# Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
## 1 Introduction
The research developed a demand forecasting method that allows the generation of many demand scenarios. [PERSON] [16] formulated a mixed queuing network model for the joint design of fleet size and station capacities. The optimization problem was solved by the genetic algorithm. They proved that the profit is maximized when the existing road congestion is moderate. This research mainly considered the carsharing model of the fixed station but ignored the setting of free stations. [PERSON], [PERSON] [17] researched the travel area and behavior of free-floating carsharing users. This study mainly considered the operation model of free-floating carsharing but ignored the setting of fixed stations. [PERSON] [18] proposed a method for optimizing the location of charging stations for one-way electric carsharing systems. The objective function was to maximize the profit of carsharing service. Simulations were performed to prove the effectiveness of the research method. The final station was optimized by 0-1 variable station selection among existing candidate sites. However, the study failed to provide scientific theoretical basis for the selection of the alternative station. [PERSON] [19] analyzed eight spatial elements related to carsharing location. A model was established to determine the optimal carsharing locations with the minimum total distance between carsharing users. This model took the minimum travel cost of the users as the optimization goal, which optimized the station location based on fixed station operations. [PERSON] [20] designed a genetic algorithm to solve the problem with the least total change and the smallest gap between supply and demand. The model results obtained the adjustment of the number of cars at each station after optimization. This model is more suitable for the scale expansion of a city that already has carsharing than for the location optimization of a new city. In this research, the electric carsharing location and car allocation model of fixed and free stations are considered first. This way improves not only the efficiency of electric carsharing management but also the convenience of user demands. Second, this study uses the grid division method for area discretization, which improves the shortcomings of random optimization of alternative stations based on alternative station optimization. To the best of the authors' knowledge, this study is the first to integrate the above-mentioned method to carsharing location method. Electric carsharing is an asset-heavy and high-risk industry. Networking is the development trend. The electric carsharing station network is not only crucial to the company's profitability but also significant for the development of urban transportation systems [21]. First, electric carsharing network is difficult to alter after completion due to its large investment. The geographical location of the network directly determines the network location and economic benefits of the company. Second, the current charging, parking space, and car license plate resources in the industry are limited. The location of the stations determines whether these resources can be fully utilized for reducing costs [22]. This research analyzes the optimization model of the location of electric carsharing networks. The optimization model and application scenarios of electric carsharing are combined to determine the affecting factors of electric carsharing network station location. The objective function is to meet the maximum user demand with the limited cost of the company as the constraint while considering factors, such as land rent, parking prices, and the number of chargers. An optimization model for the location of electric carsharing is established. Then, the location plan of the network in the research area is obtained using genetic algorithm by analyzing the established nonlinear integer programming model. Finally, a case study is conducted. The results indicate that this research can improve the coverage ratio of user demand and the convenience of users.