Document Detail


A joint-probability approach to crash prediction models.
MedLine Citation:
PMID:  21376914     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Many road safety researchers have used crash prediction models, such as Poisson and negative binomial regression models, to investigate the associations between crash occurrence and explanatory factors. Typically, they have attempted to separately model the crash frequencies of different severity levels. However, this method may suffer from serious correlations between the model estimates among different levels of crash severity. Despite efforts to improve the statistical fit of crash prediction models by modifying the data structure and model estimation method, little work has addressed the appropriate interpretation of the effects of explanatory factors on crash occurrence among different levels of crash severity. In this paper, a joint probability model is developed to integrate the predictions of both crash occurrence and crash severity into a single framework. For instance, the Markov chain Monte Carlo (MCMC) approach full Bayesian method is applied to estimate the effects of explanatory factors. As an illustration of the appropriateness of the proposed joint probability model, a case study is conducted on crash risk at signalized intersections in Hong Kong. The results of the case study indicate that the proposed model demonstrates a good statistical fit and provides an appropriate analysis of the influences of explanatory factors.
Authors:
Xin Pei; S C Wong; N N Sze
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Publication Detail:
Type:  Journal Article     Date:  2011-01-11
Journal Detail:
Title:  Accident; analysis and prevention     Volume:  43     ISSN:  1879-2057     ISO Abbreviation:  Accid Anal Prev     Publication Date:  2011 May 
Date Detail:
Created Date:  2011-03-07     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1254476     Medline TA:  Accid Anal Prev     Country:  England    
Other Details:
Languages:  eng     Pagination:  1160-6     Citation Subset:  IM    
Copyright Information:
Copyright © 2010 Elsevier Ltd. All rights reserved.
Affiliation:
Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
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