Assessing the color status and daylight chromaticity of road signs through machine learning approaches
The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111223000286 |
| _version_ | 1797670294321627136 |
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| author | Roxan Saleh Hasan Fleyeh Moudud Alam Arend Hintze |
| author_facet | Roxan Saleh Hasan Fleyeh Moudud Alam Arend Hintze |
| author_sort | Roxan Saleh |
| collection | DOAJ |
| description | The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs.The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden.The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates.The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively.The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. |
| first_indexed | 2024-03-11T20:57:36Z |
| format | Article |
| id | doaj.art-518cf6c9512841b1a3f8ec61739592aa |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-03-11T20:57:36Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-518cf6c9512841b1a3f8ec61739592aa2023-09-30T04:54:02ZengElsevierIATSS Research0386-11122023-10-01473305317Assessing the color status and daylight chromaticity of road signs through machine learning approachesRoxan Saleh0Hasan Fleyeh1Moudud Alam2Arend Hintze3School of Information and Engineering, Dalarna University, Borlänge 781 70, Sweden; Swedish Transport Administration, Röda Vägen 1, Borlänge 781 89, Sweden; Corresponding author at: School of Information and Engineering, Dalarna University, Borlänge 781 70, Sweden.School of Information and Engineering, Dalarna University, Borlänge 781 70, SwedenSchool of Information and Engineering, Dalarna University, Borlänge 781 70, SwedenSchool of Information and Engineering, Dalarna University, Borlänge 781 70, SwedenThe color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs.The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden.The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates.The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively.The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context.http://www.sciencedirect.com/science/article/pii/S0386111223000286Road signsDaylight chromaticityRegressionClassificationPredictionMachine learning algorithms |
| spellingShingle | Roxan Saleh Hasan Fleyeh Moudud Alam Arend Hintze Assessing the color status and daylight chromaticity of road signs through machine learning approaches IATSS Research Road signs Daylight chromaticity Regression Classification Prediction Machine learning algorithms |
| title | Assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| title_full | Assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| title_fullStr | Assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| title_full_unstemmed | Assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| title_short | Assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| title_sort | assessing the color status and daylight chromaticity of road signs through machine learning approaches |
| topic | Road signs Daylight chromaticity Regression Classification Prediction Machine learning algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S0386111223000286 |
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