The University of Akron, Civil Engineering, Auburn Science and Engineering Center, Akron, OH 44325, United States.
Accid Anal Prev. 2011 May;43(3):621-30. doi: 10.1016/j.aap.2010.09.015. Epub 2010 Nov 11.
Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e., incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations. Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck.
标准多项逻辑回归(MNL)和混合逻辑回归(MXL)模型用于估计自行车手、驾驶员、机动车、几何、环境和碰撞类型特征对自行车手伤害严重程度的影响程度,伤害严重程度分为仅财产损失、可能、非致残或严重(即致残或致命)伤害。本研究基于 2002 年至 2008 年俄亥俄州发生的 10029 起涉及自行车的碰撞事故。似然比检验的结果表明,一些影响交叉口和非交叉口自行车手伤害严重程度的因素在实质上是不同的,使用共同模型联合估计这两种类型地点的严重程度的影响可能会导致有偏差或不一致的估计。因此,分别开发了模型来独立评估各种因素对交叉口和非交叉口碰撞事故中自行车手伤害严重程度的影响。发现有几个协变量对交叉口和非交叉口的伤害严重程度都有类似的影响。相反,有六个变量被发现显著影响交叉口位置的伤害严重程度,但不影响非交叉口位置,而四个变量仅影响非交叉口位置的自行车手伤害严重程度。在交叉口发生的碰撞中,如果自行车手没有戴头盔,严重自行车手伤害的可能性增加 14.8%;如果驾驶员受酒精影响,严重自行车手伤害的可能性增加 82.2%;如果涉及的机动车是厢式货车,严重自行车手伤害的可能性增加 141.3%;如果机动车撞击自行车的侧面,严重自行车手伤害的可能性增加 40.6%;如果碰撞发生在带有坡度的水平曲线上,严重自行车手伤害的可能性增加 182.6%。来自非交叉口的结果显示,如果自行车手受药物影响,严重伤害的可能性增加 374.5%;如果驾驶员受酒精影响,严重伤害的可能性增加 150.1%;如果机动车撞击自行车的侧面,严重伤害的可能性增加 53.5%;如果涉及的机动车是重型卡车,严重伤害的可能性增加 99.9%。