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 RA 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, RA 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.

An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs

“…In a 2021 study, Alkhulaifi 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.…”

“…Previous studies have suggested prediction models based on two features (sheeting type and color). Most of these studies focused on age as the only predictor for retroreflectivity performance, with Alkhulaifi [9], who considered color and observation angle, in The coefficient of retroreflection can be measured using retroreflectometers, while spectrophotometers are used for measuring colors. In the field, measurements can be recorded using either handheld or mobile equipment.…”

“…Previous studies have suggested prediction models based on two features (sheeting type and color). Most of these studies focused on age as the only predictor for retroreflectivity performance, with Alkhulaifi [9], who considered color and observation angle, in addition to age and sheeting type, being an exception. Therefore, the accuracy of the models in these studies was low.…”

Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

“…ML and DL have found various applications in diverse fields such as face recognition and tracking [2], visual tracking [3,4], vision and language navigation [5][6][7], and image and video editing [8][9][10]. In recent years, application of such soft computing methodologies has received widespread applications for various civil and transportation engineering-related problems, including road safety [11][12][13][14], mode choice modeling [15], energy demand modeling for electric vehicles [16][17][18], and traffic sign detection and recognition [19,20]. Similarly, applications of these predictive modeling approaches are reshaping the field of pavement evaluation and management.…”

The Basic Process of Lighting as Key Factor in the Transition towards More Sustainable Urban Environments

“…However, the aforementioned standards do not give designers many opportunities to influence these requirements: while the criteria for motorized roads (class M) include the "navigational task difficulty" criterion (albeit with limited impact on the lighting class which generally pertains to driving rather than walking), the criteria for pedestrian and low-speed areas (class P, which is applicable in such situations) are not applicable in this specific case [19]. The same happens with the coexistence of public lighting and groves in urban environments [20][21][22], and also with different kinds of traffic signals [23,24]. Both are systematically ignored by rulemaking bodies and, rather frequently, by designers.…”