Date of Award:

2016

Document Type:

Thesis

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Advisor/Chair:

Dr. Kevin Heaslip

Abstract

This study links traffic sign visibility and legibility to quantify the effects of damage or deterioration on sign retroreflective performance. In addition, this study proposes GIS-based data integration strategies to obtain and extract climate, location, and emission data for in-service traffic signs. The proposed data integration strategy can also be used to assess all transportation infrastructures’ physical condition. Additionally, non-parametric machine learning methods are applied to analyze the combined GIS, Mobile LiDAR imaging, and digital photolog big data. The results are presented to identify the most important factors affecting sign visual condition, to predict traffic sign vandalism that obstructs critical messages to drivers, and to determine factors contributing to the temporary obstruction of the sign messages. The results of data analysis provide insight to inform transportation agencies in the development of sign management plans, to identify traffic signs with a higher likelihood of failure, and to schedule sign replacement.

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