Abstract
Longitudinal pavement markings play a significant role on the roadways by delivering information to motorists to help them navigate and follow the road. These markings are also considered to be a crucial control device that can enhance ideal nighttime visibility, especially on rural roads where the surrounding luminance is insufficient. Hence, the main question for public agencies or officials is about when the replacement of the pavement markings needs to take place. The Federal Highway Administration (FHWA) is considering proposing a minimum level of retroreflectivity standard and based on that, the Manual on Uniform Traffic Control Devices (MUTCD) set aside a draft (FHWA 2010). Currently, there are no specific guidelines on maintaining the pavement marking retroreflectivity. The purpose of this research is to develop a pavement marking degradation model using machine learning techniques, which is known as the most powerful technique nowadays. Using such a technique has become more popular than ever with respect to massive growth and the variety of available data to produce complex models with high accuracy compared to traditional statistical methods. In this study, we compared four different algorithms: Support Victor Machine (SVM), Gradient Boosting (GB), Random Forest (RF), and Decision Tree (DT). This study gives the performance measurements of those algorithms using three scenarios. The outcomes demonstrate that all of the algorithms perform magnificently, with accuracy ranging between 87 and 90 percent and an acceptable level of efficiency for Recall (78 percent).
Notes
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Graduation Date
2022
Semester
Summer
Advisor
Tatari, Omer
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Civil, Environmental, and Construction Engineering
Degree Program
Civil Engineering
Format
application/pdf
Identifier
CFE0009640; DP0027462
URL
https://purls.library.ucf.edu/go/DP0027462
Language
English
Release Date
February 2024
Length of Campus-only Access
1 year
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Abdelmaksoud, Ehab, "Using Machine Learning Technique to Develop a Deterioration Predicting Model for Pavement Marking in Florida" (2022). Electronic Theses and Dissertations, 2020-. 1496.
https://stars.library.ucf.edu/etd2020/1496