Statistical Analysis of Ordered Pavement Roughness Perceptions with Two-Group Random Effects

Abstract

Pavement roughness has long been linked to both vehicle fuel efficiency and pavement structural degradation. Perceptions of pavement roughness, however, may vary among users of different sociodemographic features and therefore potentially impact the allocation of resources for highway maintenance and rehabilitation when equity is considered. It is necessary to investigate the major factors that influence the user perception of pavement roughness. Considering the complexity in structure and relationship of field data, this study applied three state-of-the-art machine learning and statistical methods, including a classification tree, a random-parameter ordered-probit model with a random effect (Model 1), and a correlated random-parameter ordered-probit model (Model 2), to the analysis of user perceived roughness. Data were collected from a prior study that conducted in-vehicle tests involving individual user, pavement, and vehicle. The analysis identified more key factors influencing roughness ranking than previous research and accounted for the heterogeneity in individuals and interactions among random parameters. The results indicate that, whereas physical measurements of pavement roughness (e.g., International Roughness Index), visible distresses such as patches and faulting, and joints provided a strong indication of user roughness ranking, other factors (i.e., particular regularly used route, participants’ age, income, and gender, number of household infants, and interior vehicle noise levels) were also statistically significant. Two-way group random effects were statistically significant in the data, which should be accounted for in future studies. Results from this study fill an important gap between making accurate prediction and uncovering underlying causality in research of physical infrastructure measurement with user perceptions of infrastructure conditions.