Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide
and play an important role in many urban transportation systems. BSSs alleviate
congestion, reduce pollution and promote physical exercise. It is essential to
explore the spatiotemporal patterns of bike-sharing demand, as well as the
factors that influence these patterns, in order to optimise system operational
efficiency. In this study, an optimised geo-temporal graph is constructed using
trip data from Moby Bikes, a dockless BSS operator. The process of optimising
the graph unveiled prime locations for erecting new stations during future
expansions of the BSS. The Louvain algorithm, a community detection technique,
is employed to uncover usage patterns at different levels of temporal
granularity. The community detection results reveal largely self-contained
sub-networks that exhibit similar usage patterns at their respective levels of
temporal granularity. Overall, this study reinforces that BSSs are
intrinsically spatiotemporal systems, with community presence driven by
spatiotemporal dynamics. These findings may aid operators in improving
redistribution efficiency.

自行车共享系统是全球超过一千个城市中部署的，对许多城市交通系统起着重要作用。本研究利用摩比自行车的出行数据构建了一个优化的地理时空图，揭示了未来共享系统扩展时的最佳站点位置。使用路汶算法进行社区检测，发现不同时间粒度上呈现类似使用模式的自包含子网络。整体来看，本研究加强了自行车共享系统是物理时空系统的内在特性，并且社区的存在受到时空动态的驱动。这些发现可能有助于运营商提高再分配效率。