Many municipalities aim to support the uptake of cycling as an environmentally friendly and healthy mode of transport. It is therefore crucial to meet the demand of cyclists when adapting road infrastructure. Previous studies researching cyclists’ route choice behavior deliver valuable insights but are constrained by laboratory conditions, limitations in the number of observations, or the observation period or relay on specific use cases. The present study analyzes a dataset of over 450,000 observations of cyclists’ routing settings for the navigation of individual trips in Berlin, Germany. It therefore analyzes query data recorded in the bike-routing engine BBBike and clusters the many different user settings with regard to preferred route characteristics. Results condense the large number of routing settings into characteristic preference clusters. Compared with earlier findings, the big data approach highlights the significance of short routes, side streets and the importance of high-quality surfaces for routing choices, while cycling on dedicated facilities seems a little less important. Consequentially, providing separated cycle facilities along main roads – often the main focal point of cycle plans – should be put into the context of an integrated strategy which fulfills distinct preferences to achieve greater success. It is therefore particularly important to provide a cycle network in calm residential streets as well as catering for short, direct cycle routes.

中文翻译：

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通过分析来自自行车路线引擎的大量用户设置数据来评估骑自行车者的路线偏好


许多城市都致力于支持自行车作为一种环保且健康的交通方式。因此，在改造道路基础设施时满足骑行者的需求至关重要。先前研究骑行者路线选择行为的研究提供了有价值的见解，但受到实验室条件、观察数量的限制、观察周期或特定用例的限制。本研究分析了超过 450,000 个骑行者路线设置观察数据集，用于德国柏林个人出行导航。因此，它分析自行车路线引擎 BBBike 中记录的查询数据，并根据首选路线特征对许多不同的用户设置进行聚类。结果将大量路由设置压缩为特征偏好簇。与早期的研究结果相比，大数据方法强调了短路线、小巷的重要性以及高质量路面对于路线选择的重要性，而在专用设施上骑自行车似乎不太重要。因此，沿着主要道路提供独立的自行车设施（通常是自行车计划的主要焦点）应该纳入综合战略的背景下，以满足不同的偏好，以取得更大的成功。因此，在平静的住宅街道上提供自行车网络并提供短途、直接的自行车路线就显得尤为重要。