Introduction: Safety is a critical factor in promoting sustainable urban non-motorized travel modes like bicycles. Helmets have shown to be effective in reducing injury severity in bicycle crashes, however, their effects on bicyclists’ behaviors still requires deeper understanding, especially amid the emerging trend of using shared bicycles. Risk compensation effects suggest that bicyclists may offset perceived gains in safety from wearing a helmet by increasing risk-taking behaviors. A better understanding of these compensation effects can be useful in assessing various bicycle safety related programs. Method: Using a sample of 131 bicyclists from the San Francisco Bay area, this research studies how bicyclists respond with respect to risk-taking behaviors under various urban-street conditions, as a function of helmet use. Study participants were each shown 12 videos, shot in Berkeley, California, from the perspective of a bicyclist riding behind another bicyclist. A fractional factorial experiment design was used to systematically vary contextual attributes (e.g., speed, bike lane facilities, on-street parking, passing vehicles) across the videos. After each video, participants were asked to indicate if they would overtake the bicyclist in the video. With the help of data adaptive estimation techniques, targeted maximum likelihood estimation (TMLE) was applied to estimate the average risk difference between helmeted users and non-users, controlling for self-selection effects. Individual-based nonparametric bootstrap was performed to assess the uncertainty associated with the estimator. Results: Our findings suggest, on average, individuals more likely to wear a helmet are 15.6% more likely to undertake a risky overtaking maneuver. Practical Applications: This study doesn’t try to oppose mandatory helmet laws, but rather serves as a cautionary warning that road safety programs may need to consider strategies in which unintended impact of bicycle helmet use can be mitigated. Moreover, our findings also provide additional evaluation component when it comes to the cost-benefit assessment of helmet-related laws.

简介：安全是促进可持续城市非机动出行方式（如自行车）的关键因素。头盔已被证明可以有效降低自行车碰撞中的伤害严重程度，但是，它们对骑车人行为的影响仍需要更深入的了解，尤其是在使用共享单车的新兴趋势中。风险补偿效应表明，骑自行车的人可能会通过增加冒险行为来抵消佩戴头盔带来的安全感。更好地理解这些补偿效应有助于评估各种自行车安全相关计划。方法：本研究使用来自旧金山湾区的 131 名骑自行车者的样本，研究骑自行车者在各种城市街道条件下对冒险行为的反应，作为头盔使用的函数。研究参与者每人观看了 12 个视频，这些视频是在加利福尼亚州伯克利拍摄的，从一个骑自行车的人骑在另一个骑自行车的人后面的角度来看。部分因子实验设计用于系统地改变视频中的上下文属性（例如，速度、自行车道设施、路边停车、过往车辆）。在每个视频之后，参与者被要求表明他们是否会超过视频中的自行车手。在数据自适应估计技术的帮助下，应用目标最大似然估计（TMLE）来估计头盔用户和非用户之间的平均风险差异，控制自选择效应。执行基于个体的非参数引导程序以评估与估计器相关的不确定性。结果：我们的研究结果表明，平均而言，更有可能戴头盔的人进行冒险超车的可能性增加 15.6%。实际应用：这项研究并不试图反对强制性头盔法，而是作为一个警示性的警告，即道路安全计划可能需要考虑可以减轻使用自行车头盔的意外影响的策略。此外，我们的研究结果还为头盔相关法律的成本效益评估提供了额外的评估成分。