Three Methods for Anticipating and Understanding Uncertainty of Outputs from Transportation and Land Use Models

Abstract

This study demonstrates three methods for uncertainty propagation in transportation and land-use models (LUMs): Local Sensitivity Analysis with Interaction (LSAI), Monte Carlo (MC), and Bayesian Melding (BM). Two case-study settings are used to illustrate how these methods work, allowing for inter-method comparisons. LSAI can provide the sign of change implied by changes in model inputs, the relative importance of changes in different inputs, and a decomposition of changes in outputs due to the impact of inputs’ individual and interactive. LSAI is limited to relatively small-size problems because its computing time rises exponentially with the number of (groups of) inputs. Moreover, LSAI obtains only point estimates, while MC and BM methods can deliver entire distributions of each output through an understanding of the uncertainty in all model inputs and parameters. MC delivers each output’s distribution and requires hundreds of samples, especially for more accurate results. Fortunately, MC methods are especially useful for high-dimensional problems because convergence rates are not a function of model dimensionality and errors depend only on sample size and input uncertainties. BM delivers posterior distributions for model outputs, using prior probability distributions and likelihoods of inputs and parameters, along with validation of/comparison to intermediate model outputs. A BM approach can be extremely expensive, in terms of computing time, since it requires several hundred model runs.