Assessing bicycle safety risks using emerging mobile sensing data

IF 5.1 2区 工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-09-19 DOI:10.1016/j.tbs.2024.100906
Yan Li , Yuyang Zhang , Ying Long , Kavi Bhalla , Majid Ezzati
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Abstract

The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there’s a need to reassess the risk factors associated with multiple bike lane users. Based on this, there’s a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

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利用新兴移动传感数据评估自行车安全风险
全球电动自行车保有量的激增给自行车基础设施带来了巨大压力。从理论上讲,有必要重新评估与多个自行车道使用者相关的风险因素。在此基础上,实际需要重新评估过时基础设施的安全和质量。本文旨在结合电动自行车与传统自行车共用自行车道的情况,重新考虑与自行车基础设施安全相关的风险因素。此外,许多国家缺乏有关自行车基础设施的精确空间数据。本研究介绍了一种基于自行车的移动传感方法,旨在以低成本、高效率和大规模的方式获取白天和夜间自行车道数据集。建立了基于计算机视觉的自行车风险因素评估模型,并对自行车安全风险因素的分布进行了视觉分析。研究数据采集自北京市具有代表性的 59.5 公里自行车道区域。结果证实了电动自行车激增的重大影响,电动自行车使用者占骑车人的 72.1%,戴头盔的占 32.3%,逆行的占 8.4%。在白天,排名最高的风险因素包括自行车道的类型(半数没有专用车道或共用车道)、路边停车以及路况不佳。夜间,街道照明不足也是值得关注的问题。该研究方法易于复制,可推广到新的多用户共存自行车环境或没有自行车空间数据的国家,为自行车安全政策和道路设计提供启示。
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
9.80
自引率
7.70%
发文量
109
期刊介绍: Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.
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