Faculty of Health Sciences, Simon Fraser University, Burnaby, V5A 1S6, Canada.
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, 85281, USA.
Accid Anal Prev. 2020 Sep;145:105695. doi: 10.1016/j.aap.2020.105695. Epub 2020 Jul 30.
With only ∼20 % of bicycling crashes captured in official databases, studies on bicycling safety can be limited. New datasets on bicycling incidents are available via crowdsourcing applications, with opportunity for analyses that characterize reporting patterns. Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org. We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal, trip, route, and crash characteristics. We used a balanced random forest classifier to classify three outcomes: (i) collisions resulting in injury requiring medical treatment; (ii) collisions resulting in injury but the bicyclist did not seek medical treatment; and (iii) collisions that did not result in injury. Results indicate the ranked importance and direction of relationship for explanatory variables. By knowing conditions that are most associated with injury we can target interventions to reduce future risk. The most important reporting pattern overall was the type of object the bicyclist collided with. Increased probability of injury requiring medical treatment was associated with collisions with animals, train tracks, transient hazards, and left-turning motor vehicles. Falls, right hooks, and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment, and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries. In Victoria, British Columbia, Canada, bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks. Our findings support previous research using hospital admissions data that demonstrate how non-motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions. Crowdsourced data have potential to fill gaps in official data such as insurance, police, and hospital reports.
由于官方数据库中仅记录了 ∼20%的自行车事故，因此有关自行车安全的研究可能会受到限制。通过众包应用程序可以获得新的自行车事故数据集，从而有机会对报告模式进行分析。我们的目标是分析 BikeMaps.org 众包自行车事故报告中的受伤模式。我们从 BikeMaps.org 全球绘图平台上提取了 281 起报告的事故，并分析了 21 个解释变量，这些变量代表个人、行程、路线和事故特征。我们使用平衡随机森林分类器对三种结果进行分类：（i）导致需要医疗治疗的伤害的碰撞；（ii）导致受伤但自行车手未寻求医疗治疗的碰撞；以及（iii）未导致伤害的碰撞。结果表明，解释变量的重要性和关系方向是平衡的。通过了解与受伤最相关的条件，我们可以针对干预措施来降低未来的风险。总体上最重要的报告模式是自行车手与之发生碰撞的物体类型。与动物、火车轨道、临时危险物和左转机动车碰撞与需要医疗治疗的伤害的可能性增加有关。摔倒、右钩和车门碰撞与自行车手受伤但未寻求医疗治疗的事故有关，而与行人以及经过的机动车发生冲突与无伤害的轻微碰撞有关。在加拿大不列颠哥伦比亚省维多利亚市，通过增加支持安全左转和绕过火车轨道的基础设施，可以提高自行车安全水平。我们的发现支持了先前使用医院入院数据的研究，这些研究表明非机动车辆事故如何导致自行车手受伤，以及路线特征和条件是自行车碰撞的因素。众包数据有可能填补官方数据（例如保险、警察和医院报告）中的空白。
