## 2 Literature Review  
An extensive body of literature addresses the issue of bike-sharing from various perspectives. This literature can be divided into several groups. The first group deals with the statistics of bike-sharing usage, focusing on system performance, service demand, and its forecast [1]. In this work, some studies describe station-based and dockless or free-floating systems [2; 3]. The implications of implementing dockless systems are discussed in [4]. Many studies focus on specific demands and performance statistics of bike-sharing systems in various cities, such as Shanghai, China [5], or employ various techniques to identify bike-sharing travel patterns [6]. Additionally, comparative studies evaluate system performance across different cities [7]. Several data mining techniques have been employed for analyzing bike-sharing data [6]. Understanding demand is crucial for evaluating passenger groups' acceptance of the bike-sharing system within the context of its use [8]. The influence of the built environment on ridership is explored in studies [9; 10], which investigate the interesting, censored demand for hybrid bike-sharing systems. An innovative approach is presented in the study [11], which analyzed the vertical and horizontal performance equity of bike-sharing systems. Here, horizontal performance represents accessibility within an area, while vertical performance represents accessibility among various user groups. Governance and financing of bike-sharing systems are critical issues, as sustainable operation requires adequate coverage of operating costs. Studies have focused on the governance collaboration necessary for effective bike-sharing management [12]. Performance feedback, derived from raw rental data, is important, but obtaining user satisfaction information is equally vital for system improvement [13]. Another literature group examines inventory management to secure the system's offer. Station-based systems require effective bicycle replenishment management [14], which poses challenges in optimally distributing the bicycle supply. Several methods, including simulation, are used for optimization purposes [15]. A third group of studies explores the benefits of bike-sharing in micromobility and sustainable mobility, analyzing the potential for changing transport behavior to reduce car travel. Additionally, these studies investigate the role of bike-sharing in conjunction with public transport [16]. Some authors have confirmed that bike-sharing can serve as a feeder service for public transport [17]. Further studies compare the significance of bike-sharing in micromobility linked to public transport [18; 19]. One study compares the location of e-scooters to public transport and station-based bike-sharing systems [20]. Bike-sharing is also highlighted as a system that can contribute to sustainability by replacing passenger car use [21], with health benefits described in [22]. In the post-COVID-19 period, several studies have focused on changes in pedestrian behavior and bike-sharing use during the pandemic [23]. These studies often include geographical analyses of bike-sharing system usage during the pandemic, emphasizing the importance of understanding ridership traffic flow to balance demand and supply effectively. Origin-destination data can be obtained from transport card data [24], and the general bike-share feed specification data also aid in understanding these relationships. However, there is a lack of literature specifically addressing bike-sharing system usage for trip purposes. Activities at bike-sharing stations and their modeling have been described in [25], while [26] integrates bike-sharing data with GTFS data. The authors of [27] estimated trip purposes using smart card data and points of interest. In [28], GPS data were used to analyze trip purposes, concluding that morning trips are job-related, while evening trips are primarily home or residential-related. The connection between bike-sharing stations and points of interest has been investigated in various studies [29].