Chang L Y, Mannering F
Department of Civil Engineering, University of Washington, Seattle, WA 98195, USA.
Accid Anal Prev. 1999 Sep;31(5):579-92. doi: 10.1016/s0001-4575(99)00014-7.
The impact that large trucks have on accident severity has long been a concern in the accident analysis literature. One important measure of accident severity is the most severely injured occupant in the vehicle. Such data are routinely collected in state accident data files in the U.S. Among the many risk factors that determine the most severe level of injury sustained by vehicle occupants, the number of occupants in the vehicle is an important factor. These effects can be significant because vehicles with higher occupancies have an increased likelihood of having someone seriously injured. This paper studies the occupancy/injury severity relationship using Washington State accident data. The effects of large trucks, which are shown to have a significant impact on the most severely injured vehicle occupant, are accounted for by separately estimating nested logit models for truck-involved accidents and for non-truck-involved accidents. The estimation results uncover important relationships between various risk factors and occupant injury. In addition, by comparing the accident characteristics between truck-involved accidents and non-truck-involved accidents, the risk factors unique to large trucks are identified along with the relative importance of such factors. The findings of this study demonstrate that nested logit modeling, which is able to take into account vehicle occupancy effects and identify a broad range of factors that influence occupant injury, is a promising methodological approach.
大型卡车对事故严重程度的影响长期以来一直是事故分析文献中的一个关注点。事故严重程度的一个重要衡量标准是车辆中受伤最严重的驾乘人员。此类数据在美国各州事故数据文件中常规收集。在决定车辆驾乘人员受伤最严重程度的众多风险因素中，车内驾乘人员数量是一个重要因素。这些影响可能很显著，因为乘坐人数较多的车辆中有人受重伤的可能性更大。本文使用华盛顿州事故数据研究乘坐人数与受伤严重程度之间的关系。大型卡车对受伤最严重的车辆驾乘人员有显著影响，通过分别估计涉及卡车事故和不涉及卡车事故的嵌套逻辑模型来考虑这种影响。估计结果揭示了各种风险因素与驾乘人员受伤之间的重要关系。此外，通过比较涉及卡车事故和不涉及卡车事故的事故特征，确定了大型卡车特有的风险因素及其相对重要性。本研究结果表明，嵌套逻辑模型能够考虑车辆乘坐人数的影响，并识别出影响驾乘人员受伤的广泛因素，是一种很有前景的方法。
