An Assessment Scheme for Road Network Capacity under Demand Uncertainty

Abstract

Network capacity is a vital index with which to assess the operation of traffic networks. The majority of existing traffic network capacity models are formed as bi-level programming, which maximizes the traffic flows under equilibrium constraints and is extremely dependent on the current origin–destination (O–D) travel demand. However, an accurate O–D matrix is not easy to obtain in practice. This article aims to provide an assessment method for traffic network capacity under demand uncertainty. To consider the variation in real demand in traffic networks, the current travel demand is treated as unknown parameters that are defined inside a restricted set. Based on the hypothesis of different probability distributions, the road network capacity is calculated by repeated capacity loading experiments with a random sampling of uncertain parameters. To improve the efficiency of the repeated calculations of the traffic assignment model, a sensitivity-analysis-based (SAB) approximation method was developed to avoid the double calculation of the network capacity model for each random O–D matrix. The SAB method significantly improved the calculation efficiency while ensuring accuracy. Using simulation experiments, we researched the reliability of road network capacity and the probabilities of high congestion for each link under uncertain demand.