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A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

机译：使用贝叶斯方法的多元Poisson对数正态回归模型，用于按严重程度预测事故数

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摘要

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.
机译：已经进行了许多努力来调查与道路设计特征，环境因素和交通状况有关的撞车事故。但是，大多数研究都依赖于单变量计数模型。也就是说，分别估计不同严重性级别的流量崩溃计数，这可能会忽略未观察到的错误术语中的共享信息，降低参数估计的效率，并导致样本数据库中的潜在偏差。本文提供了多元Poisson对数正态（MVPLN）规范，该规范同时根据伤害严重性对事故计数进行建模。 MVPLN规范允许使用更通用的相关结构以及过度分散。此方法解决了几个问题，分别估计崩溃数时很难回答。多亏了碰撞建模和贝叶斯统计的最新进展，使用Gibbs采样器和Metropolis-Hastings（M-H）算法在贝叶斯范式内对参数进行了估计，以解决华盛顿州农村两车道高速公路上的碰撞。 MVPLN方法的估计结果表明，在不同伤害严重程度的事故计数之间，统计上的显着相关性。非零对角线元素表明在所有严重性级别上崩溃计数的过度分散。结果为高速公路安全处理和设计政策提供了一些建议。例如，宽车道和路肩是降低撞车频率的关键，较长的垂直曲线也是如此。

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