Development of Linear Mixed Effects Models for Predicting Individual Pavement Conditions
Publication: Journal of Transportation Engineering
Volume 133, Issue 6
Abstract
Predicting future conditions of pavement plays an important role in pavement management. Prediction for a specific pavement is usually based on the deterioration trend of a group of pavements with similar characteristics, i.e., the same pavement family. This study proposes using the linear mixed effects model (LMEM) to predict future conditions of a specific pavement section by a weighted combination of the average deterioration trend of the family and the past conditions of the specific pavement. The relative weights are determined by the number of past condition measurements available and the degree of variations of the measured past conditions for the specific pavement. The results of the LMEM show significantly higher accuracy in predicting specific pavement conditions compared with two existing adjustment methods that use the last available condition measurement of the specific pavement to adjust the family trend prediction. The finding of this study shows that the LMEM can be used for project level pavement condition prediction or other types of infrastructure condition prediction, whereas future conditions of a specific entity are to be projected based on a combination of the average “family” trend, as well as the individual’s condition history.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper originated from a research study funded by the Ohio Department of Transportation (ODOT) and the Federal Highway Administration (FHwA). The writers would like to thank the agencies for their funding support and assistance. Comments provided by the anonymous reviewers are also appreciated.
References
Cook, W. D., and Kazakov, A. (1987). “Pavement performance prediction and risk modeling in rehabilitation budget planning: A Markovian approach.” Proc., 2nd North American Conf. on Managing Pavements, Toronto, Vol. 2, 63–75.
Diggle, P. J. (1988). “An approach to the analysis of repeated measurements.” Biometrics, 44, 959–971.
Henderson, C. R. (1953). “Estimation of variance and covariance components.” Biometrics, 9, 226–252.
Laird, N. M., and Ware, J. H. (2004). Applied longitudinal analysis, Wiley, N.J.
Lee, J. C., and Hwang, R. C. (2000). “On estimation and prediction for temporally correlated longitudinal data.” J. Stat. Plan. Infer., 87, 87–104.
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. (1996). “SAS system for mixed models.” SAS Institute Inc., Cary.
Madanat, S., Nakat, Z., and Sathaye, N. (2005). “Development of empirical-mechanistic pavement performance models using data from the Washington State PMS database.” Final Rep. Prepared for the California Department of Transportation through the Partnered Pavement Research Center Contract, PPRC Item 4.5, Rep. No. UCPRC-RR-2005-5, UC Pavement Research Center, Univ. of California, Davis and Berkeley, Calif.
McCulloch, C. E., and Searle, S. R. (2001). Generalized, linear, and mixed models, Wiley, New York.
Shahin, M. Y. (1994). Pavement management for airports, roads, and parking lots, Chapman and Hall, New York.
Verbeke, G., and Molenberghs, G. (2000). Linear mixed models for longitudinal data, Springer, New York.
Verbeke, G., and Molenberghs, G. (1997). Linear mixed models in practice: A SAS-oriented approach, Springer, New York.
Whittaker, E. T., and Robinson, G. (1967). “The Newton-Raphson method.” The calculus of measurements: A treatise on numerical mathematics, 4th Ed., New York, 84–87.
Information & Authors
Information
Published In
Copyright
© 2007 ASCE.
History
Received: Dec 8, 2005
Accepted: Jul 14, 2006
Published online: Jun 1, 2007
Published in print: Jun 2007
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.