School of Civil Engineering, 550 Stadium Mall Drive, Purdue University, West Lafayette, IN 47907, United States.
Accid Anal Prev. 2009 Jul;41(4):829-38. doi: 10.1016/j.aap.2009.04.006. Epub 2009 May 5.
In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.
在这项研究中，提出了两种状态马尔可夫转换多项逻辑回归模型，用于事故伤害严重程度的统计建模。这些模型假设在道路安全的两个未观察到的状态之间随时间进行马尔可夫转换，作为解释潜在未观察到的异质性的一种手段。这些状态是不同的，因为在不同的状态下，事故严重程度的结果是由单独的多项逻辑回归过程产生的。为了展示该方法的适用性，对印第安纳州道路上发生的事故严重程度结果进行了为期四年的两种状态马尔可夫转换多项逻辑回归模型估计。贝叶斯推理方法和马尔可夫链蒙特卡罗（MCMC）模拟用于模型估计。对于许多道路等级和事故类型，估计的马尔可夫转换模型相对于标准（单状态）多项逻辑回归模型具有更好的统计拟合度。结果发现，道路安全更频繁的状态与更好的天气条件相关，而不那么频繁的状态与不利的天气条件相关。
