Date of Award:
5-2016
Document Type:
Thesis
Degree Name:
Doctor of Philosophy (PhD)
Department:
Civil and Environmental Engineering
Committee Chair(s)
Kevin Heaslip
Committee
Kevin Heaslip
Committee
Guifang Fu
Committee
Ziqi Song
Committee
John Rice
Committee
Laurie McNeill
Abstract
Traffic signs often convey critical information to drivers. However, traffic signs are only effective when clearly visible and legible. This study aims to determine the effects of various damage and deterioration forms on sign retroreflectivity, identify the most important factors affecting traffic sign visual condition, predict traffic sign vandalism that obstructs critical messages to drivers, and identify important environmental factors contributing to the temporary obstruction of the sign messages. To do so, two data sets are used. A sample data of over 1,700 signs was manually collected in the field, and the background retroreflectivity of each sign was measured using a handheld retroreflectometer. In addition, sign data of over 97,000 traffic signs was digitally collected by driving an equipped vehicle. Sign visual condition and damage/deterioration data were obtained from inspection of daytime digital images taken of each individual sign. A GIS-based strategy is proposed to extract location and climate data for every individual sign. Various statistical tests and models are also used to accomplish the goals of study.
Checksum
1c6857dd176bae8c762c9e4b846a1f8d
Recommended Citation
Khalilikhah, Majid, "Traffic Sign Management: Data Integration and Analysis Methods for Mobile LiDAR and Digital Photolog Big Data" (2016). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 4744.
https://digitalcommons.usu.edu/etd/4744
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