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Activity Number:
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415
- Modeling in Transportation Safety Issues
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Transportation Statistics Interest Group
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Abstract #327116
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Presentation
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Title:
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Bayesian Analysis of Multivariate Crash Counts Using Copulas
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Author(s):
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Eun Sug Park* and Man-Suk Oh and Rosy Oh and Jae Youn Ahn
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Companies:
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Texas A&M Transportation Institute and Ewha Womans University and Ewha Womans University and Ewha Womans University
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Keywords:
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highway safety;
crash severity;
unobserved heterogeneity;
overdispersion;
correlated crash counts
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Abstract:
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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 a more general copula-based multivariate count regression model within a Bayesian framework, which incorporate the dependence among the multivariate crash counts by modeling multivariate random effects using copulas. Overdispersion and 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 negative binomial regression models and multivariate Poisson-Lognormal regression models. The proposed method is illustrated with the crash count data of five different severity levels collected from 451 three-leg unsignalized intersections in California.
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Authors who are presenting talks have a * after their name.
