Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach

Huang, H.L., Chin, H.C., & Haque, M.M. (2009) Hotspot Identification: A Full Bayesian Hierarchical Modeling Approach. In Lam, W.H.K., Wong, S.C., & Lo, H.K. (Eds.) Transportation and Traffic Theory 2009: Golden Jubilee. Springer US, pp. 441-462.

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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.

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3 citations in Web of Science®
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ID Code: 51228
Item Type: Book Chapter
Additional Information: Papers selected for presentation at ISTTT18, a peer reviewed series since 1959
Additional URLs:
Keywords: full Bayes (FB) hierarchical, hotspot identification
DOI: 10.1007/978-1-4419-0820-9_22
ISBN: 9781441908193
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2009 Springer Science+Business Media
Deposited On: 28 Jun 2012 22:43
Last Modified: 15 Jul 2017 22:01

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