Capturing Data and Model Uncertainties in Highway Performance Estimation

Abstract

Infrastructure agencies worldwide develop and continue to refine quantitative decision support systems for performance and investment projections. In the earlier stages of the creation of infrastructure decision support systems in the United States, agencies focused on developing appropriate databases and deterministic analysis models to capture important investment and performance relationships. As decision support systems have matured, stronger emphasis has been placed on improving the quality of information for decision makers. Analyzing data and analysis model uncertainties is one logical approach for addressing the information quality of infrastructure decision support systems. This paper develops a computer simulation approach to explore the effects of data and model uncertainties on highway performance estimation, using the federal-level highway decision support system as a case in point. The paper illustrates how data-induced and model-induced influences on the expected value and variance of performance estimates may be used to track real changes in highway performance estimates while capturing changes in the performance evaluation criteria and efficiency of modeling procedures.