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Markov models for Bayesian analysis about transit route origin-destination matrices

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journal contribution
posted on 05.12.2011, 14:09 by Baibing Li
The key factor that complicates statistical inference for an origin-destination (O-D) matrix is that the problem per se is usually highly underspecified, with a large number of unknown entries but many fewer observations available for the estimation. In this paper, we investigate statistical inference for a transit route O-D matrix using on-off counts of passengers. A Markov chain model is incorporated to capture the relationships between the entries of the transit route matrix, and to reduce the total number of unknown parameters. A Bayesian analysis is then performed to draw inference about the unknown parameters of the Markov model. Unlike many existing methods that rely on iterative algorithms, this new approach leads to a closed-form solution and is computationally more efficient. The relationship between this method and the maximum entropy approach is also investigated.

History

School

  • Business and Economics

Department

  • Business

Published in

Transportation Research Part B: Methodological

Volume

43

Issue

3

Pages

301 - 310

Citation

LI, B., 2009. Markov models for Bayesian analysis about transit route origin-destination matrices. Transportation Research Part B: Methodological, 43 (3), pp. 301-310

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publication date

2009

Notes

This article was published in the journal, Transportation Research Part B: Methodological [© Elsevier]. The definitive version is available from: http://www.sciencedirect.com/science/article/pii/S0191261508000805

ISSN

0191-2615

Language

en