Podium Session 6: Estimating Markov Chain Mixing Times: Convergence Rate Towards Equilibrium of a Stochastic Process Traffic Assignment Model
Title: Estimating Markov Chain Mixing Times: Convergence Rate Towards Equilibrium of a Stochastic Process Traffic Assignment Model
Authors: Takamasa Iryo, David Watling, Martin Hazelton
Abstract: Network equilibrium models have been extensively used for decades. The rationale for using equilibrium as a predictor is essentially that (i) a unique and globally stable equilibrium point is guaranteed to exist, and (ii) the transient period over which a system adapts to a change is sufficiently short in time that it can be neglected. However, we ...
Keywords: Markov chain mixing time; Day-to-day dynamics; Stochastic traffic assignment process