Statistical Analysis of Ordered Pavement Roughness Perceptions with Two-Group Random Effects
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 150, Issue 2
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.
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Data Availability Statement
All data used during the study are available online at http://cee.eng.usf.edu/faculty/flm/StatEcon-Files/Ex14-2.txt. The models and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors express their gratitude to Dr. Fred Mannering for his guidance and his previous research team for providing the data. The authors also thank Dr. Yu Zhang, Dr. Pei-Sung Lin, Dr. Xiaopeng Li, Dr. Zhenyu Wang from the University of South Florida for their valuable help and input. The first author is also appreciative of research funding from the Guangdong Province Highway Administration Bureau of China (201762).
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© 2024 American Society of Civil Engineers.
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Received: Mar 21, 2023
Accepted: Dec 29, 2023
Published online: Apr 8, 2024
Published in print: Jun 1, 2024
Discussion open until: Sep 8, 2024
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