# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
###### Abstract  
Traffic signs are essential for the safe and efficient movement of vehicles through the transportation network. Poor sign visibility can lead to accidents. One of the key properties used to measure the visibility of a traffic sign is retro-reflection, which indicates how much light a traffic sign reflects back to the driver. The retro-reflection of the traffic sign degrades over time until it reaches a point where the traffic sign has to be changed or repaired. Several studies have explored the idea of modeling the sign degradation level to help the authorities in effective scheduling of sign maintenance. However, previous studies utilized simpler models and proposed multiple models for different combinations of the sheeting type and color used for the traffic sign. In this study, we present a neural network based deep learning model for traffic sign retro-reflectivity prediction. Data utilized in this study was collected using a handheld retro-reflectometer GR3 from field surveys of traffic signs. Sign retro-reflective measurements (i.e., the \(R_{A}\) values) were taken for different sign sheeting brands, grades, colors, orientation angles, observation angles, and aging periods. Feature-based sensitivity analysis was conducted to identify variables' relative importance in determining retro-reflectivity. Results show that the sheeting color and observation angle were the most significant variables, whereas sign orientation was the least important. Considering all the features, \(R_{A}\) prediction results obtained from one-hot encoding outperformed other models reported in the literature. The findings of this study demonstrate the feasibility and robustness of the proposed neural network based deep learning model in predicting the sign retro-reflectivity. t 2021 11 11595 11.11.11595 11.  
account for over 90% of total crashes [3]. To ensure the safety of drivers and vehicles, the local department of transportation (DOT) must have periodic traffic sign evaluation and replacement programs [4]. Evaluation of sign retro-reflection is one of the key strategies in this regard. It is measured by an index that is commonly known as \"retro-reflectivity\" or \"coefficient of retro-reflectivity\", which is expressed in units of candela per lux per square meter (cd/lx/m\({}^{2}\)). Retro-reflectivity of traffic signs' sheeting gradually deteriorates over time, thus making signs progressively less visible at night [5]. Highway agencies are using a variety of sign sheeting materials that provide greater luminance with significantly improved results. Degradation of sign retro-reflectivity has been the focus of several of the previous studies (e.g., [5; 6; 7]). In previous works, simple regression models were explored and no advanced machine learning techniques were sought for predicting sign retro-reflectivity degradation. The predictive performance of the traditional regression model in this regard is also typically low, with one of the highest accuracies obtained by a study conducted by [1]. The weaker prediction performance of the statistical regression-based methods may be attributed to assumptions regarding predefined associations between variables and the linear form of the utility function, which may not necessarily hold for most real-world problems. Frequently, regression-based statistical models are unable to capture latent correlations among the predictor variables. Additionally, in previous studies, researchers have proposed models that are capable of predicting sign retro-reflectivity for a specific combination of sign sheeting grade and color. To the best of our knowledge, no study has incorporated sign retro-reflectivity deterioration for different sign attributes and aging conditions into one model. In this paper, we present a deep learning based regression model that is capable of predicting sign retro-reflection as a function of sheeting type/grade, sheeting brand (the manufacturer of the sheeting), sheeting color, observation angle, orientation (sign facing) and age.