1. Introduction
Most cities currently experience the “high-density gathering” phenomenon. Infrastructure for urban transportation cannot be developed at the same rate as the rising demand for transportation. According to the Ministry of Public Security, China had owned 319 million cars by the end of December 2022, accounting for 76.59% of all motor vehicles, with 464 million people driving those cars (
http://www.gov.cn/shuju/2023-01/11/content_5736278.htm accessed on 12 April 2023). Congestion in traffic is becoming worse and worse. The average road network peak travel delay index for China’s 50 largest cities was 1.609 in 2020, and it had climbed to 60% by 2021. Only 2% of locations affected by the new coronavirus epidemic saw an increase in traffic in 2022 (
https://report.amap.com/index.do accessed on 12 April 2023). Overall, though, traffic congestion will only become worse as pandemic control becomes more commonplace. A driver’s license has grown more rapidly than automobile ownership, so car ownership will likely expand more rapidly in the future. In addition, the growing fleet of urban fuel-powered vehicles significantly threatens both traffic congestion and urban air pollution due to the high exhaust emissions. It is crucial that shared new energy vehicles are made more widely available for the city’s development to be healthy and sustainable. Shared new energy vehicles are electric vehicles that consumers use on the appropriate platform over the course of a brief rental period. These vehicles not only promote environmentally friendly transportation for locals and address the long-standing issue of traditional vehicles’ carbon emissions, but they can also effectively utilize human resources, conserve urban land resources, and reduce traffic congestion. It has the inherent benefits of significant development and is one of the primary fields of the future sharing economy against the backdrop of the internet.
Shared new energy vehicles are becoming increasingly integrated into people’s daily mobility, from short-term rental sharing services such as Airbnb and Uber to the introduction of apps such as Panda Car and EVCARD. Although the market for shared new energy electric vehicles has grown quickly recently, there are unavoidably challenging operational issues. Due to issues with high startup and operating costs as well as limited land resources, pertinent businesses must plan their development. Consumers’ unsatisfied with the user experience caused by “difficult to find, park, and charge a car” (Wei et al., 2020 [
1]) decreases their inclination to use, which inhibits the development of shared new energy cars. A few businesses, including EZZY and others, have shut down, Car2Go has left the Chinese market, and Panda is experiencing operational issues.
Therefore, the “G-B-U” triangle theoretical framework based on the “stakeholder—quasi-public goods” perspective is constructed in this study. This paper analyzes the factors that influence the location choice of shared new energy vehicles based on geographic location, enterprise cost and benefit, user demand, government policy, and other factors, which corrects the shortcomings of previous studies that are primarily based on the single dimension of economic benefits. In addition to providing a comprehensive integration of urban transportation, low-carbon environmental protection, and resident convenience, this paper offers a systematic analysis framework for the location deconstruction and industrial layout of urban shared new energy vehicles and theoretically advances the research on the integration of the sharing economy, urban economy, logistics site selection, and public management. Based on this, this paper uses the P-median model to optimize the location selection of urban shared electric vehicles in three districts, which are Gulou, Yunlong, and Quanshan in Xuzhou, a city in the Jiangsu province of China. It hopes to serve as a reference for the scientific location selection of urban shared new energy electric vehicles.
2. Research Status
Now, the creation of location models and influencing factors, as well as the use intention of consumers of shared new energy vehicles, are the key areas of domestic and international researchers’ research on shared new energy vehicles.
Based on social and economic aspects, researchers examined the variables influencing the demand for car-sharing. Sun Lishan et al. (2020) [
2] developed the demand prediction model of the land use index and extensively examined the relationship between the demand for car-sharing and the distribution traits of the nearby land use. Pucci Paola (2021) [
3] researched how metropolitan spatial patterns affect consumer demand for electric vehicles. Using 13 variables, including journey features, personal attributes, and perceived attributes, Zhang Ronghua et al. (2022) [
4] conducted a quantitative analysis of the factors influencing consumers’ use of shared electric vehicles based on the theory of planned behavior. The empirical findings revealed that the most significant aspects influencing travelers’ attitudes toward electric vehicles were whether the charge was affordable, the trip was comfortable, and the process of renting was simple. Their intention to use is significantly influenced by subjective norms, attitudes, perceived risk, and control practices. The extended technology acceptance model “belief-attitudinal-intention” was used by Jaiswal et al. (2021) [
5] and other researchers to demonstrate how attitudes, perceptions of utility, usability, and risk can either directly or indirectly influence an individual’s decision to adopt an electric car. The one with the biggest effect on consumers’ use intentions is perceived usefulness. Lee et al. (2020) [
6] optimized the design of the SAEV system with the goal of minimizing total cost and satisfying customer waiting time. Young individuals, those with low and intermediate incomes, those with higher education levels, and others are more likely to join car-sharing, as noted in studies by Huang Youlin (2021) [
7]. These articles have made great contributions to guiding the operation and development of car-sharing.
Urban sharing new energy cars are a very convenient new form of transportation for locals. However, as it grows, it also encountered a few issues, such as a lack of parking spaces, insufficient charging piles, hazards to automobile safety, high operating costs of businesses, a lack of funding, and ambiguous legal obligations (Wei et al., 2020 [
8]; Gao, 2020 [
9]; Wang et al., 2020 [
10]). According to Zhang Jingwen (2020) [
11], to understand the operation and management style of local car-sharing enterprises and compare the degree of compatibility between the operation strategy of the enterprises and the current situation of the city, comprehensive factors such as urban traffic, residents, and enterprises should be investigated and analyzed. Researchers are looking at the placement choice of shared automobiles from a variety of angles, including those of businesses, users, power grids, and overall social benefits, in an effort to break the shared new energy vehicle development bottleneck. From an enterprise’s perspective, building a location model is primarily focused on minimizing costs and maximizing profits. To calculate the ideal quantity, capacity, location, and development time of EV parking lots in a distribution system, Mirzaei M. J. and Kazemi A. (2020) [
12] used a dynamic programming approach. Their findings demonstrate that the dynamic programming strategy they provide can assist businesses in maximizing revenues while taking system and financial constraints into account. Based on the user utility maximization perspective, Guo et al.’s (2018) [
13] study on the location of charging stations considered consumers’ distance anxiety and deviation. A model created by Zenginis et al. (2016) [
14] and validated through a simulation enabled parking charging stations to service more clients without lengthening queue wait times. Based on the grid level, Li Ran et al. (2021) [
15] created an optimal planning model and solved it with the goal of reducing network losses and voltage shifts in the distribution system. From the perspective of system management, the key requirement of a location optimization system is high energy efficiency and low cost (Ghorpade et al., 2021) [
16]. To reduce the overall societal cost, Liu Haoxiang et al. (2021) [
17] developed a “two-layer model” based on the charging procedure, driving route, and charging behavior. They also concentrated on the ideal location of EV charging facilities.
Previous research has examined customer demand preferences, network selection modeling techniques, and the effect factors related to the location selection of shared new energy electric vehicles. However, most current studies concentrate on one-dimensional scattered analysis, and there is currently no theoretical direction or organized framework for shared automobile placement (Mirzaei et al., 2020 [
12]; Li et al., 2021 [
18]). As a result, this study develops an analytical framework of the “G-B-U” theory based on the “stakeholder—quasi-public goods” perspective, which provides a theoretical framework for the examination of the variables that affect the market demand for shared new energy vehicles and the selection of the parking lots. In addition, the earlier models for predicting market demand were primarily built using straightforward demographic data. An in-depth investigation is used in this work to attempt to assess the elements that affect the consumption and use of urban shared new energy vehicles. A market demand prediction model for these vehicles is built based on two dimensions of social demographic characteristics and user intentions, particularly the relationship between shared new energy vehicle usage experience on subsequent use intentions. This approach significantly increases the accuracy of market demand prediction for urban shared new energy vehicles when compared with the prior prediction model based solely on population size and density.
3. Construction of Theoretical Analysis Framework for Shared New Energy Vehicles
3.1. The Quasi-Public Product Attributes of Shared New Energy Vehicles
There are three ways to supply modern public goods: independent supply by the government, purchase of public services by the government, and supply by multiple subjects (Yang, 2013 [
19]). However, whether the supplier is the public sector or the private sector cannot be used as a basis for judging quasi-public goods. It is about whether the product on offer has the characteristics of non-competitive and non-exclusive, which are the most prominent feature of public goods. However, competitive and segmented quasi-public products are not exclusive. Shared transportation is a vital component of contemporary urban transportation and includes typical features of quasi-public goods. Consumption of shared bikes is non-competitive if the availability of shared bikes in a given area of a city is sufficient to meet user demand, the use of shared bikes by one consumer does not reduce the use of shared bikes by other consumers, and the marginal cost of the shared bike business enterprise to provide shared bikes to another user is zero (Guo, 2019 [
20]). Nonetheless, shared bikes exhibit some exclusivity and competitiveness of private goods when market demand exceeds supply quantity in a particular area of the city, which is considered to be limited and non-competitive for shared bikes.
Drawing on the above analysis, shared new energy vehicles have the typical indivisibility but also have the limited non-competitiveness of quasi-public goods. Therefore, this paper considers that urban shared new energy vehicles are quasi-public transportation products under the background of sharing economy.
3.2. Analysis of Stakeholders of Sharing New Energy Vehicles
As a quasi-public good, the site selection of urban shared electric vehicles is affected by many factors from the perspective of the main stakeholders involved, including the government, businesses, and users. Any urban transportation facility’s modernized development is inextricably linked to the government’s strategic planning and direction. The cost and profitability of new energy vehicle companies, which serve as the operating unit for urban sharing new energy cars, are also major determinants of how these vehicles will be developed and how they will be laid out in the city. Urban sharing new energy vehicle development planning and network layout, whether motivated by governmental governance or corporate profit, ultimately satisfy market demand, notably the travel requirements of urban residents and users. Thus, the above three stakeholders must be prioritized in order to examine the network layout and placement of urban sharing new energy vehicles.
3.3. Construction of Theoretical Analysis Framework for Location Selection of Urban Shared New Energy Vehicles
The aforementioned section summarizes the quasi-public product characteristic of city-sharing new-energy cars and its important constituents, namely governments, business, and users. This serves as the theoretical foundation for this paper’s construction of the “G-B-U” analysis framework, which is used to analyze the variables influencing the supply and layout of these vehicles. The government (location characteristics and policy regulations), operation platform enterprises (costs and advantages), and users (demands) will all be thoroughly examined in this framework as elements impacting the location choice of new urban energy vehicles.
4. Influencing Factors of Choosing the Location of New Energy Electric Vehicles
Based on the above theoretical analysis, this paper mainly considers the main factors affecting the location selection of urban shared new energy electric vehicles from the perspectives of government, businesses, and users.
4.1. Location Factors
Yang Dingwu (2017) [
21], Luo Dong (2018) [
22], and Liu Guohua (2021) [
23] found in their research that location characteristics are very important influencing factors in the location selection of logistics centers, commercial hotels, and shared bikes, which mainly have the following specific influences.
First, the location of the parking lots of shared new energy vehicles for urban transportation should be customized to the overall design and development of the city. The network’s location should be convenient for switching between different modes of transportation on the one hand while avoiding encroaching on public transportation road resources and the already congested urban parking lots on the other.
Second, the placement of the shared new-energy cars directly affects the company’s operating profit. In densely populated places, such as shopping malls, schools, office buildings, and residential communities, the demand and frequency of sharing new energy vehicles are higher (
China Journal of Highway and Transport, 2017 [
24]).
Thirdly, for users, if the location of shared new energy cars is in a flat and open area that is simple to discover, then it will be easier for users to locate outlets quickly and offer convenience.
Therefore, this paper preliminary determined the placement of alternative locations near 10 different types of areas, as illustrated in
Figure 1, based on careful consideration of the aforementioned factors.
4.2. Enterprise Factors
The shared new energy automotive sector has not had a long development period. It lacks sophisticated theoretical support and management techniques as compared with the conventional vehicle sector. Enterprise management is the coordination of a company’s employees, vehicles, equipment and charging stations, parking spaces, and other elements. Enterprises must pay for software development and repair, operating personnel salaries, and other expenses in addition to the cost of purchasing vehicles, charging infrastructure, and equipment. Enterprises have comparatively less income, and a number of enterprises closed down because their income does not offset their expenses.
When choosing the location, the operating and construction costs should be as low as possible because of the large initial investment capital. In order to reap benefits, businesses should include as many users as possible in the network layout and boost vehicle usage.
4.3. Demand Factors
Through the Wenjuanxing platform, the link (
https://www.wjx.cn/vm/t7v9U0o.aspx accessed on 12 April 2023) to the survey was distributed to the inhabitants of Xuzhou, and the factors influencing their use of shared new energy cars were examined. In total, 420 questionnaires were given out for this study, and 395 of them were valid, accounting for 96.58%. SPSS was used to process the data, and the following findings were obtained (considering the length of the paper, the detailed data are not revealed here):
(1) Among the participants, just 17.47 percent had used shared new energy vehicles. They think sharing new energy vehicles can address issues, such as a shortage of personal vehicles, parking challenges, the inconvenience of public transit, and lengthy commutes. The findings also demonstrate that shared new energy vehicle penetration is modest. A total of 80% of the participants possessed a driver’s license, while 12% did not. A total of 11% of the subjects did not have a car at home, according to the statistics on family automobile ownership, indicating that there are more potential users of these vehicles and broad market potential.
(2) According to the survey, the participants using experience primarily care about the convenience of renting and returning, and the secondary influencing factors are travel comfort, expense, and urgency. The ease of renting and returning remained the top concern for individuals without using experience. However, different from those with user experience, for the individuals who had never driven such shared cars, the potential risk, along with the urgency, cost, and comfort of the trip, was the second most crucial factor. Simply said, whether a shared new energy vehicle is used or not, the ease of rental and return should be a priority. Uneven distribution, too few parking lots, a small number of cars, and not being as convenient as other modes of transportation are the reasons that hinder many participants from using shared new energy vehicles.
(3) The respondents evaluated their likelihood and frequency of utilizing shared new energy cars in the future after being asked about the elements considered when using such vehicles. Respondents who have used such vehicles before are more likely to do so in the future. This shows that most electric shared vehicle users are content with their experiences and have high expectations for their continued use. Further cross-tabulations and model regressions were carried out to assess future use willingness with other characteristics. The findings indicated that individuals’ future readiness to utilize shared new energy cars was highly influenced by criteria such as age, low carbon, environmental protection, risk of use, and prevalence.
4.4. Policy Factors
The environmental problem is represented by the “haze” that is seriously plaguing China’s urban air quality. Existing studies show that traditional cars burning fossil energy are one of the sources of haze, which causes pollution to the environment. Second, the gradual increase in personal car ownership has put great pressure on urban traffic. Based on environmental protection and alleviating traffic congestion, the state has introduced several policies to support the development of shared new energy electric vehicles (Cao et al., 2021 [
25]). All over the country have also responded to the call to refine relevant policies to help green travel. Some of the policy documents are shown in
Table 1.
As seen in
Table 1, the Chinese government and local authorities have released policies to direct the location categories for shared electric vehicles, including transfer parking lots, parks and scenic areas, etc. This clarifies the essential role of these vehicles in urban transportation, effectively guaranteeing and promoting the development of shared new-energy vehicles.
5. Case Analysis
Xuzhou is the sub-central city of Jiangsu province, known as the “transportation center in five provinces”. Xuzhou has actively responded to the national call and issued a series of policies to promote the development of the new energy vehicle industry. For example, “Notice on Local financial subsidies for the promotion and application of new energy vehicles in 2018–2020” provides subsidies to operating units. Data from the Xuzhou Statistical Yearbook in 2021 and the 7th national census in November 2020 show that the populations of Gulou District, Quanshan District, and Yunlong District are 638,534, 619,653, and 421,157, respectively. Jiawang District and Tongshan District have comparatively low population densities. Therefore, this study takes the Gulou District, Yunlong District, and Quanshan District of Xuzhou City as examples to demonstrate the location selection model of new energy-sharing vehicles.
5.1. Determining the Requirement Points
According to the daily travel range of residents, Yunlong District, Gulou District, and Quanshan District of Xuzhou was divided into 140 research areas with 2500 m × 2500 m on the map as the minimum research unit. The demand for this incomplete area is transferred to the closest research unit because some grids are partial, and the study area is tiny. A total of 110 demand points was eventually confirmed by using the geometric center point of each research unit as the demand point designating the research unit. Demand points were labeled, and their longitude and latitude were calculated using the ArcGIS10.2 program. The longitude and latitude of demand points are shown in
Table 2, and the network distribution diagram of demand points is shown in
Figure 2.
5.2. Determining Alternative Network Points
This study uses 10 types of locations as potential selection sites, including schools, hospitals, large shopping malls, public institutions, tourist attractions, transportation hubs, business office buildings, residential communities, large business hotels, and industrial parks. These locations were chosen based on survey results, policy planning, and practical site selection factors. The crawler tool was used to find the latitude and longitude of these 250 locations on the Baidu Map and Amap Map, and the data were imported into ArcGIS10.2 software and displayed on the maps of Gulou District, Quanshan District, and Yunlong District. The coordinates of alternative dots are shown in
Table 3, and the dot distribution is shown in
Figure 2.
5.3. Population Prediction for Each Point
5.3.1. Calculate the Number of Potential User Groups of Each Street in Yunlong District, Quanshan District, and Gulou District
According to the population data provided by the Xuzhou Municipal Bureau of Statistics in May 2022, the total population of each district can be known (
Table 4). According to the seventh national census data in 2020, the proportions of the population aged 15–59 in each district of Xuzhou are 64.84% in Gulou District, 63.11% in Quanshan District, and 64.97% in Yunlong District. Considering the legal age to obtain a driver’s license and residents’ regular driving age, this study takes individuals aged between 20 and 59 as potential customers. According to the news report of Xuzhou, the proportion of high school students is about 3.01% of the total citizens, which can be subtracted to estimate the proportion of potential customers in the three districts: 61.67% in Gulou District; 60.10% in Quanshan District; and 61.96% in Yunlong District.
According to Formula (1), the number of potential customers on each street can be calculated as follows:
where
i is the district number as
i = {1, 2, 3};
j is the street number as
j = {1, 2, 3, …, 15};
Hij represents the number of potential customers of Street j in District
i;
Tij represents the total population of street
j in District
i; and
Wi represents the proportion of potential customers in District
i. The results are shown in
Table 4.
5.3.2. Calculate the Number of Target User Groups of Each Street in Yunlong District, Quanshan District, and Gulou District
According to the results of the questionnaire, user experience has a significant impact on the possibility of customers using shared electric vehicles in the future. This study employs the usage ratio in a questionnaire survey as the basis for calculating the number of users with experience in each district of Xuzhou. Similarly, 83% is used as the proportion of people who have no experience in sharing new energy vehicles in Xuzhou, and the target number of people on each street in each region is calculated based on the two data above. The calculation formula is as follows:
where,
i is the district number as
i = {1, 2, 3};
j is the street number as
j = {1, 2, 3, …, 15};
Rij represents the number of target users of Street
j in District
i;
Hij represents the number of potential customers at Street
j in District
i;
P1 represents the proportion of users with shared travel experience in Xuzhou (
P1 = 17%);
P2 represents the proportion of people without shared travel experience in Xuzhou (
P2 = 83%);
K1 represents the proportion of users with sharing the experience of new energy vehicles who have the intention to use them in the future (
K1 = 82.2%); and
K2 represents the proportion of people who have no experience in using electric sharing new energy vehicles and are willing to use them in the future (
K2 = 65%).
P1,
P2,
K1, and
K2 are derived from the results of the preliminary questionnaire survey. The final target population of each street is shown in
Table 4.
5.3.3. Calculate the Demand of Each Demand Point
Firstly, identify which streets are included in each research unit, then the proportion of the street area falling in the study area to the total street area is calculated, which is the reference standard for assigning the total target population of the street to the study area, and finally, the total population of the study area is added together as the target population of corresponding demand points. The study region, taking demand point 1 as an example, contains 0.34% of Duanzhuang Street, 29.6% of Heping Street, 37.22% of Wangling Street, and 64.49% of Yongan Street. The research area of demand point 1 is allotted to the target population of Duanzhuang Street, 22,238, at a ratio of 0.34%. That is, the target number of customers assigned to demand point 1 in Duanzhuang Street is 75. Similar to this, the objective number of people in Heping Street assigned to the demand point 1 research area is 11,599, Wangling Street 6157, and Yong’an Street 20,089. Hence, the target population at demand point 1 is 37,920.
Table 5 shows the target population of each demand point.
5.4. Calculate the Travel Distance between Each Demand Point and the Alternative Outlets
In the process of this research, the distance between the demand point a and the alternative point b is used as the main index to measure the travel cost. According to the coordinates of 110 demand points and 250 alternative points, the point spacing between different sites is calculated through the domain analysis module of ArcToolbox in ArcGIS10.2 software, and part of the point spacing is shown in
Table 6 (a represents the demand point and b represents the alternative point).
5.5. Construct and Solve the Location Model
This study aims to optimize the original 38 site selection points in the three districts and select 38 more suitable points among the alternative sites so as to minimize the total travel cost of all residents within the study scope.
Table 7 shows the information on existing charging stations.
In order to reduce the overall weighted travel distance between each demand point and the associated unique site selection point, the P-median model was utilized in this study to identify P ideal site selection locations among n alternative points. The journey distance of users to 110 demand sites is minimized by choosing 38 optimal site selection points from 250 alternative places in this study. The equation reads as follows:
where
Z represents the objective function;
N is the set of 250 alternative nodes (
N = {1, 2, ⋯, 250}); let
M denote the set of 110 demand points (
M = {1, 2, ⋯, 110});
a represents the sequence number of each demand point;
b denotes the serial number of each alternative dot;
P represents the number of sites (
P = 38);
Da denotes the quantity demanded at demand point
a;
dab denotes the distance between the demand point
a and the alternative point
b; and
Xb is 0/1 decision variable, where 0 represents that the location point is not established at point
b, and 1 represents that the location point is established at point
b.
Yab is a 0/1 decision variable, where 0 means that location
b does not serve demand
a, and 1 means that location
b provides service to demand
a.
The site selection model of the charging pile and parking space alternative network in Yunlong District, Quanshan District, and Gulou District is designed according to D
a, d
ab, and P, which have been identified in the earlier stage, and in accordance with Formulas (3)–(8). The model is created first by calling Matlab’s yalmip solution toolbox. The final step in the approach is to apply the CPLEX 12.8 optimization solver. Finally, the optimal layout scheme of charging facilities in the three districts of Xuzhou is calculated, as shown in
Figure 3. The corresponding demand points of all site selection points are shown in
Table 8.
Before optimization, consumers traveled a total of 1,356,537,148.7 m (Zfront = 1,356,537,148.7 m) to the alternative network. After optimization, the total distance is 916,902,356.24 m (Zafter = 916,902,356.24 m). It is discovered that while the number of current charging piles stays the same, the weighted distance difference between the before and after optimization is 439,634,792.5 m. With a combined target population of 696,146 individuals across Gulou, Quanshan, and Yunlong Districts, the travel time per person has been cut by 631.5267 m. At an average walking speed of 5 km/h, this translates to a reduction of about 7.58 min.
6. Conclusions and Recommendations
6.1. Conclusions
This paper considers that shared new energy vehicles are quasi-public goods with indivisibility, limited competitiveness, and non-exclusivity and analyzes the three core stakeholders that affect the location of shared new energy vehicles, namely, the government, enterprises, and users. On this basis, the theoretical framework of “G-B-U “is constructed to guide the layout of such vehicles. Government plays a leading, coordinating, and supervising role in the layout planning process of new energy vehicles. Enterprises are builders and directly responsible parties. The user is the service terminal and is the direct decider in promoting the development.
Location and policy are the two key determining elements at the government level. Shared new energy vehicle locations should be chosen in accordance with urban development planning and in response to governmental directives. Cost and income are the two key variables at the enterprise level. This study primarily discusses the impact of user demand in terms of the user dimension and performs a questionnaire survey among Xuzhou locals.
This study develops a three-stage site selection optimization model that includes “market demand projection-alternative point selection-model construction and solution”. Xuzhou City’s Yunlong, Gulou, and Quanshan districts are chosen as the case study’s subjects. According to the findings, the overall weighted journey distance for users under the current architecture of 38 site selection points is 1,356,537,148.7 m, compared to 916,902,356.24 m for the improved data. The per capita travel distance is cut by 631 m, and the difference between the two is 439,634,792.5 m, demonstrating the logic and efficacy of the location selection approach.
6.2. Recommendations
The government can plan the development layout of the new energy automobile industry in cities through policies and coordinate land and financial resources to support enterprises. The government should develop supervision and planning schemes as soon as possible in accordance with the current situation, as the sharing of new energy vehicle industry has only recently emerged, and the corresponding laws and regulations have not yet been perfected. Additionally, the majority of users access shared new energy vehicles through the download and registration of apps. Relevant government entities should closely oversee the privacy leakage issue to protect user information security.
The location layout and quantity distribution of urban shared new energy vehicles should be paid attention to by the government and enterprises. It should avoid the situation that there are not enough vehicles in areas with high demand or it is inconvenient to borrow and return cars and that the number of places with low demand is distributed more and the land area is divided into large areas.
Before site selection, enterprises should screen schools, tourist attractions, large shopping plazas, hospitals, and other places with a high frequency of resident activities in the region to determine the location of alternative outlets and try to understand the number of residents in the network radiation area, frequency of use, and other information to predict the demand of the network. At the same time, in order to reduce construction and operation costs, enterprises should estimate the cost of each suitable alternative point and select the most suitable construction site.
The government should help businesses advertise shared new energy cars by enhancing publicity and developing subsidy programs to draw in more users. In order to collectively create a green and civilized city, education and training should be given to residents to help them use and park in a civilized manner.