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Bayesian multivariate spatial models for roadway traffic crash mapping


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  • Song, J.J.
  • Ghosh, M.
  • Miaou, S.
  • Mallick, B.
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    We consider several Bayesian multivariate spatial models for estimating the crash rates from different kinds of crashes. Multivariate conditional autoregressive (CAR) models are considered to account for the spatial effect. The models considered are fully Bayesian. A general theorem for each case is proved to ensure posterior propriety under noninformative priors. The different models are compared according to some Bayesian criterion. Markov chain Monte Carlo (MCMC) is used for computation. We illustrate these methods with Texas Crash Data.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 1 (January)
    Pages: 246-273

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:1:p:246-273

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    Keywords: Hierarchical models Markov chain Monte Carlo Multivariate CAR Noninformative priors Posterior propriety;


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    Cited by:
    1. Peter Congdon, 2008. "The need for psychiatric care in England: a spatial factor methodology," Journal of Geographical Systems, Springer, vol. 10(3), pages 217-239, September.
    2. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.


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