A joint-probability approach to crash prediction models
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| Název: | A joint-probability approach to crash prediction models |
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| Autoři: | Wong, SC, Pei, X, Sze, NN |
| Zdroj: | Accident Analysis & Prevention. 43:1160-1166 |
| Informace o vydavateli: | Elsevier BV, 2011. |
| Rok vydání: | 2011 |
| Témata: | 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 |
| Popis: | 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. |
| Druh dokumentu: | Article Conference object |
| Jazyk: | English |
| ISSN: | 0001-4575 |
| DOI: | 10.1016/j.aap.2010.12.026 |
| Přístupová URL adresa: | 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 |
| Přístupové číslo: | edsair.doi.dedup.....d87885d5bf510ef01666e39a1f9fae0c |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 00014575 |
| DOI: | 10.1016/j.aap.2010.12.026 |