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

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  • Li, Baibing

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.

Suggested Citation

  • Li, Baibing, 2009. "Markov models for Bayesian analysis about transit route origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 43(3), pages 301-310, March.
    • Handle: RePEc:eee:transb:v:43:y:2009:i:3:p:301-310
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    References listed on IDEAS

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    1. Li, Baibing & De Moor, Bart, 1999. "Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 33(3), pages 203-214, April.
    2. Li, Yuwei & Cassidy, Michael J., 2007. "A generalized and efficient algorithm for estimating transit route ODs from passenger counts," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 114-125, January.
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    7. Lamond, B. & Stewart, N. F., 1981. "Bregman's balancing method," Transportation Research Part B: Methodological, Elsevier, vol. 15(4), pages 239-248, August.
    8. Li, Baibing & Moor, Bart De, 2002. "Dynamic identification of origin-destination matrices in the presence of incomplete observations," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 37-57, January.
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    Citations

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    Cited by:

    1. Maryam Abareshi & Mehdi Zaferanieh & Mohammad Reza Safi, 2019. "Origin-Destination Matrix Estimation Problem in a Markov Chain Approach," Networks and Spatial Economics, Springer, vol. 19(4), pages 1069-1096, December.
    2. Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    3. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.
    4. Yang, Xuejing & Low, Joyce M.W. & Tang, Loon Ching, 2011. "Analysis of intermodal freight from China to Indian Ocean: A goal programming approach," Journal of Transport Geography, Elsevier, vol. 19(4), pages 515-527.
    5. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
    6. Tangjian Wei & Feng Shi & Guangming Xu, 2019. "Estimation of Time-Varying Passenger Demand for High Speed Rail System," Complexity, Hindawi, vol. 2019, pages 1-24, March.
    7. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.

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