School of Civil Engineering, Purdue University, West Lafayette, IN 47907-2051, United States.
Accid Anal Prev. 2011 May;43(3):1140-7. doi: 10.1016/j.aap.2010.12.024. Epub 2011 Jan 8.
Traditional crash-severity modeling uses detailed data gathered after a crash has occurred (number of vehicles involved, age of occupants, weather conditions at the time of the crash, types of vehicles involved, crash type, occupant restraint use, airbag deployment, etc.) to predict the level of occupant injury. However, for prediction purposes, the use of such detailed data makes assessing the impact of alternate safety countermeasures exceedingly difficult due to the large number of variables that need to be known. Using 5-year data from interstate highways in Indiana, this study explores fixed and random parameter statistical models using detailed crash-specific data and data that include the injury outcome of the crash but not other detailed crash-specific data (only more general data are used such as roadway geometrics, pavement condition and general weather and traffic characteristics). The analysis shows that, while models that do not use detailed crash-specific data do not perform as well as those that do, random parameter models using less detailed data still can provide a reasonable level of accuracy.
传统的碰撞严重程度模型使用碰撞发生后收集的详细数据（涉及的车辆数量、乘客年龄、碰撞时的天气条件、涉及的车辆类型、碰撞类型、乘客约束装置使用情况、安全气囊展开情况等）来预测乘客受伤程度。然而，出于预测目的，由于需要了解大量变量，因此使用此类详细数据来评估替代安全对策的影响变得非常困难。本研究使用印第安纳州州际公路的 5 年数据，探索了使用详细碰撞特定数据和包括碰撞伤害结果但不包括其他详细碰撞特定数据的数据的固定和随机参数统计模型（仅使用更一般的数据，例如道路几何形状、路面状况以及一般天气和交通特征）。分析表明，虽然不使用详细碰撞特定数据的模型表现不如使用这些数据的模型，但使用较不详细数据的随机参数模型仍然可以提供合理的准确性水平。
