# Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
## 1 Introduction  
Carsharing is gaining popularity due to its green advantages. Carsharing helps reduce the number of private cars, which alleviates traffic congestion in turn [1]. Carsharing refers to the leasing business in which cars are owned by a carsharing company and used by different users at varying times with measurement by duration and mileage. Unlike traditional offline car rental service and day-by-day billing units, carsharing users register and authenticate on their mobile phones, use applications to find surrounding stations, and rent cars by time [2]. This research analyzes electric carsharing, which is a carsharing that is powered by electricity and has less pollution impact than traditional cars. This research focuses on the types of electric carsharing [3, 4] in which the cars can be fetched and parked at fix or free stations. Fix stations require a certain area to store cars, and the costs include land rent, charger and station construction costs [5, 6]. If the area does not have fix stations, then users can park their cars on the public parking space in a certain area. Different fees of users are charged for parking at varying stations. However, the parking fee is paid by the company. Therefore, for the carsharing company, the free station does not require the cost of station construction, but parking costs will be incurred. The construction of fixed stations is conducive to the management of cars, and free stations are convenient for users to use cars and for reducing the cost of station construction. The combination of two operation forms is conducive to the sustainable development of carsharing. Carsharing is advantageous because it provides great convenience for users and improves the flexibility of cars [7]. [PERSON] [8] analyzed the objective conditions of the new energy carsharing in China and revealed the popularization value and prospects of carsharing in China. [PERSON] [9] analyzed thecurrent development status and future scale of the major car long-term rental, short-term rental, and online car rental markets. These studies are biased toward policy and market analysis. Notably, carsharing has a great future in the transportation field. However, the development of the carsharing industry is still in its early stages. The problems of few stations, few available cars, and few chargers have become key restricting factors of the development of carsharing companies [10]. Some studies have found that carsharing network settings can affect user willingness and company development. [PERSON] [11] used a binary logistic model and showed that the location of stations actually affects potential membership. This research used elastic analysis to find the relationship between distance and number of users but did not introduce specific methods for station location optimization. [PERSON] [12] found that financial losses can be reduced through appropriate choices with respect to the number, location, and size of the depots. This research provided the foundation for the necessity of the station location optimization model. Scholars have conducted the following research to address the problems of carsharing station location optimization. [PERSON] [13] used analytic hierarchy process to calculate the best scheme for carsharing stations. The study identified variables with a significant effect on station location selection but did not build an optimization model for carsharing station location determination. [PERSON] [14] used the interval fuzzy soft set method of risk preference for each carsharing station in Wang Cheng County. The evaluation of the plan has certain reference significance for the location and future planning of carsharing. This method is more suitable for evaluating existing carsharing stations than for planning for new cities. The above-mentioned research methods are relatively subjective. Other methods using mathematical models are presented as follows. [PERSON] [15] illustrated a carsharing locating recharging station model that operates under demand uncertainty.