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A joint-probability approach to crash prediction models

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Titel: A joint-probability approach to crash prediction models
Autoren: Wong, SC, Pei, X, Sze, NN
Quelle: Accident Analysis & Prevention. 43:1160-1166
Verlagsinformationen: Elsevier BV, 2011.
Publikationsjahr: 2011
Schlagwörter: Models, Statistical, Accidents, Traffic - Classification - Mortality - Prevention & Control - Statistics & Numerical Data, 05 social sciences, Accidents, Traffic, Bayes Theorem, Statistical, Survival Analysis, 01 natural sciences, Markov Chains, Wounds And Injuries - Classification - Epidemiology - Mortality - Prevention & Control, Traffic - Classification - Mortality - Prevention & Control - Statistics & Numerical Data, Models, Accidents, 11. Sustainability, 0502 economics and business, Humans, Wounds and Injuries, Safety - Statistics & Numerical Data, Safety, 0101 mathematics, Monte Carlo Method
Beschreibung: 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.
Publikationsart: Article
Conference object
Sprache: English
ISSN: 0001-4575
DOI: 10.1016/j.aap.2010.12.026
Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/21376914
https://www.sciencedirect.com/science/article/pii/S0001457510004033
https://core.ac.uk/display/37971546
https://hub.hku.hk/handle/10722/150553
http://www.sciencedirect.com/science/article/pii/S0001457510004033
https://trid.trb.org/view/1097747
http://hdl.handle.net/10722/150553
http://hdl.handle.net/10722/197363
Rights: Elsevier TDM
Dokumentencode: edsair.doi.dedup.....d87885d5bf510ef01666e39a1f9fae0c
Datenbank: OpenAIRE
Beschreibung
Abstract: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.
ISSN:00014575
DOI:10.1016/j.aap.2010.12.026