Multivariate poisson lognormal modeling of crashes by type and severity on rural two lane highways

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• 作者：Kai Wang^a ; ^{kai.wang@uconn.edu} ; John N. Ivan^b ; ^{john.ivan@uconn.edu} ; Nalini Ravishanker^c ; ^{nalini.ravishanker@uconn.edu} ; Eric Jackson^d ; ^{eric.d.jackson@uconn.edu}
• 关键词：Multivariate Poisson Lognormal (MVPLN) model ; Integrated Nested Laplace (INLA) Bayesian approach ; Rural two-lane highways ; Crash type models ; Crash severity models
• 刊名：Accident Analysis & Prevention
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：99
• 期：part_PA
• 页码：6-19
• 全文大小：742 K
• 卷排序：99

文摘

Crash counts by crash type and severity are significantly correlated. The effects of contributing factors on crashes can vary by crash type and by crash severity. The Multivariate Poisson Lognormal (MVPLN) model outperforms the Univariate Poisson Lognormal (UPLN) model in crash prediction accuracy. The Integrated Nested Laplace Approximation (INLA) MVPLN model can decrease the computational time compared with the Markov Chain Monte Carlo (MCMC) MVPLN model.