Li-B-Markov-2009.pdf (367.49 kB)
Download fileMarkov models for Bayesian analysis about transit route origin-destination matrices
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.
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School
- Business and Economics
Department
- Business