This study investigates the determinants of driver injury severity in animal-vehicle collisions while systematically accounting for unobserved heterogeneity in the data by using three methodological approaches: mixed logit model, mixed logit model with heterogeneity in means, and mixed logit model with heterogeneity in means and variances. Using the data from Washington state from January 1, 2012 to December 31, 2016, a wide range of factors that could potentially affect the injury severity of drivers were examined. Moreover, the temporal stability and transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also used to study the temporal stability of the explanatory variables. Model estimation results show that many parameters can potentially increase the likelihood of severe injuries in Animal-vehicle crashes including freeways/expressways, daylight crashes, early morning crashes, dry road surface and clear weather condition. Moreover, the model estimation results show that accounting for the heterogeneity in the means (and variances) of the random parameters can improve the overall fit of the model. Some variables showed relatively similar marginal effects among different methodological approaches while some others showed different marginal effects upon the application of different methods. With regard to temporal stability of explanatory variables, the findings of this study show how underestimating the temporal stability concept may lead to inaccurate and unreliable conclusions.

这项研究调查了动物和汽车碰撞中驾驶员伤害严重程度的决定因素，同时通过三种方法学方法系统地解释了数据中未观察到的异质性：混合对数模型，均值具有异质性的混合logit模型以及均值和异质性具有混合性的logit模型差异。使用2012年1月1日至2016年12月31日华盛顿州的数据，研究了可能影响驾驶员伤害严重性的各种因素。此外，通过一系列似然比检验研究了模型的时间稳定性和可传递性。边际效应也被用来研究解释变量的时间稳定性。模型估计结果表明，许多参数可能会增加动物车辆碰撞中严重受伤的可能性，包括高速公路/高速公路，日光碰撞，清晨碰撞，干燥的路面和晴朗的天气。此外，模型估计结果表明，考虑随机参数均值（和方差）的异质性可以改善模型的整体拟合度。在不同的方法论方法中，一些变量显示出相对相似的边际效应，而另一些变量在应用不同方法时显示出不同的边际效应。关于解释变量的时间稳定性，本研究的结果表明，低估时间稳定性概念可能会导致不准确和不可靠的结论。