Comparative modeling of risk factors for near-crashes from crowdsourced bicycle airbag helmet data and crashes from conventional police data

IF 3.9 2区 工程技术 Q1 ERGONOMICS
Kuan-Yeh Chou, Mads Paulsen, Anders Fjendbo Jensen, Thomas Kjær Rasmussen, Otto Anker Nielsen
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引用次数: 0

Abstract

Introduction: Conventional cycling crash data is valuable for shaping safe cycling environments but has limitations due to the rarity and under-reporting of cycling crashes. However, recent technological developments can provide information from near-crashes. the subheads should be italic, not bf. Also in the Abstract, there shouldn’t be hard return between subheads, the whole section should all run together, so run up any text between subheads. Method: With Metropolitan Copenhagen as a case, this study uses a very large crowdsourced near-crash dataset from Hövding bicycle airbag helmet users and conventional police crash data to model and identify differences in the infrastructure factors influencing rates of crashes and near-crashes in these datasets. Results: In contrast to existing literature, our results show considerable differences in the factors influencing the frequency of crashes and near-crashes. The risk of crashes increases predominantly at intersections and roundabouts, whereas near-crashes are also associated with infrastructure types shared with pedestrians. Conclusion: When used complementarily, crowdsourced near-crash data can enrich the data foundation and help increase the awareness of near-crash-prone infrastructure types necessary for shaping more comprehensive cycling safety policies. Practical Applications: The findings of the study advocate for a broader perspective on cyclist safety, incorporating currently undisclosed near-crash-prone infrastructure types, such as paths shared by cyclists and pedestrians.
基于众包自行车安全气囊头盔数据和传统警察数据的碰撞风险因素比较建模。
传统的自行车碰撞数据对于塑造安全的自行车环境是有价值的,但由于自行车碰撞的稀有性和报告不足而具有局限性。然而,最近的技术发展可以提供来自近乎崩溃的信息。副标题应该是斜体,而不是正体。此外,在摘要中,副标题之间不应该有硬返回,整个部分应该一起运行,所以在副标题之间运行任何文本。方法:本研究以哥本哈根大都会为例,使用来自Hövding自行车安全气囊头盔使用者和传统警察碰撞数据的大型众包近碰撞数据集,对这些数据集中影响碰撞和近碰撞率的基础设施因素进行建模并识别差异。结果:与现有文献相比,我们的研究结果显示,影响碰撞和接近碰撞频率的因素存在相当大的差异。碰撞风险主要在交叉路口和环形交叉路口增加,而与行人共享的基础设施类型也与接近碰撞有关。结论:当互补使用时,众包近碰撞数据可以丰富数据基础,并有助于提高对易发生近碰撞的基础设施类型的认识,这对于制定更全面的自行车安全政策是必要的。实际应用:该研究的结果提倡从更广泛的角度来看待骑自行车的人的安全,包括目前未公开的易发生碰撞的基础设施类型,例如骑自行车者和行人共用的道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.40
自引率
4.90%
发文量
174
审稿时长
61 days
期刊介绍: Journal of Safety Research is an interdisciplinary publication that provides for the exchange of ideas and scientific evidence capturing studies through research in all areas of safety and health, including traffic, workplace, home, and community. This forum invites research using rigorous methodologies, encourages translational research, and engages the global scientific community through various partnerships (e.g., this outreach includes highlighting some of the latest findings from the U.S. Centers for Disease Control and Prevention).
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