: Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved. In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country. The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

中文翻译：

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涉及车祸的驾驶员的异常行为和相关伤害严重程度：同一国家的主要城市之间是否存在差异？

：基于事故数据集的碰撞数据分析通常不包括与人类相关的变量，因为它们很难从碰撞数据中重建。然而，事故情况的记录可以帮助实现这一目的，因为可以根据所涉及驾驶员的异常行为和不当行为对事故进行分类。在这种情况下，来自意大利 10 个最大城市的城市碰撞数据被用来开发四个逻辑回归模型，以与驾驶员相关的碰撞情况（异常行为：疏忽驾驶、非法操纵、与行人的错误互动和超速）作为因变量，作为预测因素的其他与碰撞相关的因素（有关用户和所涉及车辆以及有关道路几何形状和条件的信息）。还建立了另外两个模型来研究相同因素对观察到与驾驶员异常行为相关的碰撞情况的车辆乘员以及相关行人的伤害严重程度的影响。通过多层次方法考虑了 10 个不同城市之间的差异，该方法仅揭示了与注意力不集中相关的碰撞情况下的显着差异。在其他模型中，城市之间的差异并不显着，表明同一国家内的结果相当相似。结果显示了碰撞因素（驾驶员、车辆或道路相关）与人类相关碰撞情况和严重程度之间的多种关系。无信号交叉口与非法操纵碰撞事故尤其相关，而夜间时段则与超速相关碰撞事故以及车辆乘员受伤/伤亡明显相关。骑自行车者和摩托车手比汽车乘客遭受更多伤害/伤亡，而后者通常表现出更多异常行为。行人伤亡与主干道、重型车辆和老年行人有关。