title: A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods.
abstract: Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis-Hastings (M-H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.
authors.affiliation: Texas Department of Transportation, 125 E. 11th Street, Austin, TX 78701-2483, USA. jma@dot.state.tx.us
authors.forename: Jianming
authors.initials: J
authors.lastname: Ma
authors.forename: Kara M
authors.initials: KM
authors.lastname: Kockelman
authors.forename: Paul
authors.initials: P
authors.lastname: Damien
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D001336:Automobiles / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D016012:Poisson Distribution; D012449:Safety / Q000706:statistics & numerical data*; D013781:Texas; D014481:United States