Date of Graduation


Document Type


Degree Name

Master of Science in Civil Engineering (MSCE)

Degree Level



Civil Engineering


Kevin D. Hall

Committee Member

Andrew F. Braham

Second Committee Member

Andrew F. Braham


Pure sciences, Applied sciences, Heavy vehicle, Lateral placement/position, Long-term pavement performance, Wheel path


The position and width of wheel path on a pavement lane is an important consideration in conducting pavement distress survey, especially for distinguishing longitudinal cracking from fatigue cracking. Currently, the widely used wheel path definition is derived from the LTPP distress manual with fixed width and position, in which the influence of traffic wander on wheel path was overlooked. This thesis attempts to develop a new wheel path definition based on the lateral placement investigation of heavy vehicles' wandering in the field. Generally, the factors influencing vehicle lateral placement can be divided into seven categories, but only four factors are considered to be the most important in this research: weather, visibility, horizontal curve, and traffic condition. To achieve this goal, a high resolution digital camera mounted on overpass is utilized to acquire lateral placement data under low and heavy traffic condition, during daytime and nighttime, in sunny, rainy, and windy weather, and at five different locations on an interstate highway. Based on the collected data, statistical methods are utilized to investigate the influence of the four factors on the lateral displacement distribution of truck wheels. Herein, the ANalysis Of VAriance (ANOVA) test was applied to rank the effects of four factors on wheel shift, and the Phillips-Perron test (pp.test) was employed to check the stability of data. Subsequently, an approach is proposed to analyze the lateral position data with different parameters. A new analysis model called Multi-Factor Traffic Lateral Position (M-FTLP) is developed in the research to compute the frequency distribution of wheel-path wandering at different deviation levels. The M-FTLP model exploits local information (index weight) to determine lateral distribution of truck positions at a particular location. Based on the frequency distributions of the wandering deviation levels from M-FTLP model, the wheel paths at the five sample road segments are identified.