Influence of Demand Uncertainty and Correlations on Traffic Predictions and Decisions

Abstract: Decisions to improve a regional transportation network are often based on predictions of future link flows that assume future travel demand is a deterministic matrix. Despite broad awareness of the uncertainties inherent in forecasts, rarely are uncertainties considered explicitly within the methodological framework due at least in part to a lack of knowledge as to how uncertainties affect the optimality of decisions. This article seeks to address this issue by presenting a new method for evaluating future travel demand uncertainty and finding an efficient technique for generating multiple realizations of demand. The proposed method employs Hypersphere Decomposition, Cholesky Decomposition, and user equilibrium traffic assignment. Numerical results suggest that neglecting correlations between the future demands of travel zone pairs can lead to improvement decisions that are less robust and could frequently rank improvements improperly. Of the six sampling techniques employed, Antithetic sampling generated travel demand realizations with the least relative bias and error.