Against the backdrop of the strong market expansion of free-floating bikeshare systems (FFBS), the future of government-funded station-based bikeshare system (SBBS) is a matter of controversy. Merely relying on point density analysis proves to be inadequate in reflecting the flow characteristic, this paper employs a flow clustering analysis to investigate the relationship between SBBS and FFBS. To recognize both bikeshare flow clusters with inhomogeneous density and shape in less time, we propose a two-step network-constrained bivariate flow clustering method that organically combines multiplex-network community detection and bivariate flow clustering method. The performance and applicability of the method in flow clustering detection is exemplified by the SBBS and FFBS systems in Nanjing. The results indicate that though FFBS outnumbers SBBS in terms of bikes, there are still specific flow clusters where SBBS performs better. Spatiotemporal patterns of flow clusters reveal that about one-third of flow clusters dominated by FFBS move from the metro station to the business district during the morning peak, while more SBBS-dominated flow clusters (55.4%) move from the residence to the metro station. Conversely, during the evening peak, flow clusters are observed in the opposite direction. Nevertheless, if the difference in bike numbers is significant, SBBS will be at a disadvantage. It is necessary for SBBS to prioritize resource allocation towards target groups and advantageous areas to prevent decentralized development. Our findings contribute to a more profound comprehension of the interplay between SBBS and FFBS, thereby offering more informed recommendations for strategically aligning the functions of bikeshare systems.

中文翻译：

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自由浮动的共享单车能否取代基于车站的共享单车？从双变量流聚类分析两个共享单车系统之间的关系


在自由浮动共享单车系统（FFBS）市场强劲扩张的背景下，政府资助的基于站点的共享单车系统（SBBS）的未来存在争议。单纯依靠点密度分析不足以反映流动特征，本文采用流动聚类分析来研究SBBS和FFBS之间的关系。为了在更短的时间内识别密度和形状不均匀的共享单车流簇，我们提出了一种两步网络约束的双变量流聚类方法，该方法将多重网络社区检测和双变量流聚类方法有机地结合起来。南京的SBBS和FFBS系统验证了该方法在流聚类检测中的性能和适用性。结果表明，尽管 FFBS 的自行车数量超过 SBBS，但仍然存在 SBBS 表现更好的特定流集群。流簇时空格局显示，早高峰期间，约三分之一以FFBS为主的流簇从地铁站流向商务区，而更多以SBBS为主的流簇（55.4%）从住宅流向地铁站。相反，在晚高峰期间，可以观察到相反方向的人流集群。然而，如果自行车数量差异很大，SBBS 将处于劣势。 SBBS有必要将资源优先配置到目标群体和优势领域，防止分散发展。我们的研究结果有助于更深入地理解 SBBS 和 FFBS 之间的相互作用，从而为战略性调整共享单车系统的功能提供更明智的建议。