An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/5/2413 |
| _version_ | 1797475669019459584 |
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| author | Roxan Saleh Hasan Fleyeh Moudud Alam |
| author_facet | Roxan Saleh Hasan Fleyeh Moudud Alam |
| author_sort | Roxan Saleh |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-09T20:48:21Z |
| format | Article |
| id | doaj.art-2fe8766d91d141adbd69e3193be3c546 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:48:21Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2fe8766d91d141adbd69e3193be3c5462023-11-23T22:40:22ZengMDPI AGApplied Sciences2076-34172022-02-01125241310.3390/app12052413An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic SignsRoxan Saleh0Hasan Fleyeh1Moudud Alam2School of Information and Engineering, Dalarna University, 781 70 Borlänge, SwedenSchool of Information and Engineering, Dalarna University, 781 70 Borlänge, SwedenSchool of Information and Engineering, Dalarna University, 781 70 Borlänge, SwedenThe 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 R<sup>2</sup> 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.https://www.mdpi.com/2076-3417/12/5/2413road traffic signretroreflective sheeting materiallinear regression |
| spellingShingle | Roxan Saleh Hasan Fleyeh Moudud Alam An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs Applied Sciences road traffic sign retroreflective sheeting material linear regression |
| title | An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs |
| title_full | An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs |
| title_fullStr | An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs |
| title_full_unstemmed | An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs |
| title_short | An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs |
| title_sort | analysis of the factors influencing the retroreflectivity performance of in service road traffic signs |
| topic | road traffic sign retroreflective sheeting material linear regression |
| url | https://www.mdpi.com/2076-3417/12/5/2413 |
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