Estimation of Infrastructure Transition Probabilities from Condition Rating Data

Abstract

Markovian transition probabilities have been used extensively in the field of infrastructure management to provide forecasts of facility conditions. However, existing approaches used to estimate these transition probabilities from inspection data are mostly ad hoc and suffer from important methodological limitations. In this paper, we present a rigorous econometric method for the estimation of infrastructure deterioration models and associated transition probabilities from condition rating data. This methodology, which is based on ordered probit techniques, explicitly treats facility deterioration as a latent variable, recognizes the discrete ordinal nature of condition ratings, and, as opposed to state-of-the-art methods, explicitly links deterioration to relevant explanatory variables. An empirical case study using a bridge inspection data set from Indiana demonstrates the capabilities of the proposed methodology.