Using information from 1990 single-vehicle accidents that occurred between 2011 and 2013 in the state of Washington, the injury severity level of the most severely injured vehicle occupant is studied using two latent class modeling approaches: segment-based and accident-based latent class ordered probit model with class-probability functions. The segment-based latent class ordered probit framework allows explanatory parameters to vary across unobserved groups (classes) of the highway segment population, while the modeling structure treats all segment-specific injury observations homogeneously (grouped). The accident-based latent class ordered probit framework allows for the explanatory parameters to vary across unobserved groups of the accident population, and the modeling structure treats all accident injury-severity observations individually (ungrouped). To further address heterogeneity arising from the probabilistic assignment of the highway segments or accident observations in the latent classes, the class probabilities are allowed to vary as a function of explanatory parameters, respectively. For both modeling approaches, the proposed latent class approach with class-probability functions is compared to its latent class counterpart with fixed class probabilities, and the results support the statistical superiority of the former, in terms of statistical fit and explanatory power. The empirical findings show the potential of both modeling approaches to unmask driver-, vehicle-, collision- and weather-specific sources of heterogeneity and, specifically, the capability of the segment-based approach to account for segment-specific heterogeneity. The comparative evaluation between the two modeling approaches shows that the segment-based approach provides better overall statistical fit. Furthermore, the forecasting accuracy of both approaches is explored through probability- and error-based measures demonstrating the forecasting accuracy benefits of the segment-based approach.

利用华盛顿州在2011年至2013年之间发生的1990年单车事故的信息，使用两种潜在类别建模方法研究了受重伤最严重的乘员的伤害严重程度：基于分段的和基于事故的潜在类别具有类概率函数的概率模型。基于分段的潜在类有序概率框架允许高速公路各部分人群的未观察组（类）的解释参数有所不同，而建模结构则将所有特定于分段的损伤观测结果均等地（分组）对待。基于事故的潜伏类有序概率模型框架使解释性参数在未观察到的事故人群中有所不同，建模结构将所有事故伤害严重性观察值单独（未分组）处理。为了进一步解决潜在类别中高速公路路段的概率分配或事故观测所产生的异质性，允许类别概率分别随解释参数而变化。对于这两种建模方法，将拟议的具有类概率函数的隐性类方法与具有固定类概率的隐性类对应方法进行比较，结果在统计拟合和解释能力方面都支持前者的统计优势。经验发现表明，这两种建模方法都有潜力揭示驾驶员，车辆，碰撞和天气特定的异质性来源，特别是 基于片段的方法解决特定于片段的异质性的能力。两种建模方法之间的比较评估表明，基于细分的方法提供了更好的总体统计拟合。此外，通过基于概率和误差的度量来探索这两种方法的预测准确性，以证明基于分段的方法的预测准确性的好处。