Systematic Statistical Approach to Populate Missing Performance Data in Pavement Management Systems
Publication: Journal of Infrastructure Systems
Volume 21, Issue 4
Abstract
Transportation agencies use pavement management systems (PMS) for their maintenance and rehabilitation planning, programming, and budgeting. PMS is used to make decisions regarding when maintenance and rehabilitation should be applied. To support these decisions, it is important to have reliable data on pavement conditions and accurate performance models for predicting pavement condition. The data on pavement condition typically come from regular field surveys resulting in distress, condition, and ride scores. PMS data sets are often incomplete (for some locations and some years) as a result of operational limitations reducing the predictive power of the performance models. Model-free and model-based replacement techniques for estimating missing data points have been designed and successfully used in other application areas like statistics, economics, marketing, medicine, psychometrics, and political science. It is therefore reasonable to apply these methods to the PMS databases. Statistical techniques are assembled and used in a robust approach to systematically analyze the effect of applying these techniques to rebuild missing performance data. As a case study, continuous reinforced concrete pavement (CRCP) sections were selected to test the proposed statistical systematic approach from a pavement management information system (PMIS) maintained by the Texas Department of Transportation (TxDOT). A major effect was observed in the results of predicting the distress scores when applying the developed approach.
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Acknowledgments
This research was conducted at the Center for Transportation Infrastructure Systems (CTIS) at the University of Texas at El Paso (UTEP) using pavement management data provided by the Texas Department of Transportation.
References
Al-Zou’bi, M. M. (2013). “A systematic approach to manage missing data in pavement management systems.” Ph.D. dissertation, Civil Engineering, Univ. of Texas at El Paso, El Paso, TX.
Anderson, A., Basilevsky, A., and Hum, D. (1983). “Measurement: Theory and techniques.” Handbook of survey research, P. Rossi, J. Wright, and A. Anderson, eds., Academic Press, New York, 244–251.
Day, N. E. (1969). “Estimating the components of a mixture of normal distributions.” Biometrika, 56(3), 463–474.
Chu, C., and Durango, C. P. (2008). “Incorporating maintenance effectiveness in the estimation of dynamic infrastructure performance models.” Comput.-Aided Civ. Infrastruct. Eng., 23(3), 174–188.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). “Maximum likelihood for incomplete data via the EM algorithm.” J. R. Stat. Soc. B, 39, 1–38.
Dempster, A. P., and Rubin, D. B. (1983). “Introduction.” Incomplete data in sample surveys (volume 2): Theory and bibliography, W. G. Madow, I. Olkin, and D. B. Rubin, eds., Academic Press, New York, 3–10.
DeSarbo, W. S., and Rao, V. R. (1986). “A constrained unfolding model for product positioning analysis.” Marketing Sci., 5(1), 1–19.
Fuchs, C. (1982). “Maximum likelihood estimation and model selection in contingency tables with missing data.” J. Am. Stat. Assoc., 77, 270–278.
Gao, L., Aguiar-Moya, J., and Zhang, Z. (2011). “Performance modeling of infrastructure condition data with maintenance intervention.”, Transportation Research Board, Washington, DC, 109–116.
Hong, F., and Prozzi, J. (2010). “Roughness model accounting for heterogeneity based on in-service pavement performance data.” J. Transp. Eng., 205–213.
Kaufman, C. J. (1988). “The application of logical imputation to household measurement.” J. Market Res. Soc., 30(4), 453–466.
Krzanowski, W.J. (1982). “Mixtures of continuous and categorical variables in discriminant analysis: A hypothesis-testing approach.” Biometrics, 38, 991–1002.
Little, R. (1988). “Missing data adjustments in large surveys.” J. Bus. Econ. Stat., 6(3), 296–297.
Little, R. J. A., and Rubin, D. B. (1987). Statistical analysis with missing data, Wiley, New York.
Livneh, M. (1994). “Repeatability and reproducibility of manual pavement distress survey methods.” Proc., 3rd Int. Conf. on Managing Pavements, Transportation Research Board National Council, Washington, DC.
Marwala, T. (2009). Computational intelligence for missing data imputation, estimation, and management, Information Science Reference (an imprint of IGI Global), New York.
Naser, G. H., et al. (2012). “Evaluation and development of pavement scores, performance models SND need estimates for the TXDOT pavement management information system-final report.” Texas Dept. of Transportation, Texas Transportation Institute, Austin, TX.
Olkin, I., and Tate, R. F. (1961). “Multivariate correlation models with mixed discrete and continuous variables.” Anal. Math. Stat., 448–465.
Orchard, T., and Woodbury, M. A. (1972). “A missing information principle: Theory and applications.” Proc., Sixth Berkeley Symp. on Mathematical Statistics and Probability, University of California Press, Berkeley, CA, 697–715.
Raymond, M. R. (1986). “Missing data in evaluation research.” Valuation Health Prof. 9(4), 395–420.
Roth, P. L. (1994). “Missing data: A conceptual review for applied psychologists.” Personnel Psychol., 47(3), 537–560.
Roth, P. L., Switzer, F. S., and Switzer, D. M. (1999). “Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques.” Organiz. Res. Methods, 2(3), 211–232.
Shahin, M. Y., Darter, M. I., and Kohn, S. D. (1980). “Condition evaluation of jointed concrete airfield pavement.” Transp. Eng. J., 106(4), 381–399.
Stampley, B. E., Smith, R. E., Scullion, T., and Miller, B. (1995a). Pavement management information system concepts, equations, and analysis models, Texas Transportation Institute, Texas A&M Univ. System, College Station, TX.
Stampley, B. E., Smith, R. E., Scullion, T., and Miller, B. (1995b). “Pavement management information system concepts, equations, and analysis of pavements.”, Texas Transportation Institute, Texas A&M University System, College Station, TX.
Tsikriktsis, N. (2005). “A review of techniques for treating missing data in OM survey research.” J. Oper. Manage., 24(1), 53–62.
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Published In
Journal of Infrastructure Systems
Volume 21 • Issue 4 • December 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Apr 14, 2013
Accepted: Dec 22, 2014
Published online: Mar 24, 2015
Discussion open until: Aug 24, 2015
Published in print: Dec 1, 2015
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