Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data

Injury risk curves are vital in quantifying the relative safety consequences of real-world collisions. Previous injury risk curves for bicycle-passenger vehicle crashes have predominantly focused on frontal impacts. This creates a gap in cyclist injury risk assessment for other geometric crash configurations. The goal of this study was to create an “omnidirectional” injury risk model, informed by known injury causing mechanisms, that is applicable to most geometric configurations. We used data from years 1999–2022 of the German In-Depth Accident Study (GIDAS). We describe the pattern of injuries for cyclists involved in collisions with passenger vehicles, and we developed injury risk functions at various AIS levels for these collisions. A mechanistic-based approach accounting for biomechanically-relevant variables was used to select model parameters a priori. Cyclist age (including children) and sex were regarded as relevant predictors of injury risk. Speed and impact geometry were captured through a novel predictor, Effective Collision Speed, which transforms the vehicle and cyclist speeds into a single value and incorporates frictional considerations observed during side engagements. Cyclist engagement with the vehicle was captured with a variable demonstrating the potential for a normal projection. We additionally present analyses weighted toward German nationwide data. We identified 6,576 cyclists involved in collisions with passenger vehicles. AIS3+ cyclist injuries occurred most often in the head, thorax, and lower extremities. Effective Collision Speed was a strong predictor of injury risk. Collisions with a potential for a normal projection were associated with increased risk, though this was only significant at the MAIS2+F severity level. Younger children had slightly higher injury risk compared to young adults, while elderly cyclists had the highest risk of AIS3+ injury. Sex was a significant predictor only for the MAIS2+F injury risk curves. U.S. cyclist fatalities increased 55% from 2010 to 2021. To reduce injuries and fatalities, it is crucial to understand cyclist injury risk. This study builds on previous analyses by including children, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using weighting to generalize these estimates toward national German statistics.伤害风险曲线（Injury risk curves）是量化真实世界碰撞相对安全后果的核心工具。此前针对自行车与乘用车碰撞的伤害风险曲线研究，大多聚焦于正面碰撞场景，这使得其他几何碰撞构型下的骑行者伤害风险评估存在显著空白。 本研究旨在构建一套全向性伤害风险模型，该模型基于已知致伤机制，可适用于绝大多数碰撞几何构型。本研究采用了1999年至2022年德国深度事故研究（German In-Depth Accident Study, GIDAS）的数据，描述了与乘用车发生碰撞的骑行者的损伤分布特征，并针对此类碰撞构建了不同简明损伤定级（Abbreviated Injury Scale, AIS）等级下的伤害风险函数。 研究采用基于损伤机制的方法，预先选取了与生物力学相关的变量作为模型参数：骑行者年龄（含儿童群体）与性别被视为伤害风险的重要预测因子；通过一项创新性预测指标——有效碰撞速度（Effective Collision Speed），可将车辆与骑行者的速度整合为单一数值，同时纳入侧面碰撞场景中观测到的摩擦因素，以此表征碰撞速度与几何特征；骑行者与车辆的接触状态则通过一项可体现法向投射潜力的变量进行量化。此外，本研究还呈现了基于德国全国数据加权的分析结果。 本研究共纳入6576名与乘用车发生碰撞的骑行者。AIS 3+级骑行者损伤最常发生于头部、胸部与下肢。有效碰撞速度是伤害风险的强预测因子。存在法向投射潜力的碰撞与更高的伤害风险相关，但这一关联仅在最大简明损伤定级（Maximum Abbreviated Injury Scale, MAIS）2+F严重程度下具有统计学显著性。与年轻成年人相比，低龄儿童的伤害风险略高，而老年骑行者发生AIS 3+级损伤的风险最高。性别仅在MAIS 2+F伤害风险曲线中为显著预测因子。 2010年至2021年，美国骑行者死亡人数上升了55%。为降低骑行者的损伤与死亡人数，明晰其伤害风险至关重要。本研究在既往分析的基础上，纳入了儿童群体，新增了更多基于损伤机制的预测因子，拓宽了纳入碰撞场景的范围，并通过加权处理使研究结果可推广至德国全国统计数据。