Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
Accid Anal Prev. 2011 May;43(3):1160-6. doi: 10.1016/j.aap.2010.12.026. Epub 2011 Jan 11.
Many road safety researchers have used crash prediction models, such as Poisson and negative binomial regression models, to investigate the associations between crash occurrence and explanatory factors. Typically, they have attempted to separately model the crash frequencies of different severity levels. However, this method may suffer from serious correlations between the model estimates among different levels of crash severity. Despite efforts to improve the statistical fit of crash prediction models by modifying the data structure and model estimation method, little work has addressed the appropriate interpretation of the effects of explanatory factors on crash occurrence among different levels of crash severity. In this paper, a joint probability model is developed to integrate the predictions of both crash occurrence and crash severity into a single framework. For instance, the Markov chain Monte Carlo (MCMC) approach full Bayesian method is applied to estimate the effects of explanatory factors. As an illustration of the appropriateness of the proposed joint probability model, a case study is conducted on crash risk at signalized intersections in Hong Kong. The results of the case study indicate that the proposed model demonstrates a good statistical fit and provides an appropriate analysis of the influences of explanatory factors.
许多道路安全研究人员使用了碰撞预测模型，如泊松和负二项回归模型，来研究碰撞发生与解释因素之间的关系。通常，他们试图分别对不同严重程度级别的碰撞频率进行建模。然而，这种方法可能会受到不同严重程度级别的模型估计之间的严重相关性的影响。尽管通过修改数据结构和模型估计方法来努力提高碰撞预测模型的统计拟合度，但很少有工作涉及到对不同严重程度级别的碰撞发生中解释因素的影响的适当解释。在本文中，开发了一个联合概率模型，将碰撞发生和碰撞严重程度的预测集成到一个单一的框架中。例如，应用马尔可夫链蒙特卡罗 (MCMC) 方法全贝叶斯方法来估计解释因素的影响。为了说明所提出的联合概率模型的适用性，在香港信号交叉口的碰撞风险案例研究中进行了说明。案例研究的结果表明，所提出的模型具有良好的统计拟合度，并为解释因素的影响提供了适当的分析。
