Pavement rutting is one of the major asphalt pavement surface distresses affecting pavement structure integrity and driving safety and is also a required performance measure specified in the Highway Performance Monitoring System (HPMS). Manual rutting measurement is still conducted by many state Departments of Transportation (DOTs), like Georgia DOT; however, it is time-consuming, labor-intensive, and dangerous. Although point-based rut bar systems have been developed and utilized by state DOTs to measure rutting conditions, they often underestimate rut depth measurements. There is an urgent need to develop an automated method to accurately and reliably measure rutting conditions. With the advance of sensing technology, emerging 3D line laser imaging technology is capable of collecting high-resolution 3D range data at highway speed (e.g., 100 km/h) and, therefore, holds a great potential for accurately and repeatedly measuring pavement rutting condition. The main contribution of this research includes a methodology, along with a series of methods and procedures, for the first time, developed utilizing emerging 3D line laser imaging technology to improve existing 1D rut depth measurement accuracy and repeatability and to measure additional 2D and 3D rutting characteristics. These methods and procedures include: (1) a threshold-based outlier removal method employing the multivariate adaptive regression splines (MARS) technique to remove outliers caused by non-rutting features, such as wide transverse cracks and potholes; (2) a modified topological-ordering-based segment clustering (MTOSC) method to optimally partition the continuous roadway network into segments with uniform rutting condition; (3) an overlapping-reducing heuristic method to solve large-scale segmentation problems; (4) a network-level rutting condition assessment procedure for analyzing 3D range data to statistically interpret the pavement rutting condition in support of network-level pavement management decisions; (5) an isolated rut detection method to determine the termini, maximum depth, and volume of isolated ruts in support of project-level maintenance operations. Comprehensive experimental tests were conducted in the laboratory and the field to validate the accuracy and repeatability of 1D rut depth obtained using the 3D range data. Experimental tests were also conducted in the laboratory to validate the accuracy of 3D rut volume. Case studies were conducted on one interstate highway (I-95), two state routes (SR 275 and SR 67), and one local road (Benton Blvd.) to demonstrate the capability of the developed methods and procedures. The results of experimental tests and case studies show that the proposed methodology is promising for improving the rutting measurement accuracy and reliability. This research is one of the initial effort in studying the applicability of this emerging sensing technology in pavement management. And the outcomes of this research will play a key role in advancing state DOTs’ existing pavement rutting condition assessment practices.

Previous studies show that pavement condition properties are significant factors to enhance road safety and riding experience, and pavements with low quality might have inadequate performance in terms of safety and riding experience. Pavement Management System (PMS) databases include pavement properties for each segment of the road collected by the agencies. Understanding the impact of road characteristics on crash frequency is a key step to prevent crashes. Whereas other studies analyzed the effect of different characteristics such as International Roughness Index (IRI), Rutting Depth (RD), Annual Average Daily Traffic (AADT), this thesis analyzed the effect of Critical Condition Index (CCI) on crash frequency, in addition to the other factors identified in previous studies. Other characteristics such as Percentage of Heavy Vehicles, Road Surface Condition, Road Lighting Condition, and Driver Conditions are taken into the consideration. The scope of the study is the interstate highway system in Fairfax County, Virginia. Negative Binomial, Least Square and Nominal Logistic Models were developed, showing that the CCI value is a significant factor to predict the number of crashes, and that it has different effect for different values of AADT. The result of this study is a substantial step towards developing an integrated transportation control and infrastructure management framework. / Master of Science / Many factors cause crashes in the roads. Although there is a common sense that road characteristics such as asphalt quality are important in terms of road safety, there are few studies that scientifically prove that statement. In addition, asphalt maintenance decisions making process is mainly based on cost benefit optimization, and traffic safety is not considered at the process. The purpose of this study is to analyze crashes and road characteristics related to each crash to understand the effect of those characteristics on crash frequency, and eventually, to build a model to predict the number of crashes at each part of the road. The model can help transportation agencies to have a better understanding in terms of safety consequences of their infrastructure management plans. The scope of this study is the highway interstate system in Northern Virginia. Results suggest that pavement condition has a significant impact on crash frequency.