The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count – like the number of accidents – is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005–2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.

交通事故数据分析对于解决众多问题至关重要，例如了解事故链中的促成因素、识别热点以及为有关道路安全管理的政策决策提供信息。用于分析交通事故数据的大多数统计模型都是逻辑计数回归模型（通常是泊松回归），因为计数（如事故数量）被用作响应。然而，观测数据的特征经常不会使泊松分布成为一个站得住脚的假设。例如，观察到的数据很少表现出相等的均值和方差，并且通常具有过多的零。有时，数据可能具有由群体混合而不是单个群体组成的异质结构。在这样的数据分析中，可以使用混合泊松回归模型。在这项研究中，安全总局（土耳其，2005-2014 年）登记的交通事故伤亡人数使用具有两个分量的新型混合分布建模：泊松分布和零截断泊松分布。这种模型不同于文献中现有的混合模型，其中的成分要么都是泊松分布或所有零截断泊松分布。通过仿真和交通数据分析，将所提出的模型与泊松回归模型进行了比较。