Stochastic Assignment with Gammit Path Choice Models

Abstract

Traffic assignment models simulate transportation systems, where flows resulting from user choice behaviour are affected by transportation costs, and costs may be affected by flows due to congestion. Several path choice behaviour models can be specified through random utility theory. Probabilistic path choice models, where perceived path costs are modelled as random variables, lead to stochastic assignment. In this paper, reasonable modelling requirements are proposed to assure a realistic simulation of path choice behaviour through probabilistic choice models. Then, additive Gammit path choice models based on Gamma distribution are introduced and deeply analysed. These models satisfy all the proposed modelling requirements, and can be effectively embedded within existing models and algorithms for stochastic assignment.