Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan
This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable o...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | International Journal of Transportation Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043024000182 |
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| _version_ | 1846100646307037184 |
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| author | Roxan Saleh Hasan Fleyeh |
| author_facet | Roxan Saleh Hasan Fleyeh |
| author_sort | Roxan Saleh |
| collection | DOAJ |
| description | This study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards. |
| format | Article |
| id | doaj-art-df12d63e435a4beeaec90d0382ae187f |
| institution | Kabale University |
| issn | 2046-0430 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj-art-df12d63e435a4beeaec90d0382ae187f2024-12-30T04:15:39ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302024-12-0116276291Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespanRoxan Saleh0Hasan Fleyeh1School of Information and Engineering, Dalarna University, 781 70 Borlänge, Sweden; Swedish Transport Administration, Röda Vägen 1, 781 89 Borlänge, Sweden; Corresponding author at: School of Information and Engineering, Dalarna University, 781 70 Borlänge, Sweden.School of Information and Engineering, Dalarna University, 781 70 Borlänge, SwedenThis study addresses the critical safety issue of declining retroreflectivity values of road traffic signs, which can lead to unsafe driving conditions, especially at night. The paper aims to predict the retroreflectivity coefficient values of these signs and to classify their status as acceptable or rejected (in need of replacement) using machine learning models. Moreover, logistic regression and survival analysis are used to predict the median lifespans of road traffic signs across various geographical locations, focusing on signs in Croatia and Sweden as case studies. The results indicate high accuracy in the predictive models, with classification accuracy at 94% and an R2 value of 94% for regression analysis. A significant finding is that a considerable number of signs maintain acceptable retroreflectivity levels within their warranty period, suggesting the feasibility of extending maintenance checks and warranty periods to 15 years which is longer than the current standard of 10 years. Additionally, the study reveals notable variations in the median lifespans of signs based on color and location. Blue signs in Croatia and Sweden exhibit the longest median lifespans (28 to 35 years), whereas white signs in Sweden and red signs in Croatia show the shortest (16 and 10 years, respectively). The high accuracy of logistic regression models (72–90%) for lifespan prediction confirms the effectiveness of this approach. These findings provide valuable insights for road authorities regarding the maintenance and management of road traffic signs, enhancing road safety standards.http://www.sciencedirect.com/science/article/pii/S2046043024000182Road signDaylight chromaticityRetroreflectivityPredictionMachine learning algorithms |
| spellingShingle | Roxan Saleh Hasan Fleyeh Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan International Journal of Transportation Science and Technology Road sign Daylight chromaticity Retroreflectivity Prediction Machine learning algorithms |
| title | Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan |
| title_full | Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan |
| title_fullStr | Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan |
| title_full_unstemmed | Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan |
| title_short | Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan |
| title_sort | predictive models for road traffic sign retroreflectivity status retroreflectivity coefficient and lifespan |
| topic | Road sign Daylight chromaticity Retroreflectivity Prediction Machine learning algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2046043024000182 |
| work_keys_str_mv | AT roxansaleh predictivemodelsforroadtrafficsignretroreflectivitystatusretroreflectivitycoefficientandlifespan AT hasanfleyeh predictivemodelsforroadtrafficsignretroreflectivitystatusretroreflectivitycoefficientandlifespan |