Cross-time transportation network improvement strategies under supply and demand uncertainty

Abstract

Traditional transportation planning is based on deterministic assumptions which can result in unreasonable conclusion in the planning scheme. This paper considers the network optimization problem under the condition of cross-time decision-making. Assuming that supply and demand obey the known random distribution, we further give the network design model of stochastic bi-level programming. The objective function of the upper model is to maximize the net present value of the system, and the lower model uses traditional user equilibrium model. Genetic algorithm with Monte Carlo is used to solve the cross-time transportation network design problem under supply and demand uncertainty. The Sioux Falls network example shows that the model can be applied to medium-sized networks.