Many cities around the world have adopted dockless bike-sharing programs with the hope that this new service could enhance last-mile public transit connections. However, our understanding of the travel patterns using dockless bike sharing is still limited. To advance the knowledge on the new service, this study investigates mobility patterns of dockless bike sharing in Singapore using a four-month dataset. An exploratory spatiotemporal analysis is conducted to show daily travel patterns, while community detection of networks is used to explore the spatial clusters emerged from cycling behaviors. A series of Poisson regression models are then estimated to characterize the generation, attraction and resistance factors of bike trips in different periods of a day. The proposed regression model, which considers built environment factors of origin and destination simultaneously, is proved to be effective in deciphering mobility. The empirical findings shed light on policy implications in sustainable transportation planning.

世界上许多城市都采用了无桩共享单车计划，希望这项新服务能够加强最后一英里的公共交通连接。然而，我们对使用无桩共享单车的出行模式的了解仍然有限。为了提高对新服务的了解，本研究使用为期四个月的数据集调查了新加坡无桩共享单车的移动模式。进行探索性时空分析以显示日常出行模式，同时使用网络社区检测来探索骑自行车行为产生的空间集群。然后估计一系列泊松回归模型来表征一天中不同时段自行车旅行的产生、吸引力和阻力因素。建议的回归模型，它同时考虑了起点和目的地的建成环境因素，被证明在破译流动性方面是有效的。实证结果阐明了可持续交通规划的政策含义。