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An Approach for Predicting Latent Infrastructure Facility Deterioration

Author

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  • Moshe Ben-Akiva

    (Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Rohit Ramaswamy

    (AT&T Bell Laboratories, Holmdel, New Jersey 07733)

Abstract

A pavement deterioration model predicts the performance of a pavement over time as a function of traffic, pavement characteristics and environmental factors. The most important performance characteristics of a pavement are its ability to bear traffic loads and its ability to provide a smooth ride. However, there is no unambiguous approach to directly measure these performance characteristics. Therefore, we consider pavement performance to be unobservable. The problem of designing pavement deterioration models is the problem of defining the above unobservable characteristics in terms of what is observed, i.e., in terms of measured extents and severities of different damage components. The methodology presented in this paper describes a statistical technique to estimate latent pavement performance from observed pavement damage. No constraints are placed on the number or type of measurements required, so the methodology is flexible enough to include different measurement techniques and data collection strategies. The estimation procedure simultaneously fits a deterioration model and a performance index calibration model to data, thereby producing much better fits to data than traditional deterioration models. The methodology presented in this paper will be useful for deriving more realistic predictive models of pavement deterioration and for defining better data collection strategies.

Suggested Citation

  • Moshe Ben-Akiva & Rohit Ramaswamy, 1993. "An Approach for Predicting Latent Infrastructure Facility Deterioration," Transportation Science, INFORMS, vol. 27(2), pages 174-193, May.
  • Handle: RePEc:inm:ortrsc:v:27:y:1993:i:2:p:174-193
    DOI: 10.1287/trsc.27.2.174
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Li, Sirui & Liu, Ying & Wang, Pengfei & Liu, Peng & Meng, Jun, 2020. "A novel approach for predicting urban pavement damage based on facility information: A case study of Beijing, China," Transport Policy, Elsevier, vol. 91(C), pages 26-37.
    4. Swei, Omar & Gillen, David & Onayev, Anuarbek, 2021. "Improving productivity measures of producing transportation infrastructure using quality-adjusted price indices," Transport Policy, Elsevier, vol. 114(C), pages 372-381.

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