Prediction of traffic sign vandalism that obstructs critical messages to drivers

Khalilikhah, Majid; Heaslip, Kevin

doi:10.3846/16484142.2016.1252946

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Khalilikhah and Heaslip utilized Random Forest (RF) to predict traffic sign vandalism as the function of several predictor variables such as mount height, size, background color, exposure, land cover, and road type [21]. Chi-square test results showed that there was a significant association between all the sign attributes and vandalism.…”

Section: Related Workmentioning

confidence: 99%

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

2021

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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.

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Section: Related Workmentioning

confidence: 99%

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

2021

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The effectiveness of information signs in deterring visitor vandalism

Yang

2024

Journal of Outdoor Recreation and Tourism

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Psychological specificity of the representation of vandalism of traffic signs in Russian megalopolises

Kruzhkova

Воробьева

2022

Applied Psychology and Pedagogy

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Vandalism of traffic signs is a phenomenon that arose in the 20th century and is not only in the stage of its formation, but also has no comprehensive scientific justification today, both from the standpoint of the determinants of occurrence, and from the point of view of its likely consequences for society. Vandal damage of traffic signs is varied – from tagging and sticking with stickers, to deformation of the sign or its dismantling and theft. At the same time, in the works of researchers, a decrease in the ability of a vandal-damaged sign to effectively regulate traffic is noted, which can lead to potentially dangerous situations on the road. Despite the significance of the problem, empirical studies of vandalism of traffic signs were carried out mainly in the United States, the experience of which cannot be extrapolated to Russian reality due to cultural, economic and social reasons. Thus, the purpose of the study presented in the article was to identify the specifics of vandalism of traffic signs in the conditions of Russian megalopolises. In the period of 2021, empirical material was collected using the method of photographing vandally damaged traffic signs in three megalopolises of the Russian Federation (Vladivostok, Ekaterinburg, St. Petersburg), carried out as part of the route photographing of vandal transformations of the urban environment. For the analysis, 63 images of vandal-damaged traffic signs were selected, which were later evaluated by experts. It was found that the most frequently vandalized information signs and signs of additional information, which have a white background and a rectangular shape, acting as an actual "canvas" for vandals. Tagging and pasting with stickers (70.4% and 59.2% of cases of vandal damaged signs, respectively) become typical damage to signs. The study of vandalism of traffic signs, its causes, the degree of threat to road safety, the possibility of preventing illegitimate transformations requires additional research, which is especially relevant with the growing trend towards vandal transformation of road signs in the modern conditions of Russian megalopolises. The study was supported by a grant from the Russian Science Foundation (project no. 17-18-01278).

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Prediction of traffic sign vandalism that obstructs critical messages to drivers

Cited by 5 publications

References 18 publications

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks

The effectiveness of information signs in deterring visitor vandalism

Psychological specificity of the representation of vandalism of traffic signs in Russian megalopolises

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