Parallel chain convergence of time dependent origin-destination matrices with gibbs sampler

Yow-Jen Jou*, Hsun-Jung Ch, Chien Lun Lan, Chia-Chun Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

An effective method of O-D estimation by the state-space model has been introduced by Jon. Coupled with Gibbs sampler and Kalman filter, the state-space model can generated precious O-D matrices without any prior information while other studies assume that the transition matrix is known or at least approximately known. The Gibbs sampler, a particular type of Markov Chain Monte Carlo method, is one of the iterative simulation methods. To monitor of convergence of this iterative simulation, a parallel chain technique is implemented in this paper. By the numerical example, the convergence of the different chains would be clearly pointed out. The comparison of simulation and real data also shows that satisfying results can be obtained by the model.
Original languageAmerican English
Title of host publicationRecent progress in computational sciences and engineering, vols 7a and 7b
EditorsG Maroulis, T Simos
Pages834-+
Volume7A-B
StatePublished - 2006
Event International Conference on Computational Methods in Science and Engineering - Chania, Greece
Duration: 27 Oct 20061 Nov 2007

Publication series

Name LECTURE SERIES ON COMPUTER AND COMPUTATIONAL SCIENCES
Volume7A-B
ISSN (Print)1573-4196

Conference

Conference International Conference on Computational Methods in Science and Engineering
CountryGreece
CityChania
Period27/10/061/11/07

Keywords

  • origin-destination; state space model; gibbs sampler; Kalman filter; parallel chain

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