01123228

Component

https://doi.org/10.3141/2103-05

http://scholar.google.com...aque&publication_year=2009

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard EB ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate–related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.

01138796

09-1065

English

pp 32-41

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309126182

Print

Figures (6) ; References (30) ; Tables (1)

Crash data; Empirical methods; High risk locations

Empirical Bayes method; Full Bayes method; Hierarchical models; Naive ranking method

Singapore

Data and Information Technology; Highways; Safety and Human Factors; I81: Accident Statistics

TRIS, TRB, ATRI

Jan 30 2009 5:17PM

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