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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/9178

Title: Markov models for Bayesian analysis about transit route origin-destination matrices
Authors: Li, Baibing
Keywords: Bayesian analysis
Markov model
Maximum entropy method
O-D matrix
Transit route
Issue Date: 2009
Publisher: © Elsevier
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
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1016/j.trb.2008.07.001
URI: https://dspace.lboro.ac.uk/2134/9178
Publisher Link: http://www.sciencedirect.com/science/article/pii/S0191261508000805
ISSN: 0191-2615
Appears in Collections:Published Articles (Business School)

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