马尔可夫切换负二项模型：应用于车辆事故频率
Markov switching negative binomial models: an application to vehicle accident frequencies
摘要：
本文提出了两状态马尔可夫切换模型来研究事故频率。这些模型假设存在两种不可观测的道路安全状态，并且道路实体（路段）可以随着时间的推移在这些状态之间切换。状态是不同的，在这个意义上，在不同的状态下，事故频率是由单独的计数过程（通过单独的泊松或负二项过程）产生的。为了证明本文所提出的方法的适用性，两个状态的马尔可夫切换负二项模型估计使用五年的事故频率印第安纳州际公路段。贝叶斯推断方法和马尔可夫链蒙特卡罗（MCMC）模拟用于模型估计。估计马尔可夫转换模型的结果在一个优越的统计拟合相对于标准（单状态）负二项模型。研究发现，更频繁的状态是安全的，它与更好的天气条件。频率较低的状态被认为是不太安全的，并与不利的天气条件。
英文摘要：
In this paper, two-state Markov switching models are proposed to study accident frequencies. These models assume that there are two unobserved states of roadway safety, and that roadway entities (roadway segments) can switch between these states over time. The states are distinct, in the sense that in the different states accident frequencies are generated by separate counting processes (by separate Poisson or negative binomial processes). To demonstrate the applicability of the approach presented herein, two-state Markov switching negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. 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) negative binomial model. It is found that the more frequent state is safer and it is correlated with better weather conditions. The less frequent state is found to be less safe and to be correlated with adverse weather conditions.
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