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Bayesian analysis of multivariate crash counts using copulas

Title
Bayesian analysis of multivariate crash counts using copulas
Authors
Park, Eun SugOh, RosyAhn, Jae YounOh, Man-Suk
Ewha Authors
오만숙안재윤
SCOPUS Author ID
오만숙scopus; 안재윤scopus
Issue Date
2021
Journal Title
ACCIDENT ANALYSIS AND PREVENTION
ISSN
0001-4575JCR Link

1879-2057JCR Link
Citation
ACCIDENT ANALYSIS AND PREVENTION vol. 149
Keywords
Highway safetyMultivariate crash countsCrash typesCrash severityUnobserved heterogeneityOverdispersion
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Indexed
SSCI; SCOPUS WOS scopus
Document Type
Article
Abstract
There has been growing interest in jointly modeling correlated multivariate crash counts in road safety research over the past decade. To assess the effects of roadway characteristics or environmental factors on crash counts by severity level or by collision type, various models including multivariate Poisson regression models, multivariate negative binomial regression models, and multivariate Poisson-Lognormal regression models have been suggested. We introduce more general copula-based multivariate count regression models with correlated random effects within a Bayesian framework. Our models incorporate the dependence among the multivariate crash counts by modeling multivariate random effects using copulas. Copulas provide a flexible way to construct valid multivariate distributions by decomposing any joint distribution into a copula and the marginal distributions. Overdispersion as well as general correlation structures including both positive and negative correlations in multivariate crash counts can easily be accounted for by this approach. Our copular-based models can also encompass previously suggested multivariate count regression models including multivariate Poisson-Gamma mixture models and multivariate Poisson-Lognormal regression models. The proposed method is illustrated with crash count data of five different severity levels collected from 451 three-leg unsignalized intersections in California.
DOI
10.1016/j.aap.2019.105431
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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