## 1 Introduction  
Residents carry out their activities in different territories using various transport systems, each with its advantages and disadvantages from a transportation perspective. Among these systems, bike-sharing has gained significant traction globally as cities strive to enhance sustainable and active mobility conditions. Bike-sharing systems promote environmental benefits and provide a flexible and healthy alternative to traditional transportation methods.  
Currently, various types of data on the use of bike-sharing systems are available. Traditional data sets include the number of rentals at stations, which offer a basic understanding of usage patterns. More comprehensive data sets provide information on the origins and destinations of users, allowing for a deeper analysis of travel behavior. In some advanced systems, data on the specific routes taken by users are also available. However, a significant challenge remains since the data are anonymous, and it is difficult to determine the specific purposes for which the bike-sharing system is used. Understanding these purposes is crucial for optimizing system operations and enhancing user satisfaction.  
This study addresses this challenge by presenting an approach to estimating the purposes of bike-sharing rides using multinomial logistic regression. This method allows for a sophisticated analysis of the factors influencing trip purposes, providing insights that can inform policy and practice.  
The importance of understanding the trip purposes of bike share users for optimizing the operation and efficiency of bike share systems is very important. Trip purposes provide valuable insights into how different user groups use the system, which can have a direct impact on site planning, system design, and service offerings. Determining trip purpose assists in optimizing system operations by matching bicycle availability to peak demand periods, such as commute hours. In addition, recognizing the key factors influencing user decisions allows service providers to tailor their offerings and increase user satisfaction. For example, focusing on the needs of commuters can improve route planning and station locations.  
The data for this research were obtained from a survey on using the bike-sharing system. These data provide a rich source of information for analyzing usage patterns and inferring trip purposes.  
To estimate the purposes of bike-sharing trips, we employed multinomial logistic regression, a powerful statistical method suitable for handling categorical dependent variables with more than two categories. This method allows us to model the probability of each trip purpose (e.g., commuting, recreational, attending school) based on various predictor variables such as social status, bike-sharing service, and average distance.  
The study provides a detailed overview of the current state of bike-sharing systems, including their benefits and challenges. The literature review summarizes the existing literature on bike-sharing and its impact on urban mobility.  
The methodology describes data collection methods and the application of multinomial logistic regression for trip purpose estimation.  
The discussion highlights the patterns of bike-sharing use and the estimated purposes of trips, providing insights into how different user groups utilize the system and identifying key factors that influence trip purposes.  
By examining the available data and applying analytical methods, this study aims to shed light on the various purposes for which bike-sharing systems are utilized. The insights gained from this research can help city planners and service providers tailor their offerings more effectively, improving bike-sharing systems' efficiency and user satisfaction.