Department of Civil and Coastal Engineering, University of Florida, 512 Weil Hall, PO Box 116580, Gainesville, FL 32611, USA.
Accid Anal Prev. 2011 Jan;43(1):49-57. doi: 10.1016/j.aap.2010.07.007. Epub 2010 Aug 12.
Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data - these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined.
鉴于卡车运输对一个国家经济福祉的重要性，以及卡车带来的安全问题，对大型卡车事故进行研究至关重要。本文通过对影响大型卡车事故伤害严重程度的实证因素进行广泛分析，做出了贡献。本研究使用最近全国范围内具有代表性的大型卡车事故样本数据，以确定影响这些事故整体伤害严重程度的因素。解释性因素包括事故、车辆和驾驶员的特征。使用两种措施对伤害严重程度进行建模。两个模型之间观察到了一些相似之处和一些差异，这突显了需要提高对事故伤害严重程度评估的准确性。所估计的模型同时捕捉了各种解释性因素的边际效应。特别是，这些模型表明，在控制了多种其他因素后，驾驶员行为变量（如卡车驾驶员的分心、汽车驾驶员的酒精使用和汽车驾驶员的情绪因素）对事故的严重程度有影响。另一个有趣的发现是，几个表示缺失数据的哑变量具有很强的统计学意义，这反映了事故本身的性质如何影响数据的完整性。未来的研究应努力更全面地收集此类数据，以便确定这些方面对事故严重程度的实际影响。
