# Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints
## 2 Data Preprocessing
### Definitions of Variables  
#### 2.2.1 Regional Demand Forecast  
The amount of user demand in the area as needed is determined. First, the total demand for electric carsharing in the region can be obtained through the macro demand forecast. Then, according  
Figure 1: Schematic of the study area meshing. to the population density, land use, and other parameters of each small grid, the total demand for regional electric carsharing is allocated to each small area, and the demand matrix of the entire area can be obtained [24]. In accordance with Equation (1), the total demand of electric carsharing in the area is obtained on the basis of the urban resident population in consideration of the average daily trip volume, travel mode rate, and electric carsharing travel demand. We define the total number of carsharing in a region that is proportional to the user's total carsharing distance divided by the daily carsharing travelable distance. \[M=\frac{\omega\sum\limits_{i=0}^{r}apd}{gv}, \tag{1}\]  
where \(M\) is the total demand number of electric carsharing. \(r\) is the total number of residents. \(i\) is the resident. a is the daily number of trips per capita, (trips/day). \(p\) is the share of electric carsharing that can be acquired by survey data. \(d\) is the average distance traveled by the electric carsharing, (meter), \(g\) is the daily travelable duration of the electric carsharing,(minutes). \(v\) is the average travel speed of the electric carsharing, (meter/minutes). \(\omega\) is the adjustment coefficient, which is related to car redistribution efficiency and car utilization efficiency. After the total demand number of electric carsharing is obtained, the demand for electric carsharing in each small region is calculated. Combined with factors, such as electric carsharing application scenarios and pedestrian flow, this research considers seven indicators, namely, residential population density, population of the campus, job numbers, leisure entertainment venues, tourist attractions, transportation hubs, and ordinary interchange point demand for electric carsharing, to measure the effect of each small area. The demand for electric carsharing \(C_{uv}\) in each small grid is calculated as follows. \[C_{uv}=c_{uv}^{1}+0.5c_{uv}^{2}+c_{uv}^{3}+c_{uv}^{4}+c_{uv}^{5}+c_{uv}^{6}+c_{ uv}^{7}, \tag{2}\]  
where \(v\) is the \(v\)-th column and \(u\) is the \(u\)-th row. We consider several factors that may affect the demand for carsharing in an area. These factors are chosen due to the following reasons. \(c_{uv}^{1}\) is the daily human traffic of residential users and represents the number of potential users and the amount of trips to a certain extent (people volume/day). \(c_{uv}^{2}\) is the daily human traffic of college students who have a high percentage of driving licenses, limited purchasing power, and strong ability to accept new things. Meanwhile, students need to go to the city center for shopping and entertainment because most college campuses are in suburbs. Thus, college students are the important users of electric carsharing (people volume/day). \(c_{uv}^{3}\) is the daily human traffic for jobs. Job places are not only the starting and ending points of commute but also an important node for people traveling during the day. High-density jobs will generate high travel demands (people volume/day). \(c_{uv}^{4}\) is the daily human traffic of the leisure entertainment places. Electric carsharing is often suitable for the purposes of going to the places of leisure entertainment places at night, weekends, and holidays (people volume/day). \(c_{uv}^{5}\) is the daily human traffic of tourist attractions that often attract a large number of tourists from the city. The convenience and privacy of electric carsharing meet their travel needs to stations, hotels, and attractions (people volume/day). \(c_{uv}^{6}\) is the daily human traffic of large-scale transportation hub, which mainly refers to passenger stations, high-speed rail stations, ordinary railway stations, and airports in the city.