With the global rise of bike-sharing programs (BSP), planners and traffic experts have raised concerns as to whether the rapid growth of BSP ensures cycling safety. Despite numerous studies on built environments encouraging bike usage, there is limited knowledge whether streetscape environments around BSP stations affect bicycle accidents. We address this gap by investigating the relationships between various built environments and bicycle accidents around BSP stations, with a particular focus on the moderating effects of comprehensive streetscapes. Streetscape environments, estimated through semantic segmentation and k-means clustering, were used in two-level negative binomial regression models to clarify how street- and station-level environments affect different types of bicycle accidents. The findings indicate increased crash likelihoods on higher-speed roads, streets with traffic facilities, in proximity to public transportation infrastructure, and specific streetscape types. In particular, streetscape features like green oasis streets and open-sky roadways positively contribute to mitigating the negative effects of traffic facilities, such as bike networks and crosswalks, on cycling safety. This study aids in developing comprehensive strategies and guidelines to retrofit built environments for better traffic safety and thereby promote urban cycling.

中文翻译：

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使用街景图像和深度学习技术研究建筑环境对自行车共享计划站周围自行车事故的影响：街景特征的调节作用


随着自行车共享计划（BSP）在全球的兴起，规划者和交通专家对 BSP 的快速增长是否能确保骑行安全提出了担忧。尽管对鼓励自行车使用的建筑环境进行了大量研究，但对于 BSP 车站周围的街景环境是否会影响自行车事故的了解有限。我们通过调查 BSP 车站周围各种建筑环境与自行车事故之间的关系来解决这一差距，特别关注综合街道景观的调节作用。通过语义分割和 k 均值聚类估计的街景环境被用于二级负二项式回归模型，以阐明街道和车站级环境如何影响不同类型的自行车事故。研究结果表明，高速道路、有交通设施的街道、公共交通基础设施附近以及特定街道景观类型的碰撞可能性增加。特别是，绿洲街道和露天道路等街景特征有助于减轻自行车网络和人行横道等交通设施对骑行安全的负面影响。这项研究有助于制定全面的战略和指南，以改造建筑环境，以提高交通安全，从而促进城市自行车运动。