IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v97y2006i1p246-273.html
   My bibliography  Save this article

Bayesian multivariate spatial models for roadway traffic crash mapping

Author

Listed:
  • Song, J.J.
  • Ghosh, M.
  • Miaou, S.
  • Mallick, B.

Abstract

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.

Suggested Citation

  • Song, J.J. & Ghosh, M. & Miaou, S. & Mallick, B., 2006. "Bayesian multivariate spatial models for roadway traffic crash mapping," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 246-273, January.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:1:p:246-273
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(05)00030-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mardia, K. V., 1988. "Multi-dimensional multivariate Gaussian Markov random fields with application to image processing," Journal of Multivariate Analysis, Elsevier, vol. 24(2), pages 265-284, February.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guikema, Seth D., 2009. "Natural disaster risk analysis for critical infrastructure systems: An approach based on statistical learning theory," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 855-860.
    2. Mingyu Kang & Anne Vernez Moudon & Haena Kim & Linda Ng Boyle, 2019. "Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS," IJERPH, MDPI, vol. 16(19), pages 1-14, September.
    3. Ricardo A. Daziano & Luis Miranda-Moreno & Shahram Heydari, 2013. "Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice," Transport Reviews, Taylor & Francis Journals, vol. 33(5), pages 570-592, September.
    4. Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.
    5. 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.
    6. Daniel J. Graham & Emma J. McCoy & David A. Stephens, 2013. "Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial dependence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(4), pages 931-950, October.
    7. 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.
    8. Wu, Peijie & Meng, Xianghai & Song, Li, 2021. "Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    9. Peter Congdon, 2010. "Random‐effects models for migration attractivity and retentivity: a Bayesian methodology," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 755-774, October.
    10. Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
    11. Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    2. Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Dennis Matanda & Otibho Obianwu & Ngianga-Bakwin Kandala, 2021. "Evaluating changes in the prevalence of female genital mutilation/cutting among 0-14 years old girls in Nigeria using data from multiple surveys: A novel Bayesian hierarchical spatio-temporal model," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-31, February.
    3. Marco Gramatica & Peter Congdon & Silvia Liverani, 2021. "Bayesian modelling for spatially misaligned health areal data: A multiple membership approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 645-666, June.
    4. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.
    5. S. Upadhyay & M. Peshwani, 2008. "Posterior analysis of lognormal regression models using the Gibbs sampler," Statistical Papers, Springer, vol. 49(1), pages 59-85, March.
    6. Ngianga-Bakwin Kandala & Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Gerry Mackie & Bettina Shell-Duncan, 2019. "A Spatial Analysis of the Prevalence of Female Genital Mutilation/Cutting among 0–14-Year-Old Girls in Kenya," IJERPH, MDPI, vol. 16(21), pages 1-28, October.
    7. Naranjo, L. & Martín, J. & Pérez, C.J., 2014. "Bayesian binary regression with exponential power link," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 464-476.
    8. Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    9. Brown, Sarah & Ghosh, Pulak & Su, Li & Taylor, Karl, 2015. "Modelling household finances: A Bayesian approach to a multivariate two-part model," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 190-207.
    10. Zhang, Yue & Zhang, Bin, 2018. "Semiparametric spatial model for interval-censored data with time-varying covariate effects," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 146-156.
    11. Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.
    12. Xiaoping Jin & Bradley P. Carlin & Sudipto Banerjee, 2005. "Generalized Hierarchical Multivariate CAR Models for Areal Data," Biometrics, The International Biometric Society, vol. 61(4), pages 950-961, December.
    13. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.
    14. Susanne Gschlößl & Claudia Czado, 2008. "Modelling count data with overdispersion and spatial effects," Statistical Papers, Springer, vol. 49(3), pages 531-552, July.
    15. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    16. Refik Soyer & M. Murat Tarimcilar, 2008. "Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach," Management Science, INFORMS, vol. 54(2), pages 266-278, February.
    17. Cindy Xin Feng, 2015. "Bayesian joint modeling of correlated counts data with application to adverse birth outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1206-1222, June.
    18. Wenchen Liu & Yincai Tang & Ancha Xu, 2021. "Zero-and-one-inflated Poisson regression model," Statistical Papers, Springer, vol. 62(2), pages 915-934, April.
    19. Sain, Stephan R. & Cressie, Noel, 2007. "A spatial model for multivariate lattice data," Journal of Econometrics, Elsevier, vol. 140(1), pages 226-259, September.
    20. Xiaoping Jin & Sudipto Banerjee & Bradley P. Carlin, 2007. "Order‐free co‐regionalized areal data models with application to multiple‐disease mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 817-838, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:97:y:2006:i:1:p:246-273. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.