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An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs
Dalarna University, School of Information and Engineering, Microdata Analysis. Swedish Transport Administration, Borlänge.
Dalarna University, School of Information and Engineering, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Information and Engineering, Statistics.ORCID iD: 0000-0002-3183-3756
2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 5, article id 2413Article in journal (Refereed) Published
Abstract [en]

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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
2022. Vol. 12, no 5, article id 2413
Keywords [en]
Linear regression, Retroreflective sheeting material, Road traffic sign
National Category
Computer Systems Signal Processing Infrastructure Engineering
Identifiers
URN: urn:nbn:se:du-39844DOI: 10.3390/app12052413ISI: 000926968600001Scopus ID: 2-s2.0-85125768375OAI: oai:DiVA.org:du-39844DiVA, id: diva2:1644332
Available from: 2022-03-14 Created: 2022-03-14 Last updated: 2024-04-22Bibliographically approved
In thesis
1. Towards Smart Maintenance: Machine-Learning Based Prediction of Retroreflectivity and Color of Road Traffic Signs
Open this publication in new window or tab >>Towards Smart Maintenance: Machine-Learning Based Prediction of Retroreflectivity and Color of Road Traffic Signs
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proper maintenance of road traffic signs is vital for safety, as their low visibility can cause accidents and fatalities. Many countries, including Sweden, lack a systematic approach for replacing signs due to the risky, costly, and complex methods needed to measure their color and retroreflectivity.

This thesis introduces a predictive maintenance method for road traffic signs to ensure their visibility day and night. The proposed data-driven models predict sign degradation, helping maintain optimal visibility, decreasing accidents, and enhancing safety, and environmental sustainability by reducing material consumption and waste reduction.

This thesis suggests using machine learning methods to predict the values of retroreflectivity (coefficient of retroreflection) and color (daylight chromaticity), and to estimate the status (rejected/accepted) and longevity according to color and retroreflectivity. Datasets collected in Sweden, Denmark, and Croatia were used in this research.

Regression and classification models, employing Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) utilized to predict the degradation of road traffic signs. ANN showed the highest performance, 94% R2 for retroreflectivity predictions and up to 94% accuracy for color and retroreflectivity status. SVM and RF also achieved acceptable accuracies.

Statistical methods, including linear and logarithmic regression, were also applied to examine the impact of age on the retroreflectivity values and status, chromaticity, and color status of road traffic signs. Findings revealed age as a significant factor, with a generally linear relationship between chromaticity values and age, except for yellow signs which displayed non-linear patterns between 8 and 22 years. Logarithmic regression models achieved R2 values of 50% and 95%, which are more accurate than those from previous studies. These models reveal an annual decrease in retroreflectivity of 4-5% and a negative correlation with the sign's direction, indicating that signs facing south and west degrade faster due to more solar exposure.

Logistic regression and Kaplan-Meier survival analyses were used to assess road traffic signs' longevity. The longevity based on retroreflectivity and color durability varies depending on color, retroreflective sheeting classes, direction, and location.

In Sweden, the median lifespan of road traffic signs estimated based on retroreflectivity lasts up to 25 years for red, 20 for yellow, 20 for white, and 35 for blue sheeting. In Croatia, the lifespan is shorter, 12 years for red, 16 for yellow, and 17 for white, 20 for blue.

Considering color degradation, the median lifespan of yellow road traffic signs is 45 years, 35 years for white, and blue signs, while red signs have a shorter lifespan. However, the red signs deteriorate in color before retroreflectivity with a median lifespan of 16 years, whereas other signs maintain their color longer. This emphasizes the effect of factors like pigment choice and environmental conditions on the durability of road traffic signs.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2024
Series
Dalarna Doctoral Dissertations ; 32
Keywords
Road traffic signs, Retroreflectivity, Chromaticity, Maintenance, Predictive models, classification, Survival analysis, Kaplan Estimator, Machine Learning
National Category
Computer Systems Infrastructure Engineering
Identifiers
urn:nbn:se:du-48198 (URN)978-91-88679-61-1 (ISBN)
Public defence
2024-05-24, room Clas Ohlson and online, Campus Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2024-04-22 Created: 2024-03-07 Last updated: 2024-04-22Bibliographically approved

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Saleh, RoxanFleyeh, HasanAlam, Moudud

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