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Estimation of dynamic performance models for transportation infrastructure using panel data

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  • Chu, Chih-Yuan
  • Durango-Cohen, Pablo L.

Abstract

We present state-space specifications of time series models as a framework to formulate dynamic performance models for transportation facilities, and to estimate them using panel data sets. The framework provides a flexible and rigorous approach to simultaneously capture the effect of serial dependence and of exogenous factors, while controlling for individual heterogeneity when pooling data across the facilities that comprise the panel. Because the information contained in time series and cross-section data are combined in the estimation, the ensuing performance models capture effects that are not identifiable in either pure time series or pure cross-section data. Also, pooling data across facilities leads to improved estimation results. To illustrate the methodology, we consider three classes of models for a panel of asphalt pavements from the AASHO Road Test. The models differ in the assumptions regarding the structure of the underlying mechanisms generating the data sequences. The results indicate that serial dependence is indeed significant, thereby reinforcing the importance of dynamic modeling. We also compare the specifications to assess the poolability of pavement condition data. The results provide evidence that heterogeneity among the facilities is present in the panel. Finally, we highlight features that elude existing performance models developed with static modeling approaches: the ability to estimate maintenance activities as exogenous variables, and the capability of updating forecasts in response to inspections.

Suggested Citation

  • Chu, Chih-Yuan & Durango-Cohen, Pablo L., 2008. "Estimation of dynamic performance models for transportation infrastructure using panel data," Transportation Research Part B: Methodological, Elsevier, vol. 42(1), pages 57-81, January.
  • Handle: RePEc:eee:transb:v:42:y:2008:i:1:p:57-81
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    References listed on IDEAS

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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. Prozzi, J A & Madanat, S M, 2004. "Development of Pavement Performance Models by Combining Experimental and Field Data," University of California Transportation Center, Working Papers qt6cf8v5cw, University of California Transportation Center.
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, December.
    4. Gendreau, Michel & Soriano, Patrick, 1998. "Airport pavement management systems: an appraisal of existing methodologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(3), pages 197-214, April.
    5. Humplick, Frannie, 1992. "Highway pavement distress evaluation: Modeling measurement error," Transportation Research Part B: Methodological, Elsevier, vol. 26(2), pages 135-154, April.
    6. Durango-Cohen, Pablo L., 2007. "A time series analysis framework for transportation infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 493-505, June.
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    Cited by:

    1. Wesonga, Ronald, 2015. "Airport utility stochastic optimization models for air traffic flow management," European Journal of Operational Research, Elsevier, vol. 242(3), pages 999-1007.
    2. Chen, Yikai & Durango-Cohen, Pablo L., 2015. "Development and field application of a multivariate statistical process control framework for health-monitoring of transportation infrastructure," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 78-102.
    3. Xiaohong Chen & Xiang Wang & Hua Zhang & Jia Li, 2014. "The Diversity and Evolution Process of Bus System Performance in Chinese Cities: An Empirical Study," Sustainability, MDPI, Open Access Journal, vol. 6(11), pages 1-17, November.
    4. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
    5. Chen, Yikai & Corr, David J. & Durango-Cohen, Pablo L., 2014. "Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 96-116.

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