Abstract
This paper focuses on path-based solution algorithms to the stochastic user equilibrium (SUE) and investigates their convergence properties. Two general optimization methods are adapted to solve the logit SUE problem. First, a method that closely follows the Gradient Projection (GP) algorithm developed for the deterministic problem is derived. While this method is very efficient for the deterministic user equilibrium problem, we use a simple example to illustrate why it is not suitable for the SUE problem. Next, a different variant of gradient projection, which exploits special characteristics of the SUE solution, is presented. In this method the projection is on the linear manifold of active constraints. The algorithms are applied to solve simple networks. The examples are used to compare the convergence properties of the algorithms with a path-based variant of the Method of Successive Averages (MSA) and with the Disaggregate Simplicial Decomposition (DSD) algorithm.
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