Analysis of vehicle accident-injury severities: A comparison of segment-versus accident-based latent class ordered probit models with class-probability functions
Analysis of vehicle accident-injury severities: A comparison of segment-versus accident-based latent class ordered probit models with class-probability functions
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DOI：
10.1016/j.amar.2018.03.003
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发表时间：
2018-06-01
影响因子：
12.9
通讯作者：
Mannering, Fred
中科院分区：
文献类型：
--
作者：
Fountas, Grigorios;Anastasopoulos, Panagiotis Ch.;Mannering, Fred
利用华盛顿州2011年至2013年发生的1990起单车事故的信息，采用两种潜在类建模方法：基于分段和基于事故的潜在类有序概率模型，研究了最严重受伤车辆乘员的伤害严重度水平。基于段的潜在类排序概率单位框架允许解释性参数在高速公路段人口的未观察组（类）之间变化，而建模结构均匀地对待所有段特定的损伤观察（分组）。基于事故的潜在类排序概率单位框架允许解释性参数在事故人群的未观察组之间变化，并且建模结构单独对待所有事故伤害严重性观察（未分组）。为了进一步解决由潜在类别中的公路路段或事故观测的概率分配所引起的异质性，允许类别概率分别作为解释性参数的函数而变化。对于这两种建模方法，建议的潜在类的方法与类概率函数相比，其潜在类对应的固定类概率，结果支持前者的统计优势，在统计拟合和解释能力。实证研究结果表明，这两种建模方法的潜力，揭露司机，车辆，碰撞和天气的具体来源的异质性，特别是，基于段的方法来考虑段特定的异质性的能力。两种建模方法之间的比较评价表明，基于段的方法提供了更好的整体统计拟合。此外，这两种方法的预测精度进行了探讨，通过概率和误差为基础的措施，展示了预测精度的好处，基于段的方法。爱思唯尔有限公司出版
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. Published by Elsevier Ltd.