@inbook{e66a60a871154f1e9f85796d54ce8796,
title = "Parallel chain convergence of time dependent origin-destination matrices with gibbs sampler",
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.",
keywords = "origin-destination; state space model; gibbs sampler; Kalman filter; parallel chain",
author = "Yow-Jen Jou and Hsun-Jung Ch and Lan, {Chien Lun} and Chia-Chun Hsu",
year = "2006",
month = oct,
language = "American English",
isbn = "978-90-04-15542-8",
volume = "7A-B",
series = " LECTURE SERIES ON COMPUTER AND COMPUTATIONAL SCIENCES",
pages = "834--837",
editor = "{ Maroulis}, G and T Simos",
booktitle = "Recent progress in computational sciences and engineering, vols 7a and 7b",
note = " International Conference on Computational Methods in Science and Engineering ; Conference date: 27-10-2006 Through 01-11-2007",
}