Determining bike-sharing usage patterns and their explanatory factors on demand is essential for the effective and efficient operation of bike-sharing systems (BSSs). Most BSSs provide different passes that vary with the period of use. However, studies investigating the differences in usage patterns are rare compared to studies conducted at the system level, even though explanatory factors depending on the type of pass may cause different characteristics in terms of usage patterns. This study explores the differences in the usage patterns of BSSs and the impact of explanatory factors on the demand depending on the type of pass. Various machine learning techniques, including clustering, regression, and classification, are used, in addition to basic statistical analysis. As observed, long-term season passes of over six months are mainly used for transportation (especially commuting), whereas one-day or short-term season passes seem to be used more for leisure than for other purposes. Furthermore, differences in the purpose of bike rentals seem to cause differences in usage patterns and variations in demand over time and space. This study improves ther understanding of the usage patterns that appear differently for each pass type, and provides insights into the efficient operation of BSSs in urban areas.

根据需要确定自行车共享使用模式及其解释因素对于自行车共享系统（BSS）的有效和高效运行至关重要。大多数 BSS 提供不同的通行证，这些通行证随使用期限的不同而变化。然而，与在系统级别进行的研究相比，调查使用模式差异的研究很少，尽管取决于通行证类型的解释因素可能会导致使用模式方面的不同特征。本研究探讨了 BSS 使用模式的差异以及不同通行证类型的解释因素对需求的影响。除了基本的统计分析之外，还使用各种机器学习技术，包括聚类、回归和分类。据观察，六个月以上的长期季票主要用于交通（尤其是通勤），而一日或短期季票似乎更多用于休闲而不是其他用途。此外，自行车租赁目的的差异似乎会导致使用模式的差异以及需求随时间和空间的变化。这项研究加深了人们对每种通行证类型不同的使用模式的理解，并为城市地区 BSS 的高效运营提供了见解。