## 5 Discussion  
From the perspective of shared bicycles, the system must be actively used by residents commuting to work, schools, and for other purposes. Therefore, it is essential to have a detailed understanding of the purposes of their trips, which can significantly contribute to a better understanding of how the system is used by different user groups [46]. This insight is valuable as it can provide the necessary information to optimize the system and ensure its more efficient operation. Additionally, it is possible to model users' choices and preferences, which allows for better prediction of their behavior and needs [47]. Usage data can then contribute to improving the service offering or optimizing the existing service, thereby increasing user satisfaction and system efficiency [48]. Another important aspect is the financing of the system. The method of financing plays a significant role not only in ensuring the smooth operation of the system but also in its long-term sustainability and development [49]. Without adequate funding, it would be difficult to maintain high-quality services and ensure their availability to all users. Some sustainable mobility systems are also supported by European Union funding schemes [50]. These schemes aim to support the development and implementation of innovative solutions in urban mobility, thereby contributing to the reduction in emissions and the improvement of quality of life in cities. The impact on modal shift, i.e., the change in transportation preferences among different modes of transport, was thoroughly presented in the study [51]. The limitations of this study are as follows: as indicated by its title, the authors focused on estimating the purposes of trips presumed to have been made by bike-sharing users. However, this approach may be limited as it does not consider all the factors that can influence the choice of transport mode. Moreover, another limitation is the sample size; future research should focus more on reaching a larger group of respondents. For a more detailed determination of trip purposes, it would be necessary to combine system usage data with databases where users directly indicate the purpose of their trips [52]. Such an approach would allow for more precise analyses and a better understanding of bike-sharing users' behavior. The authors of the study also did not evaluate the economic costs and the role of price in using the system, which could reveal potential differences in the behavior of various user groups [53]. It is known that regular users have different habits and preferences compared to occasional users of the system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Observed**} & \multicolumn{4}{c}{**Predicted**} \\ \cline{2-5}  & **Relax** & **School** & **Job** & **Percent Correct** \\ \hline Relax & 7 & 19 & 20 & 15.20\% \\ \hline School & 3 & 45 & 7 & 81.80\% \\ \hline Job & 5 & 4 & 52 & 85.20\% \\ \hline Overall Percentage & 9.30\% & 42.00\% & 48.80\% & 64.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification. Interesting findings also pertain to usage statistics, such as comparisons between immigrants and domestic residents [54]. These differences can significantly impact the planning and provision of bike-sharing services. Based on the availability of individual user data, it is possible to identify trends in bike-sharing usage during different periods, whether during the week or on weekends [55]. These trends are valuable for optimizing the network of stations and managing system capacity to ensure its effective operation. Some authors present specific schemes for the sustainable operation of bike-sharing [56]. Combining user and cost data makes it possible to make sustainability estimates for bike-sharing. These estimates can help identify which aspects of the system are most efficient and where investments are needed to ensure its long-term viability and development. The study found that the majority of bike-sharing users were men, comprising over 60% of the total sample.