Highway Improvement Project Rankings due to Uncertain Model Inputs: Application of Traditional Transportation and Land Use Models

Abstract

While much research has been devoted to analyzing the variation in transportation and land use model outputs due to uncertainty, little has been done to quantitatively answer the more important question of how decision making will change based on recognition of this uncertainty. This paper aims to begin to fill this gap by evaluating how roadway investment decisions will differ depending on whether or not uncertainty is recognized. Population and employment control totals, as well as trip generation and trip distribution parameters, are found via antithetic sampling, and a full feedback integrated gravity-based land use and four-step travel model is used. It is found that the ranking of improvement projects may indeed be different if uncertainty is considered relative to treating all parameters and data as deterministic. The experimental analysis conducted in this paper found this percent difference to be between 4 and 25% depending on the performance metric used: total system travel time, vehicle miles traveled, total delay, average network speed, and standard deviation of network speed were all examined.