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Towards smart maintenance : machine-learning based prediction of retroreflectivity and color of road traffic signs

Av: Serie: Dalarna Doctoral Dissertations ; 32Utgivningsinformation: Borlänge : Dalarna University, 2024Beskrivning: 46 sISBN:
  • 9789188679611
Ämnen: Onlineresurser: Anmärkning: Härtill 6 uppsatser Avhandlingskommentar: Diss. (sammanfattning) Borlänge : Högskolan Dalarna, 2024 Sammanfattning: 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.
Exemplartyp: Dissertation
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Härtill 6 uppsatser

Diss. (sammanfattning) Borlänge : Högskolan Dalarna, 2024

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.