PERSON] [6] compared the performance of multi-linear regression models with artificial neural network models (NN) used to predict the reflectivity of sign sheeting. The NN models predicted the retroreflectivity of signs closer to the in-field values for eight out of ten models, while two regression models estimated the reflectivity values closer to the in-field values than did the NN models. Even the other regression models produced nearly the same accuracy as the NN models. Generally, NN models are more competitively expensive than multi-linear regression models. In a 2021 study, [PERSON] et al. [9] used regression models and neural networks based on deep learning models. The neural network with one-hot encoding achieved the best results with an R\({}^{2}\) value of 0.976, while polynomial regression models achieved an R\({}^{2}\) of 0.759. Of all the sign types and colors studied in the past, attention has been paid to seven factors that are used as predictors for the retroreflectivity performance of road traffic signs. These factors are: sheeting type, sheeting age, sheeting color, the orientation of the sign concerning the sun, region, observation angle, and the distance from the edge of the pavement. Previously, only five factors--age, color, observation angle, sheeting type, and sign orientation--were included as predictors for retroreflectivity performance [1; 14]. The developed regression models in previous studies showed relatively low R-squared values, with the suggested models needing improvement to achieve an accurate method of predicting the retroreflectivity performance of road traffic signs. For example, sheeting type, sheeting color, region, and distance from the edge of the pavement can be used as predictors, since including more predictors gives better prediction accuracy [6] and increases the goodness of fit of the models. Although the state of the art found that the color of the retroreflective sheeting has a big effect on the deterioration of road traffic signs, no studies have evaluated the significance of chromaticity of the color (X and Y coordinates) on retroreflectivity.