Propagation of Uncertainty in Transportation Land Use Models: Investigation of DRAM-EMPAL and UTPP Predictions in Austin, Texas

Abstract

Propagation of uncertainty in outputs of a standard integrated model of transportation and land use has been examined. Austin-calibrated DRAM-EMPAL predictions of residence and work locations were used as inputs to a UTPP-type four-step travel demand model, and the resulting travel times were fed forward into the future period’s land use models. Covariance in inputs (including model parameters and demographic variables) was accommodated through multivariate Monte Carlo sampling of 200 scenarios. Variances in land use and travel predictions were analyzed over time and as a function of input values. Results indicate that output variations were most sensitive to the exponent of the link performance function, the split of trips between peak and off-peak, and several trip generation and attraction rates. Twenty years in the future, final uncertainty levels (as measured by coefficients of variation) solely due to input and parameter estimation errors are on the order of 38% for total regional peak-period vehicle miles traveled (VMT), 45% for peak-period flows, and 50% and 37% for residential and employment densities, respectively. This means central point estimates of key model outputs are very likely (>30%) to fall 38% to 50% below or above the mean value. In the Austin example, 15% of the region’s 200 simulated peak-period VMT estimates fell below 3.7 million mi per day and 15% exceeded 8.4 million mi. Such substantial variation is solely due to standard model parameter and input uncertainties. Other uncertainty about the future and about human behavior exists and will add more variation.