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).


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Graduation Date





Tatari, Omer


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering


CFE0009640; DP0027462





Release Date

February 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Restricted to the UCF community until February 2024; it will then be open access.