# Analysis and Prediction of Carsharing Demand Based on Data Mining Methods
## 1 Introduction  
The basic concept of carsharing has existed for many years. A fleet of vehicles can be shared by several users, who can drive the vehicle when they need it, but they do not have to buy one themselves [1]. Compared with private vehicles, carsharing can offer the benefits of private vehicles without the costs and responsibilities of ownership [2]. It meets the individual demand of residents, such as leisure travel, business trips, and visiting friends. Carsharing can improve the utilization rate of resources and reduce the willingness of users to buy vehicles, thereby effectively alleviating the problems of environmental pollution, traffic congestion and excessive energy consumption. However, the development of the carsharing industry is immature, which faces many problems. Carsharing can be divided into station-based type and free-floating type. Unlike station-based carsharing, free-floating carsharing is not restricted by stations, and vehicles can be picked up and returned at will [3]. However, station-based carsharing also has its advantages. For example, operators can better manage vehicles and save human resources. Station-based carsharing is divided into one-way and round-way. In one-way carsharing, a general imbalance in vehicle demand exists at each station due to different pick-up and return stations, which is coupled with the uncertainty of users' travel demand. Issues include the inability of users to return a vehicle because of insufficient parking space and the inability of users to pick up a vehicle at an available station, which will lead to a series of problems. The user experience is so bad that it affects the profitability of operators. Therefore, the travel demand characteristics of users need to be analyzed, thus enabling the demand of users to be predicted, which can not only reduce the imbalance of vehicles at the stations but also meet the demands of users. Fully understanding the travel characteristics of carsharing users is the key to predicting user demand. For the analysis of user travel characteristics, scholars have conducted various studies. With regard to factors that affect users' travel behavior, [PERSON] et al. usedtransaction data provided by a carsharing operator in Seoul, South Korea, and performed multiple linear regression modeling with the number of carsharing transactions as a dependent variable and with three groups of independent variables: environment, demographic, and transportation variables [4]. [PERSON] et al. [5], [PERSON] et al. [6], and [PERSON] at al. [7] conducted statistical analysis on users' travel behavior through questionnaires and found that users' travel behavior was influenced by many factors such as age, gender, and income. To study the influence of personal preference on the evolution of route flow, [PERSON] et al. established a dynamic model of individual choice of routes [8]. The user's personal preference was introduced to constantly update the model rules. The result showed that subjective and objective psychological factors will affect travelers' choice of travel route. In terms of user travel characteristics, [PERSON] et al. used German carsharing order data to analyze the spatiotemporal characteristics of user demand [9]. The date (day) of order distribution in the same space was clustered to explore the long-term factors and short-term factors that affect demand. [PERSON] et al. analyzed the dynamic changes of platform vehicle supply on different periods of working days and non-working days and identified the demand contradiction period [10]. The spatial travel characteristics of users were analyzed, and results showed that the users' demand for network vehicle travel was obviously different between the morning peak and the evening peak of working days. [PERSON] and [PERSON] analyzed the correlation between the supply of public transportation and the carsharing demand using the travel data of transportation cards and the trajectory data of carsharing in Seoul, South Korea [11]. [PERSON] et al.