Assessing bicycle safety risks using emerging mobile sensing data

作者：Yan Li, Yuyang Zhang, Ying Long, Kavi Bhalla, Majid Ezzati · 发表于：Travel Behaviour and Society · 年份：2024 · DOI：10.1016/j.tbs.2024.100906 · 被引用次数：8 · 研究领域：Impact of Light on Environment and Health、Urban Transport and Accessibility、Human Mobility and Location-Based Analysis

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 Based on this, there's a practical need to re-evaluate the safety and quality of outdated infrastructure.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 ...