Abstract
The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs.
Highlights
Road traffic signs are fundamental tools to regulate traffic and provide clear and important information to road users [1,2]
Road traffic signs reflect the light coming from vehicles to the road users so that they can see these signs [4,5]
The predictors included were the age of the road traffic sign, its GPS position, and the direction of the sign, as well as the color and class of the retroreflective sheeting
Summary
Road traffic signs are fundamental tools to regulate traffic and provide clear and important information to road users [1,2]. Efforts have been made to find and evaluate models that include only the predictors that can be collected without the need for any expensive measuring instruments For this reason, the predictors included were the age of the road traffic sign, its GPS position, and the direction of the sign, as well as the color and class of the retroreflective sheeting. One other important issue in this paper was investigating the significance of the chromaticity, luminance factor, and GPS positions on the retroreflectivity of road traffic signs. The study proposes a way to analyze and calculate the performance and deterioration of retroreflectivity using factors that can be collected without using expensive instruments Such factors include the traffic sign’s color, age, class, GPS position, and direction. The limitations, suggestions for further research, and conclusions are presented in Sections 5 and 6
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