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Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach

In: Transportation and Traffic Theory 2009: Golden Jubilee

Author

Listed:
  • H.L. Huang

    (University of Central Florida)

  • H.C. Chin

    (National University of Singapore)

  • M.M. Haque

    (National University of Singapore)

Abstract

This study proposes a full Bayes (FB) hierarchical modeling approach in traffic crash hotspot identification. The FB approach is able to account for all uncertainties associated with crash risk and various risk factors by estimating a posterior distribution of the site safety on which various ranking criteria could be based. Moreover, by use of hierarchical model specification, FB approach is able to flexibly take into account various heterogeneities of crash occurrence due to spatiotemporal effects on traffic safety. Using Singapore intersection crash data (1997-2006), an empirical evaluate was conducted to compare the proposed FB approach to the state-of-the-art approaches. Results show that the Bayesian hierarchical models with accommodation for site specific effect and serial correlation have better goodness-of-fit than non-hierarchical models. Furthermore, all model-based approaches perform significantly better in safety ranking than the naive approach using raw crash count. The FB hierarchical models were found to significantly outperform the standard EB approach in correctly identifying hotspots.

Suggested Citation

  • H.L. Huang & H.C. Chin & M.M. Haque, 2009. "Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 441-462, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-0820-9_22
    DOI: 10.1007/978-1-4419-0820-9_22
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