A Study on the Key Factors for the Sustainable Development of Shared Mobility Based on TDM Theory: The Case Study from China

Abstract

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations.

1. Introduction

With the acceleration of China’s urbanization process, the increasing demand for urban transportation has brought great challenges to the carrying capacity of public transportation systems [1]. The number of cars in China is constantly expanding, but due to the large population, a series of public problems, such as congestion and carbon emissions, have occurred. At present, a significant problem is that public transport at this stage is still unable to solve the “last mile” problem [2]. After more than ten years of development, the investment subjects of China’s shared mobility industrial model are mainly the government and individuals. These systems include bicycles, electric vehicles and other complete transportation systems. In recent studies, the influencing factors of shared mobility have varied for different mobility modes. For example, in e-scooter sharing, users are more concerned about social influence, variety seeking and perceived enjoyment, performance expectancy, effort expectancy, and price sensitivity [3,4]. In bike sharing, station occupancy and bicycle availability and totem functioning are key service attributes for enhancing satisfaction [5]. In car sharing, the potential demand is mainly for public transportation, and commuting and long-distance travel are more sensitive to price fluctuations, which affects users’ choice of car sharing for travel [6]. In ride hailing, users consider the use of ride hailing cars in terms of cost, practical advantages, convenience, etc. [7]. In addition, there are different travel characteristics and modes in each field (

Table 1. Characteristics and methods of shared mobility in China.

Investment Entity	Mobility Mode	Mobility Case	Mobility Characteristics	Recommend
Government	Public bicycles	Municipal public bicycle	Swipe card rental, fixed pick-up and change points. The number is small.	Wang H [8]
	Public bus	Bus BRT	The number of buses in the city is enormous, and the lines cover the entire corner, providing great convenience for residents to travel. However, rush hour is crowded, and it cannot take passengers at any time and everywhere.	Shi J [9]
	Public rail transit	Subway Light rail	Efficient, fast, large volume, and can effectively alleviate urban traffic congestion. The construction cost is high, the cycle is long, and a lot of money and time needs to be invested in planning and construction.	Li F [10], Hu W [11]
Company	Shared bicycles	Meituan Bike Hello Bike Green Orange Bike	Helps to reduce carbon emissions and protect the environment. Convenient, fast, and economical. However, the shared bicycles are parked at will, affecting urban order and beauty. Due to frequent use and improper maintenance, some shared bicycles have been damaged, affecting the user experience.	Cao M [12]
	Shared electric vehicles	DIDI Green orange Liubike	Does not produce exhaust emissions and helps to reduce air pollution and reduce carbon emissions, in line with the concept of green travel. Sharing electric vehicles can help alleviate urban traffic congestion and improve road efficiency. The battery life of shared electric vehicles is limited, the battery capacity is generally small, and the driving range is limited.	Campbell A A [13], Wang J [14]
	Shared car	Shouqi Gofun Panda car	Car sharing reduces the need for private cars and helps alleviate the problem of urban traffic congestion. While car sharing offers a convenient rental service, parking and picking up may still be inconvenient in some areas or periods.	Ren X [15]
Individual	Ride sharing	Didi Chuxing Cao Cao Chuxing	Carpooling realizes the sharing of vehicle seat resources, which helps to reduce the empty driving rate and improve the efficiency of vehicle use. Once the carpooling scheme is determined, both passengers and owners need to travel according to the agreed time and route, and the flexibility is relatively poor.	Yao D [16]
	Private ride	Carpooling groups through social software (WeChat, QQ)	The relatively fixed ride area is convenient for people to use the relatively fixed ride area, and the ride time is relatively flexible. Because social software carpooling involves communication and interaction between strangers, there are certain security risks.	Xu D [17], Dong X [18]

).

Moreover, for the sustainable development of urban transportation, the Chinese government has accelerated the process of compliance with and legalization of shared mobility. A series of shared mobility policies have been promulgated at the government level (

Table 2. Development of China’s “shared mobility” policy in recent years.

Release Time	Policy Issuing Unit	Policy Name	Main Content
2023	Ministry of Transport	Some opinions on promoting the healthy and sustainable development of urban public transport	Urban public transportation is an important part of the urban comprehensive transportation system, which is intensive and efficient, green, and produces low carbon emissions. Give priority to the development of urban public transport, promote shared transportation modes, and alleviate urban traffic congestion.
2022	Scientific Research Institute of Ministry of Transport	China Shared Mobility Development Report	From a multidisciplinary perspective, in the form of an annual report, this paper objectively describes the development of the main types of shared mobility in China, points out existing problems, puts forwards countermeasures and suggestions, provides readers with a more three-dimensional overall picture of shared mobility, and provides decision support for promoting the development of the shared mobility industry.
2022	The State Council	The 14th Five-Year Plan for the development of a modern comprehensive transportation system	The healthy development of the northern share car, car sharing, and embroidery and final goods delivery platform in the Tiang district to prevent disorderly expansion. We will accelerate the development of new models and new business forms of “internet plus” efficient logistics.
2020	National Development and Reform Commission	Opinions on Supporting the Healthy Development of New Business Forms and Models, Activating the Consumer Market, and Expanding Employment	China encourages intelligent product upgrading and business model innovation in shared mobility, food delivery, group buying, online medicine purchase, shared accommodation, cultural tourism, and other fields, develops new ways of living consumption, and cultivates high-end online brands.
2019	The State Council	Outline for Building a Powerful Country in Transportation	By 2035, China will basically build transportation power and form a “national 123 travel and transportation circle”, with convenient and smooth passenger connection transportation and efficient and economical multimodal transport of goods. The development level of intelligent, safe, green, and shared transportation has been significantly improved.
2019	National Development and Reform Commission	Green Industry Guidance Catalogue (2019 edition)	Carry out the construction and operation of shared transportation facilities, including the construction and operation of public rental bicycles, internet rental bicycles, internet rental electric bicycles, internet rental cars, car time-sharing rental systems, three-dimensional parking facilities and equipment, and bicycle parking facilities.
2017	Ministry of Transport	Guiding Opinions on Encouraging and Regulating the Development of Internet Rental Bicycles	The guidelines affirm the positive role of the development of internet rental bicycles (commonly known as “shared bicycles”) in facilitating the short distance travel of the masses and building a green and low-carbon transportation system. The guidelines propose to encourage and regulate the development of shared bicycles in accordance with the basic principles of “service-oriented reform and innovation, standardized and orderly territorial management, and multiparty governance”.

). All urban transport authorities are required to urge shared mobility platform companies to operate in accordance with laws and regulations and speed up the compliant operation of shared mobility. The introduction of local regulations has played an important role in promoting the healthy and safe development of the local shared mobility market.

The concept of shared mobility has gradually changed from a single concept of shared transportation, such as “shared bicycles”, “shared electric vehicles”, and “shared cars”, to “shared mobility”, which is a mixed system of multiple transportation vehicles for travel needs. Although the number of research articles on the sustainable development of shared mobility is increasing annually, most research has focused on the exploration of a single shared transportation model. For example, the service planning of shared bicycles is analyzed from the perspectives of demand and pricing, supply and incentives, and tactics and operation [19]. The shared car is discussed from five aspects: mode, engine, optimization goal, time range, and methodology [20]. Moreover, the relevant studies on the factors that encourage the sustainable development of shared mobility are based mainly on in-depth discussions of a single factor, such as shared mobility education [21], shared mobility publicity [22], shared mobility financial support [23], and shared mobility standardization and normalization [24]. However, these studies do not examine the underlying dynamics and interdependence of various factors. Therefore, the current research lacks a comprehensive analysis of the factors affecting the sustainable development of shared mobility from a systematic perspective.

From the perspective of user behavior in shared mobility, the main factors affecting development currently include psychological factors, situational factors, and sociological characteristics.(

Table 3. User behavior of shared transportation.

The Main Categories of Shared Mobility	Influencing Factors of User Behavior
	Psychological Factors	Situational Factors	Sociodemographic Characteristics
Shared bicycles	Moral standards [35], Time perception [30], Health benefits [36], Safety perception [37]	Developed infrastructure [33], close to other public transportation [38], Information and Communication Technology (ICT) [34], Environmental Protection [39]	1. The use of shared bicycles by tourists in scenic spots mainly depends on behavioral control and attitude [40]. 2. Male users are more willing to use shared bicycles [41]. 3. The user is young, highly educated, and has a clear willingness to use [27]. 4. Company employees and college students are the main users [42].
Shared electric vehicles	Attitude [43], Saving time [44], Enjoyment [45], Subjective norms [46], Usability [47], Reliability [3]	Low cost [48], Higher fleet density [49]	1. Elderly and female users have a lower willingness to use it [50,51]. 2. Highly educated communities use it more commonly [52].
Shared car	Waiting time [53], Innovative personality traits [54], Environmental awareness [29]	Privacy concerns [55], Price [56], Business and personal interests [57], Regulatory framework [32], Shared Mobility services [58], Parking conventions [59]	The usage rate is higher among males than females [29]. People in higher education are more willing to use shared cars [28]. Young people use it more frequently than older people [25]. Residents living in the suburbs often use shared cars [60].
Ride Sharing	Trust (peer ratings, members’ personal information, and trip amounts) [31], Social experience (Sharing a ride with like-minded people) [61]	Price [62], Consumption habits [63], Travel distance [64], City and population density [65], Environmental protection [66]	Low income and disadvantaged transportation groups prefer to use Ride Sharing [26]. Young and highly educated individuals use ride hailing services [66]. Women are more inclined to use carpooling services than men [67].

) The main mode of shared mobility in China is shared bicycles, shared electric vehicles, shared cars, and ride sharing. These four types of transportation have differences in the factors influencing user behavior for each type. Firstly, from the perspective of sociodemographic characteristics, shared transportation is more friendly to low-income and disadvantaged groups. However, different groups have different choices for shared transportation, with older people and women having fewer options and not liking to use shared cars and shared electric vehicles [25]. Men have a demand for all shared mobility options. Low-income individuals tend to prefer shared bicycles and ride sharing [26]. The population with a higher level of education is one of the mainstream users of shared mobility [27,28]. Therefore, it can be seen that publicity and education are important measures for the sustainable development of shared mobility. Secondly, from the perspective of psychological factors, environmental awareness [29] and time perception [30] are common influencing factors of shared transportation. Security perception, trust, and other factors can affect the choice of shared transportation. The platform’s data security, privacy protection, and service quality are important indicators that affect the trust level of shared travel platforms [31]. When facing ride sharing users, they are more vigilant and often unwilling to face strangers, lacking a sense of security. The impact of psychological factors on shared mobility is complex and varies greatly among individuals. Finally, in terms of situational factors, government regulation [32], facility construction [33], and the ease of use of shared mobility apps [34] are all important factors that affect users. These aspects require more participation from governments, companies, and individuals to further improve users’ perception of shared mobility usage. Interactive participation from an organizational perspective is an important way to address this issue at present. From the above research, we found that previous studies have focused on specific categories of shared transportation, reflecting many detailed features. However, from a macro perspective of shared transportation, the integrated management of various modes of transportation is more important in the current stage of development, which requires a macro view of the sustainable development of shared transportation. Integrating various factors from previous research into a macro level study can further expand the theoretical scale of shared mobility. Especially for China, the number of shared travel users is huge, the transportation environment is complex, there is a shortage of management personnel, and the update speed of management systems is slow. Integrating user behavior and proposing a macro-level sustainable development concept for shared mobility is more in line with the current situation in China, which is also the focus of this research.

The shared travel management experience of management personnel will provide inspiration for this study. Taking shared bicycles as an example, the main management is divided into government departments and companies. In terms of the use of shared bicycles, government managed shared bicycles mainly use radio frequency identification (RFI) technology and shared bicycle cards (similar to public transportation cards). In recent years, economically developed regions have started using QR codes for registration and exit. Poor user experience and low usability. The role of shared mobility operators in technological innovation is even greater, with wireless communication technology, especially the global positioning system (GPS), monitoring the usage records of shared bicycles using only mobile phones. For government department managers, delineating parking areas and implementing “point-to-point” management of shared bicycles can effectively provide traffic order. However, this has brought difficulties for users, as fixed parking spots (with few parking spots) cannot meet their travel needs. With the intervention of shared bicycle operating companies, numerous shared non-motorized vehicle parking points have been planned using GPS technology. The shared travel experience of users has been further improved. At the same time, the government’s shared bicycle business is facing operational difficulties and is gradually withdrawing from the market, encouraging the development of shared bicycle companies. The transformation of the role of managers: they have also changed their thinking, and government departments have begun to supervise bike sharing companies as “regulators” [68]. The company assumes the role of an “operator” for shared mobility. Therefore, government managers consider more macro factors, such as infrastructure construction, providing corresponding incentive policies, improving regulatory systems, conducting education and publicity, etc., with the aim of popularizing the use of shared mobility. The management experience of company managers is more focused on specific operations such as user experience, information and communication technology, price reduction, etc., with the aim of improving the utilization rate of shared travel. Whether it is government regulation or company operation, ultimately these measures benefit the general population. From the above examples, we can see that in previous studies, more emphasis has been placed on analyzing the factors of shared transportation under corporate identity, with less research on shared transportation in government departments. For the sustainability of shared mobility, it is necessary to consider both the management experience of the company’s “operators” and the management experience of the “regulators”. Therefore, macro level research is more important at this stage, and this research requires both government and corporate managers to comprehensively analyze the sustainability of shared mobility. China has experienced more than a decade of development in shared mobility. In this process, there are insufficient supplies of shared mobility facilities, a lack of shared mobility policies, and a weak willingness to share mobility, which hinders the sustainable development of shared mobility. At the center of sustainable transportation [69] is a sustainable transportation system, providing a variety of transportation modes. Travel demand management (TDM) aims to meet people’s travel needs by introducing alternative travel options, minimizing travel burdens or redistributing travel demands [70]. Scholars divide these measures into hard strategy behavior and soft strategy behavior. Hard strategy behavior mainly refers to structural measures aimed at changing the external environment of travel behavior choices, including improving infrastructure, increasing vehicle usage costs, limiting vehicle usage policies, and so on [71]. However, hard strategy behavior does not always have the expected effect, as it cannot successfully reduce users’ attraction to car use or change their travel habits. Therefore, it is necessary to understand and educate users to increase their knowledge of “alternative transportation”, enhance their awareness of “alternative transportation”, and strengthen their tendency to adopt reasonable modes of transportation. The purpose of these behavioral strategies is to change personal attitudes and motivations and guide users to adopt more sustainable behavior; this approach is called soft strategy behavior [72]. For example, charging road fees, parking fees, etc., are difficult strategic behaviors [73]. Healthy travel publicity, education, etc., are soft strategies behaviors [74]. Scholars have also debated whether hard strategy behavior [75] or soft strategy behavior [76] can effectively reduce car use. However, these policies have achieved remarkable results in sustainable traffic management around the world. For example, in hard strategy behavior, Singapore charges a congestion fee [77], and the United States charges a parking fee [78]. Moreover, soft strategy behavior has also achieved effective results in practice [79]. The factors involved include the following: (1) provision of alternative modes of transport and services, (2) incentives for travelers to switch to other modes of transportation and services, (3) an effective balance between transportation demand and transportation facilities and policies formulated to guide the development of transportation technology, and (4) ensuring various types of technical support for the implementation of the strategy. In conclusion, TDM has great potential for sustainable urban traffic management [80].

In the relevant research on TDM theory, there are many studies focusing on traditional modes of transportation, while there is relatively little research on shared travel. Mainly providing relevant content on shared transportation in TDM research [81], TDM can affect commuting choices [82,83]. Faced with the current trend of shared mobility as the mainstream mode of transportation, the intervention of TDM theory will provide new ideas for the sustainable development of shared mobility. Owing to the lack of factors affecting the sustainable development of shared mobility from a systematic perspective, TDM theory can be used as a complete system for the study of the sustainable strategy behavior of shared mobility, compensating for the problem of a single goal in current research. However, it cannot be used to discuss the relationship strength of various strategic behavioral factors in TDM, distinguish direct factors from indirect factors, or discuss their hierarchical relationships. Therefore, through the FUZZY-DEMATEL-ISM-MICMAC integration model and the TDM theoretical framework, this study discusses the influencing factors of sustainable strategy behavior in shared mobility.

Use the DEMATEL-VIKOR model combination in related research on shared mobility to evaluate the service quality of shared bicycles [84]. Discuss the user experience of car sharing services [85]. In the field of sustainable development in transportation, the DEMATEL model has been extensively studied in combination with other models, including the sustainability of rapid transit transportation [86] and the driving and hindering factors of electric vehicle use [87]. The DEMATEL-ISM model is also involved in the study of influencing factors on the use of transportation facilities [88] and the economic operation of electric vehicle charging stations [89]. The DEMATEL-ISM-MICMAC model has been mainly used in facility engineering research in the past [90,91]. At the same time, in order to ensure the objectivity of the research, we have added the fuzzy theory of triangular fuzzy numbers to weaken the subjectivity of expert questionnaire surveys. Therefore, DEMATEL-ISM-MICMAC was mainly used in the fields of transportation and sustainable development in the past. There is relatively little research on the use of this model in the field of shared mobility, and the FUZZY-DEMATEL-ISMICMAC combination model is suitable for this study, which is also an important innovation of this research. In this study, the decision experiment and evaluation laboratory (DEMATEL) method was first used to analyze the strength relationships among the factors. Because the DEMATEL method is based on expert knowledge and experience, it is too subjective, and individual differences greatly affect the research results. Therefore, it is necessary to first adopt the fuzzy set theory and use triangular fuzzy numbers to quantify the subjective scores of expert groups to weaken the subjective influence of expert scores. Second, the factors are systematically stratified by the interpretive structure model (ISM). Finally, the importance among factors is analyzed using the MICMAC method to determine the priority of sustainable strategic behavior factors of shared mobility. Through the integration of the four methods, the sustainable development strategy of shared mobility is supported.

This study provides a more comprehensive overview of the influencing factors of “shared mobility” sustainable development and, through data research, emphasizes 21 key influencing factors that need to be discussed at the current stage. By integrating the sustainable development influencing factors of “shared mobility” into the TDM theory system, the “hard strategy and soft strategy” of the “TDM theory” have been expanded, and specific implementation strategies have been proposed. The contributions of this study are as follows.

(1) This study presents a more comprehensive list of factors that affect the sustainable development of shared mobility and explains each aspect. The results can help transportation authorities realize the importance of several aspects in the sustainable development of shared mobility.

(2) This study adopts quantitative and qualitative research methods. After the influential factors of the sustainable development of shared mobility are obtained via the TDM theoretical framework through investigation and a literature review, the influential factors are evaluated via expert interviews. A research framework for the sustainable development of shared mobility via FUZZY-DEMATEL-ISM-MICMAC technology is proposed, which may be helpful for future research.

(3) The interactions between influencing factors are discussed from a comprehensive perspective, which expands the previous single research perspective and makes the research results representative and reliable.

(4) This study provides useful information for shared mobility management departments. For the sustainable development of shared mobility to proceed smoothly, it is necessary to integrate the “hard strategy” and “soft strategy” of TDM theory. At this stage, it is necessary to pay attention to the influential factors of shared mobility education, shared mobility operating technology, and organizational interaction to improve the decision-making level of management departments and promote the sustainable development of shared mobility.

This study addresses the following questions: (1) What are the most important factors affecting the sustainable development of shared mobility? (2) From the perspective of TDM theory, what are the strengths of the logical relationships and mutual relationships among the factors influencing the sustainable development of shared mobility? (3) How can the hierarchical relationships and logical order among influencing factors be understood? This study can provide a practical basis for the sustainable development of shared mobility and support for operating organizations providing shared mobility services, the management departments of shared mobility, and the cooperative departments of shared mobility.

2. Research Methods

In previous studies, quantitative research was adopted to verify research hypotheses by processing collected data, but the relationships among factors could not be identified and the logical relationships among factors could not be determined. However, in qualitative research, researchers often use more professional data for subjective qualitative analysis, lacking the objective experience brought by data. In this study, a combination of quantitative and qualitative research methods was used to analyze the factors affecting the sustainable development behavior of shared mobility from a comprehensive and systematic perspective. First, qualitative research is carried out on the factors through a literature review and expert interviews. Second, the FUZZY-DEMATEL-ISM-MICMAC integration method is used to study the factors quantitatively. By combining qualitative and quantitative research methods, the main factors of the sustainable development of shared mobility are identified to ensure that this study can reach a reasonable conclusion.

Based on a literature review and expert research, this study identified the factors influencing the sustainable development of ‘shared mobility’. Quantitative analysis is conducted through expert evaluation to elucidate the importance of each influencing factor. To address a series of issues in this study, FUZZY-DEMATEL-ISM-MICMAC is an effective system element analysis method. The decision-making trial and evaluation laboratory (DEMATEL) method is mainly used to analyze the complex relationships between variables. It is a method that uses graph theory and matrix theory to analyze system factors and is used mainly for analyzing and ranking the feature importance of system elements, demonstrating the robustness and effectiveness between elements [92]. Because the DEMATEL method is based on expert knowledge and experience, issues such as strong subjectivity and significant individual differences significantly affect the research results. Therefore, it is necessary to first adopt fuzzy set theory and use triangular fuzzy numbers to quantify the subjective ratings of expert groups to weaken the subjective influence of expert ratings [93]. Moreover, DEMATEL analysis cannot reveal the relationships and directions among the influencing factors in the system, thus requiring the ISM method. The ISM method can further explain the hidden links between system factors, and this hierarchical linkage relationship can be visualized through Boolean logic operations [94]. The position, influence relationship, and direction of elements in the system can be seen through the ISM structure. Similarly, the ISM (interpretive structural modeling) structure cannot determine the attribute classification of elements. The MICMAC (matrice d’impacts croisés multiplication appliquée à un classement) model, on the other hand, divides elements into sporadic factors based on their importance, dependent factors, related factors, and independent factors. The purpose is to determine the priority of elements and identify key factors that have a significant impact on the system. By using these four methods and combining their advantages, we can more accurately analyze the attributes of this study.

This study adopts seven steps. The first step is to review the previous research literature on shared transportation and summarize the main factors affecting the sustainable development of shared mobility under the framework of TDM theory. In the second step, based on the summarized influencing factors, the most frequently mentioned influencing factors in the literature under TDM theory were selected as the questionnaire variables, and the questionnaire was provided to management experts in the field of shared mobility for a pretest. Twenty-one influencing factors were selected through expert interviews. In the third step, 30 experts were interviewed, and questionnaires were distributed. The fuzzy set theory is used to calculate the primary data to reduce the subjectivity of the questionnaire results. The fourth step involves the use of a direct impact matrix and the DEMATEL method to conduct a macro analysis of the influencing factors of the sustainable development of shared mobility and to discuss the degree of influence and centrality. The fifth step is to establish a recursive structure model by using the ISM method to analyze the hierarchical relationships and orientations of the influencing factors. In the sixth step, the MICMAC method was used to cluster and summarize the factors and explain the deep meaning of the structure. The seventh step, which is based on the above analysis and according to the development status of shared mobility, offers relevant countermeasures and suggestions from the perspective of the government and shared mobility management to promote the sustainable development of shared mobility. Figure 1 describes the specific steps and methods of this study.

2.1. Triangular Fuzzy Numbers

The triangular fuzzy number $\tilde{N}$ can be represented by the triplet $(l,m,r)$, and its membership function ${\mu }_{\tilde{N}}\left(x\right)$ can be represented as:

$${\mu }_{\tilde{N}}(x)=\left\{\begin{array}{c}(x-l)/(m-l)\\ (r-x)/(r-m)\\ 0\end{array}\right.\begin{array}{cc}& l⩽x⩽m\\ & m⩽x⩽r\\ & x>rorx<l\end{array}$$

(1)

In the formula, $l$ and $r$ represent the lower limit and upper limit of the fuzzy number, respectively; $m$ is the most likely value; and the larger the value of $r-l$, the greater the fuzziness of the fuzzy number.

When the expert questionnaire is scored, the evaluation can be converted into triangular fuzzy numbers through the following table.

(1) Standardization

$$x{l}_{ij}^k=(l_{ij}^k-min{l}_{ij}^k)/{\Delta }_{min}^{max}$$

(2)

$$\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=({\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})/{\Delta }_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(3)

$$\mathrm{x}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=({\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})/{\Delta }_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(4)

Then,

$${\Delta }_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}=\mathrm{m}\mathrm{a}\mathrm{x}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}$$

(5)

(2) Calculate the normalized values on the left and right sides

$$\mathrm{x}\mathrm{l}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}/(1+\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{x}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})$$

(6)

$$\mathrm{x}\mathrm{r}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\mathrm{x}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}/(1+\mathrm{x}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}-\mathrm{x}{\mathrm{m}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})$$

(7)

(3) Calculate the total standardized value

$${\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\left[\mathrm{x}\mathrm{l}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\right(1-\mathrm{x}\mathrm{l}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}})+\mathrm{x}\mathrm{r}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}\mathrm{x}\mathrm{r}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}]/[1-\mathrm{x}\mathrm{l}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}+\mathrm{x}\mathrm{r}{\mathrm{s}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}]$$

(8)

(4) Calculate the defuzzification value of the first expert evaluation

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}=\mathrm{m}\mathrm{i}\mathrm{n}{\mathrm{l}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}+{\mathrm{x}}_{\mathrm{i}\mathrm{j}}^{\mathrm{k}}{\Delta }_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$$

(9)

(5) Based on the expert evaluations, the direct impact matrix of defuzzification is obtained:

$${\mathrm{z}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{1}{\mathrm{p}}}({\mathrm{z}}_{\mathrm{i}\mathrm{j}}^1+{\mathrm{z}}_{\mathrm{i}\mathrm{j}}^2+\cdots +{\mathrm{z}}_{\mathrm{i}\mathrm{j}}^{\mathrm{p}})$$

(10)

2.2. DEMATEL

DEMATEL mainly considers obstacles or influencing factors. Through matrix calculation, the correlation of influencing factors can be determined, and the main influencing factors can be determined.

After the direct impact matrix for all expert defuzzification operations is obtained, the DE-MATEL calculations can be performed.

(1) Normative impact matrix. There are many methods for normalization, and the row maximum method is used here. Each row of matrix $Z$ is summed, its maximum value is obtained, and all the elements of matrix $Z$ are divided by the maximum value to obtain the normative impact matrix $B$.

$$\mathrm{B}={\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\max \left({\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)}}$$

(11)

(2) Combined impact matrix. The comprehensive system matrix represents the combined effects of the influences among the various elements of the system.

$$\mathrm{T}=\left(\mathrm{B}+{\mathrm{B}}^2+\cdots +{\mathrm{B}}^{\mathrm{k}}\right)=\sum _{\mathrm{k}=1}^{\mathrm{\infty }}{\mathrm{B}}^{\mathrm{k}}=\mathrm{B}{\left(\mathrm{I}-\mathrm{B}\right)}^{-1}$$

(12)

In the column expression, $I$ is the identity matrix.

(3) Calculate the influence degree, affected degree, center degree, cause degree, and weight of each factor.

The impact level refers to the sum of all the lines in the matrix, indicating the comprehensive impact of each element on all the other elements, which is $D_i$.

$${\mathrm{D}}_{\mathrm{i}}=\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}},\left(\mathrm{i}=1,2,\dots ,\mathrm{n}\right)$$

(13)

The impact degree refers to the sum of columns in matrix $T$, indicating the comprehensive impact of the elements in each column on all the other elements, denoted as $C_i$.

$$C_i=\sum _{j=1}^n{x}_{ji},\left(i=1,2,\dots ,n\right)$$

(14)

The centrality of a factor represents its position in the evaluation system and its role. The centrality of a factor is the sum of its influence degree and the influence degree, which is denoted as $M_i$.

$$M_i=D_i+C_i$$

(15)

The cause degree is obtained by subtracting the influence degree and the influence degree of the factor and is denoted as $R_i$.

$$R_i=D_i-C_i$$

(16)

The weight of the index can be obtained by normalizing the centrality.

(4) Draw a causal map. The center degree is the horizontal coordinate, the cause degree is the vertical coordinate, and the causation diagram is drawn.

2.3. ISM

Interpretive structural modeling (ISM) is a structured method of analysis that identifies the relationships between factors in a complex system. ISM uses graph theory to break down complex systems into simple systems. Using a hierarchical approach makes it easier to understand the relationships between factors.

(1) Calculate the reachability matrix. The overall impact matrix is $H=T+I$, where $T$ is the combined impact matrix and $I$ is the unit matrix. The reachability matrix $H$ is obtained by setting the numbers less than the threshold to 0 and the others to 1 in the matrix $F$.

$$h_{ij}=\{\begin{array}{cc}1& h_{ij}\ge \lambda (i,j=1,2,\dots ,n)\\ 0& h_{ij}<\lambda (i,j=1,2,\dots ,n)\end{array}$$

(17)

(2) Calculate the reachable set, the antecedent set, and the intersection.

Reachable set $R_i=\left\{f_i\right|F_{ij}=1\}$. The factor corresponding to the column with a median value of 1 in each row indicates the set of all factors that can be reached from that factor.

Antecedent set $S_i=\left\{f_i\right|F_{ji}=1\}$. The factor corresponding to the row with a value of 1 in each column indicates the set of all factors that can be reached for that factor.

Then, the intersection set $R_i\cap S_i$ is calculated.

Factor stratification. According to the reachable set $R_i$ and the antecedent set $S_i$, the hierarchical composition of the internal factors of the system is drawn, and the influence between the factors can be discussed according to the centrality of the DEMATEL matrix.

2.4. MICMAC

MICMAC is a further analysis based on the ISM methodology. This method is used to study diffusion between factors. The driving power and dependence power factors are divided into spontaneous factors, related factors, and independent factors.

(1) Calculate the driving power and dependence power. The driving power represents the degree of influence on other factors and is the lateral summation of the reachability matrix. The dependence power represents the degree of influence of other factors and is the longitudinal summation of the reachability matrix.

(2) Plot the driving power and dependence power. A scatter plot is drawn with the driving power as the horizontal coordinate and the dependence power as the vertical coordinate.

3. Factors Affecting the Sustainable Development of Shared Mobility

The sustainability of urban transportation is generally reflected in the following aspects. Energy consumption: services such as ride-sharing services can reduce vehicle fuel consumption [95]. Shared mobility services can reduce carbon emissions [96]. Car ownership and car sharing inhibit the increase in the number of private cars and reduce the negative impact on the environment [97]. Traffic congestion and travel time are reduced in the shared mobility mode [98]. These studies are reflected mainly in the assessment of the sustainability of shared mobility on the overall urban traffic environment, whereas research on the sustainable development of shared mobility traffic behavior is relatively weak. To provide a broader discussion of “shared mobility” sustainable development, our references and research foundation include both research in China and many international studies. This can provide a more comprehensive study and discussion of ‘shared mobility’. We reviewed the literature on shared mobility, including keywords such as “shared bicycle”, “shared electric vehicle”, “shared car”, “shared transportation”, “shared mobility”, “public transportation”, and “public travel”. Through ScienceDirect, Wiley, EbSCO, Scopus, PubMed, Web of Science, and other databases, we summarize the sustainable development factors of shared mobility in the literature in

Table 4. Expert evaluation and triangular fuzzy number conversion table.

Expert Evaluation	Influence Value	Triangular Fuzzy Number
No impact	0	(0, 0, 0.25)
Very low impact	1	(0, 0.25, 0.5)
Low impact	2	(0.25, 0.5, 0.75)
High impact	3	(0.5, 0.75, 1.0)
Very High impact	4	(0.75, 1.0, 1.0)

.

To ensure scientific and rigorous impact factors, we invited 10 experts with experience in managing shared mobility to participate in our study from 10 June 2024 to 30 June 2024. Some of them are public officials in the traffic management department, who are involved in the relevant management work of shared mobility, including but not limited to the road planning of shared mobility, parking place planning, and inspection of the shared transportation market sector. These experts are more inclined from the government level to promote the sustainable development of shared mobility for residents. The other group includes practitioners in the shared mobility industry, who are involved in the marketing of shared mobility and the daily operation and management of shared mobility. These experts promote the sustainable development of shared mobility for residents from the operational level of shared mobility (

Table 5. Sustainable development factors of shared mobility.

Num.	Factors	Explanation	Recommend
1	Shared mobility education	Increase the content of environmental protection and green travel, cultivate environmental awareness, and develop the habit of green travel.	Yin Y [99], Steffen J [100], Nikitas A [21]
2	Shared mobility publicity	Promote the contribution of shared mobility to environmental protection through multiple channels such as media, social platforms, and community activities, and enhance the public’s environmental awareness and willingness to use.	Yunus E [22], Taniguchi A [101], Rastogi R [102], Sun F [103]
3	Shared mobility “environment” information	Add green landscape, environmental protection information, green travel information, etc., to remind users of the environmental significance of choosing shared mobility.	Chen S Y [104], Chevalier A [105], Gamble J [106]
4	Shared mobility health database	Research and development of shared mobility technology, the implementation of healthy city healthy travel data, and promoting the development of health-friendly cities.	Caravaggi L [107], Gkoumas K [108], Zhu J [109]
5	Improve health	By promoting shared mobility, especially the benefits of shared bicycles on cardiovascular health, weight loss, and physical fitness, more users will be attracted to choose shared mobility.	Chen Y [110], Otero I [111]
6	Community organization and advocacy	Organize community shared mobility activities to enhance residents’ recognition and interest in shared mobility.	Wilhoit E D [112], Cohen B [23], Ricci M [113].
7	Organizational interaction	Strengthen the sustainable development of urban travel through social, organizational, social media, and other interactive ways.	Mavlutova I [114], Manca F [115]
8	Financial subsidy	The government provides financial subsidies or tax incentives to reduce the cost of shared mobility and attract more users.	Wang T [116], Cohen B [23]
9	Shared mobility rewards	Develop a green shared mobility plan, allowing users to accumulate travel rewards through shared mobility, which can be exchanged for rewards or concessions.	Tirachini A [117], Cantelmo G [118]
10	Transportation planning and measures	Supporting urban transportation planning and transportation measures, supporting shared mobility and green travel.	Schönauer R [119], Caulfield B [120], Wu X [121]
11	Dedicated lane	Build and maintain high-quality shared mobility lanes, especially shared bike lanes, to meet the demand for green travel and ensure cycling safety.	Zhuang D [122], Foletta N [123]
12	Parking facility	Set up intelligent parking points, provide convenient parking services, and reduce the problem of disorderly parking of shared mobility tools.	Larsen J [124], Van der Spek S C [125], Chen Z [126]
13	Integration of transportation facilities	The integrated use of a variety of transportation means provides convenient transfer services and improves user travel efficiency.	Shen Y [127], Bi H [128]
14	Operation technology of shared mobility	Develop various operational technologies to cope with shared mobility and promote the implementation of shared mobility and green travel from the technical level.	Ghosh S [129], Ricci M [113], Pfrommer J [130], Zhang D [131],
15	User detection technology	Big data is used to detect users, optimize the delivery and scheduling strategies of shared mobility tools, and improve resource utilization efficiency.	Chang X [132], Barnett J [133]
16	The use effect of the application	Shared mobility application effect evaluation and improvement.	Di Dio S [134], Du M [135]
17	Diversified payment methods	Provides a variety of payment methods for the convenient use of users, and effectively improves the efficiency of enterprises.	Kaviti S [136], Zhi D [137]
18	Promulgation of laws and regulations	Relevant policies and regulations formulated by the government play a decisive role in the legality, operation norms, and safety standards of shared mobility. The stability and foresight of policies directly affect the long-term development of the industry.	Rodriguez D B [138], Castellanos S [139]
19	Cross-industry cooperation	The cross-border cooperation between shared mobility and urban planning, public transportation, energy, real estate, and other fields will help form a more complete transportation ecosystem and promote resource sharing and efficient use.	Li M [140], Zhang D [141]
20	Standardization and normalization	The establishment of unified industry standards and norms will help improve service quality, reduce vicious competition, and promote the healthy and orderly development of the shared mobility market.	Narang N K [142], Su Y S [24]
21	Cost control	Shared mobility platforms need to effectively control operating costs, including vehicle acquisition, maintenance, insurance, personnel salaries, and technology research and development investment. By adopting more efficient vehicle scheduling algorithms, optimizing vehicle configuration, reducing energy consumption, and other means, unit operating costs can be reduced and profitability improved.	Campbell A A [13], Gansterer M [143]
22	Internationalization strategy and globalization layout	With the in-depth development of globalization, the shared mobility platform has begun to implement the internationalization strategy and a global layout. They enter overseas markets and expand their business by setting up branches abroad, partnering with or acquiring local companies.	Shaheen S A [144]
23	Recycling and reuse	Establish the recycling and reuse mechanism of shared mobility tools to reduce resource waste.	Mao G [145], Luo H [146], Lai X [147]
24	Low-carbon operation	Reduce carbon emissions during operations and establish sustainable operating mechanisms.	Zhang Z [148], Zhang B [149], Zhou Y [150]
25	User feedback	Establish an effective user feedback mechanism to solve user problems in time and improve user satisfaction.	Eren E [151], Hu J W [152]
26	User participation	Users are invited to participate in the design and service of shared mobility tools to enhance the sense of belonging and enthusiasm of users.	Lou L [153], Ban S [154]

).

The 20–30 min interviews with each expert focused on the sustainability aspects of shared mobility. Ten experts were asked to rate 26 factors of importance. On the Likert scale, values from 1 to 7 represent “strongly disagree”, “relatively disagree”, “disagree”, “generally agree”, “relatively agree”, “agree”, and “strongly agree”, respectively. Each person who completed the expert interview received a gift. Next, the collected 10-point questionnaire was input, and the scores of each factor were added and divided by 10 to obtain the average scores of the factors. The average score on a scale of 1–7 is 4. Therefore, after scoring by experts, factors with an average score greater than 4 were used as screening factors for this study, whereas those with an average score lower than 4 were excluded. According to the interview results of ten experts, 21 influencing factors related to the sustainable development of shared mobility are selected (

Table 6. Detailed table of pretest experts.

Expert	Department	Working Years	Familiarity
Mr. Zhang	Traffic management department	8	More familiar
Mr. Wang	Traffic management department	6	Very familiar
Ms. Li	Traffic management department	5	Very familiar
Mr. Zhang	Traffic police group	7	More familiar
Ms. Zhao	Traffic police group	4	Familiar
Mr. Sun	Didi Chuxing	3	Familiar
Mr. Qian	Didi Chuxing	6	Very familiar
Mr. Li	Gofun	5	Very familiar
Mr. Wang	Meituan	4	Familiar
Mr. Zhou	Meituan	7	More familiar

).

In addition, from 5 July 2024 to 27 July 2024, we again invited 30 experts to complete a structured questionnaire. They include public officials in traffic management departments, managers of shared mobility operating companies, and shared mobility research experts. All of them have participated in the management and research of shared mobility for many years, possess relevant practical experience and theoretical knowledge, understand the actual situation of the sustainable development of shared mobility, and can effectively guarantee the quality of expert questionnaires (

Table 7. Factors influencing the sustainable development of shared mobility based on TDM theory after screening.

Dimensions	Coding	Factors
1. Shared mobility education	A1	Shared mobility education
	A2	Shared mobility publicity
	A3	Shared mobility “environment” information
2. Travel health	B1	Shared mobility health database
	B2	Improve health
3. Community organization	C1	Community organization and advocacy
	C2	Organizational interaction
4. Government policies and incentives	D1	Financial subsidy
	D2	Shared mobility rewards
	D3	Transportation planning and measures
5. Infrastructure construction	E1	Dedicated lane
	E2	Parking facility
	E3	Integration of transportation facilities
6. Intelligent technology	F1	Operation technology of shared mobility
	F2	User detection technology
7. APP function optimization	G1	The use effect of the application
	G2	Diversified payment methods
8. Social responsibility	H1	Recycling and reuse
	H2	Low-carbon operation
9. User behavior	I1	User feedback
	I2	User participation

). The experts rated 21 influencing factors based on experience and expertise. The rating system is 0–4, where 0 represents no impact, 1 represents weak impact, 2 represents moderate impact, 3 represents strong impact, and 4 represents extremely strong impact.

4. Results

4.1. FUZZY-DEMATEL Result Analysis

After the 21 influencing factors are identified, the combined impact matrix (

Table 8. Demographic characteristics of the participants in the questionnaire survey.

Gender	Number	Percentage
Male	18	60%
Female	12	40%
Age group
18 to 25	11	37%
26–35	7	23%
Over 35	12	40%
Educational background
Junior college	1	3%
Undergraduate course	19	63%
Master’s degree or above	10	34%
income
1000–3000.	1	3%
3000–5000.	8	27%
More than 5000	21	70%
The number of shared mobility services used per week
1–5 times	17	57%
6 to 10 times	10	33%
More than 10 times	3	10%
Position
Public officer in traffic management department	10	33%
Manager of shared mobility operating company	10	33%
Shared mobility research expert	10	33%

) is obtained via the FUZZY-DEMATEL method according to the expert ratings of these indicators, which are used to calculate centrality and causality. Centrality is the effect of other factors on causality, in which the greater the centrality, the more important the element. The influence strength of the factors can be obtained through the combined impact matrix (

Table 9. Combined impact matrix.

	A1	A2	A3	B1	B2	C1	C2	D1	D2	D3	E1	E2	E3	F1	F2	G1	G2	H1	H2	I1	I2
A1	0	0.75	0.70	0.71	0.66	0.64	0.68	0.67	0.73	0.66	0.78	0.70	0.82	0.73	0.73	0.72	0.63	0.72	0.65	0.68	0.79
A2	0.73	0	0.76	0.62	0.62	0.67	0.71	0.73	0.68	0.63	0.75	0.66	0.70	0.62	0.52	0.45	0.54	0.65	0.56	0.75	0.88
A3	0.70	0.69	0	0.62	0.54	0.53	0.59	0.81	0.52	0.76	0.77	0.62	0.83	0.48	0.52	0.57	0.57	0.80	0.61	0.67	0.76
B1	0.62	0.61	0.66	0	0.62	0.52	0.59	0.76	0.62	0.69	0.80	0.81	0.80	0.79	0.82	0.67	0.69	0.67	0.76	0.66	0.69
B2	0.58	0.61	0.51	0.61	0	0.59	0.62	0.67	0.66	0.64	0.76	0.69	0.59	0.45	0.55	0.68	0.63	0.69	0.70	0.69	0.75
C1	0.58	0.68	0.60	0.64	0.58	0	0.60	0.64	0.68	0.71	0.63	0.69	0.70	0.55	0.55	0.63	0.55	0.69	0.56	0.75	0.69
C2	0.62	0.66	0.61	0.64	0.60	0.68	0	0.73	0.71	0.75	0.73	0.84	0.77	0.55	0.59	0.70	0.62	0.73	0.72	0.70	0.74
D1	0.62	0.62	0.60	0.69	0.53	0.62	0.72	0	0.87	0.80	0.83	0.86	0.73	0.76	0.72	0.72	0.69	0.67	0.72	0.73	0.83
D2	0.64	0.70	0.61	0.68	0.64	0.71	0.63	0.66	0	0.66	0.73	0.76	0.73	0.59	0.61	0.73	0.78	0.55	0.68	0.73	0.78
D3	0.66	0.71	0.62	0.74	0.73	0.66	0.67	0.68	0.57	0	0.84	0.83	0.83	0.75	0.65	0.53	0.69	0.64	0.76	0.76	0.64
E1	0.59	0.69	0.77	0.74	0.63	0.60	0.55	0.71	0.68	0.88	0	0.88	0.90	0.64	0.78	0.67	0.52	0.72	0.69	0.70	0.69
E2	0.62	0.66	0.61	0.68	0.63	0.76	0.66	0.70	0.61	0.83	0.81	0	0.73	0.69	0.60	0.73	0.55	0.57	0.70	0.69	0.64
E3	0.62	0.67	0.55	0.61	0.67	0.63	0.55	0.69	0.74	0.83	0.79	0.75	0	0.60	0.58	0.83	0.67	0.66	0.66	0.74	0.72
F1	0.67	0.66	0.62	0.72	0.63	0.59	0.53	0.68	0.61	0.79	0.82	0.87	0.69	0	0.68	0.75	0.78	0.73	0.64	0.73	0.73
F2	0.66	0.62	0.48	0.80	0.53	0.59	0.52	0.68	0.53	0.79	0.63	0.68	0.66	0.68	0	0.76	0.72	0.69	0.64	0.78	0.75
G1	0.48	0.55	0.52	0.70	0.50	0.54	0.52	0.70	0.72	0.80	0.65	0.69	0.67	0.64	0.77	0	0.75	0.61	0.56	0.77	0.73
G2	0.62	0.60	0.53	0.58	0.60	0.55	0.49	0.60	0.77	0.55	0.52	0.66	0.51	0.61	0.62	0.66	0	0.51	0.48	0.74	0.72
H1	0.77	0.77	0.61	0.64	0.55	0.67	0.56	0.63	0.64	0.68	0.57	0.59	0.63	0.62	0.62	0.52	0.53	0	0.69	0.64	0.53
H2	0.64	0.80	0.75	0.59	0.63	0.58	0.64	0.62	0.60	0.68	0.68	0.73	0.64	0.70	0.66	0.52	0.60	0.84	0	0.58	0.69
I1	0.70	0.67	0.66	0.66	0.69	0.55	0.59	0.64	0.63	0.76	0.73	0.76	0.69	0.72	0.71	0.77	0.70	0.63	0.70	0	0.80
I2	0.70	0.72	0.66	0.69	0.66	0.61	0.59	0.72	0.66	0.71	0.70	0.82	0.76	0.72	0.68	0.83	0.78	0.69	0.64	0.80	0

), which shows the centrality and causality rankings.

Strength analysis of the influencing factors:

(1) Centrality degree ranking analysis: According to Table 9, the top centrality degree rankings are as follows: user participation (I2), traffic planning and measures (D3), dedicated lanes (E1), parking facilities (E2), financial subsidies (D1), and user feedback (I1). Therefore, these six factors are highly correlated with other factors. Among them, the largest centrality degree score is user participation (I2), which indicates that the I2 factor plays a significant role in the overall system structure and is closely related to the correlation of other factors.

(2) Causality degree analysis: The higher the value is, the greater the influence of the factor on other factors, and the smaller the influence of other factors. According to Table 9, the highest score is for organizational interaction (C2), indicating that this factor has a greater influence on other factors and is less influenced by other factors.

(3) Analysis of the degree of influence: The larger the value is, the stronger the influence of the factor on other factors. In Table 9, the largest impact is on financial subsidies (D1), followed by shared mobility education (A1). These factors therefore require greater attention.

(4) Analysis of the degree of influence: The larger the value is, the stronger the influence of other factors. Among them are parking facilities (E2), traffic planning and measures (D3), dedicated lanes (E1), and traffic facility integration (E3). These are the factors that are more heavily influenced and more difficult to implement.

According to Table 9, a DEMATEL causal analysis of the factors affecting the sustainable development of shared mobility is created, and the causal analysis results are shown in Figure 2. The X-axis represents the degree of causation, and the Y-axis represents the degree of centrality. An influencing factor with a value greater than 0 is the causality factor, which directly affects the sustainable development of shared mobility. Influencing factors with values less than 0 are outcome factors and are affected by the causality factor, which indirectly affects the sustainable development of shared travel. The higher the centrality degree value of a factor is, the higher the ranking of the factor and the stronger the correlation with other factors.

(5) Causality factors: financial subsidy (D1), shared mobility health database (B1), shared mobility education (A1), shared mobility operation technology (F1), shared mobility reward (D2), low-carbon operation (H2), user detection technology (F2), organizational interaction (C2), shared mobility “environment” information (A3), community organization and advocacy (C1), and health improvement (B2).

(6) Outcome factors: User participation (I2), transportation planning and measures (D3), dedicated lanes (E1), parking facilities (E2), user feedback (I1), the integration of transportation facilities (E3), shared mobility publicity (A2), the use effect of the application level (G1), recycling and reuse (H1), and diversified payment methods (G2).

4.2. ISM Result Analysis

Based on the data of the combined impact matrix, the reachability matrix is obtained, and the reachability matrix shows whether there is a direct relationship between elements (

Table 10. Analysis of the influence factors of DEMATEL.

	Influencing Degree	Influenced Degree	Centrality Degree	Causality Degree	Rank	Factor Attribute
A1	15.76	14.36	30.12	1.40	9	Causality factor
A2	14.84	15.01	29.85	−0.17	12	Outcome factor
A3	14.51	13.96	28.47	0.55	18	Causality factor
B1	15.47	14.92	30.39	0.55	8	Causality factor
B2	14.19	13.76	27.95	0.43	21	Causality factor
C1	14.22	13.78	28.00	0.45	19	Causality factor
C2	15.30	13.51	28.81	1.79	17	Causality factor
D1	15.98	15.30	31.29	0.68	5	Causality factor
D2	15.17	14.77	29.93	0.40	11	Causality factor
D3	15.57	16.28	31.85	−0.71	2	Outcome factor
E1	15.67	16.18	31.85	−0.51	3	Outcome factor
E2	15.06	16.57	31.63	−1.51	4	Outcome factor
E3	15.15	16.03	31.18	−0.88	7	Outcome factor
F1	15.52	14.47	29.99	1.05	10	Causality factor
F2	14.77	14.52	29.28	0.25	15	Causality factor
G1	14.42	15.02	29.45	−0.60	13	Outcome factor
G2	13.41	14.55	27.95	−1.14	20	Outcome factor
H1	13.98	14.98	28.96	−1.01	16	Outcome factor
H2	14.71	14.70	29.41	0.01	14	Causality factor
I1	15.34	15.93	31.27	−0.59	6	Outcome factor
I2	15.74	16.18	31.92	−0.44	1	Outcome factor

). Interval and hierarchical decomposition of the reachability matrix is required (

Table 11. Reachability matrix.

	A1	A2	A3	B1	B2	C1	C2	D1	D2	D3	E1	E2	E3	F1	F2	G1	G2	H1	H2	I1	I2
A1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
A2	1	1	0	1	0	0	0	1	1	1	1	1	1	1	0	1	1	1	1	1	1
A3	0	1	1	1	0	0	0	1	0	1	1	1	1	0	0	1	0	1	0	1	1
B1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
B2	0	0	0	0	1	0	0	1	0	1	1	1	1	0	0	1	0	1	0	1	1
C1	0	1	0	0	0	1	0	1	0	1	1	1	1	0	0	1	0	1	0	1	1
C2	1	1	0	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
D1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
D2	1	1	0	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
D3	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
E1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
E2	1	1	0	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
E3	1	1	0	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
F1	1	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
F2	0	1	0	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
G1	0	1	0	1	0	0	0	1	1	1	1	1	1	0	0	1	0	1	0	1	1
G2	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	0	0	1	1
H1	0	0	0	0	0	0	0	1	0	1	1	1	1	0	0	0	0	1	0	1	1
H2	0	1	0	1	0	0	0	1	1	1	1	1	1	1	1	1	0	1	1	1	1
I1	1	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1
I2	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1

). In this study, a hierarchical structure of 21 factors was constructed, and after five iterations, a multilayer recursive structure model was constructed, as shown in Figure 3.

The following conclusions can be drawn from Figure 3:

(1) Top-level influencing factors (surface influencing factors): transportation planning and measures (D3), dedicated lanes (E1), parking facilities (E2), diversified payment methods (G2), recycling and reuse (H1), user feedback (I1), and user participation (I2). The above seven top-level factors are also performance layer factors. I1 and I2 represent user behavior, which reflects the user’s attitude towards shared mobility. The sustainable development of shared mobility cannot be achieved without the active participation of users [153]. The sustainability of shared mobility can be directly reflected through the feedback and participation of users. E1 and E2 also belong to infrastructure construction, which is also a direct reflection of the sustainable aspects of shared mobility. The development of infrastructure is an important standard for measuring the sustainability of urban transportation [155]. H1 is not only the core link of overall sustainable development but also an important indicator for assessing the impact of shared mobility on the environment [156]; it is also part of the social responsibility aspect. D3 is the responsibility of the traffic management department. The planning of shared transportation facilities and the promulgation of shared mobility policies directly affect the development direction of shared mobility in urban transportation and directly reflect the attitudes of government departments towards shared mobility in terms of policies and incentives [120]. G2 is the most intuitive behavior of the public towards shared mobility. Diversified payment methods give the public the convenience of travel and help motivate users to choose the transportation mode of shared mobility [137].

(2) The second layer of influencing factors (shallow influencing factors): shared mobility publicity (A2), shared mobility health databases (B1), financial subsidies (D1), shared mobility rewards (D2), transportation facility integration (E3), and the application effect (G1). These factors have shallow influences, which indirectly affect the sustainable development of shared mobility through the top seven influencing factors and are also affected by the next layer of factors. Both D1 and D2 are government policies and incentives. The behavior dependence and habit formation of shared mobility cannot be separated from the policy support at the government level [117], which includes travel rewards for travel users and policy support for enterprises providing shared mobility services. A2 can promote shared mobility through various media channels, change travel methods, cultivate the willingness to travel green, and contribute to the sustainable development of transportation [22]. The influencing factors of B1, E3, and G1 are all improvements at the technical level. These technical means can provide technical support for shared mobility and provide technical guarantees for convenient shared mobility [128]. From the perspective of the second level of influencing factors, mainly technology and government policies are considered.

(3) Tier 3 influencing factors: shared mobility “environment” information (A3), health improvement (B2), community organization and advocacy (C1), user detection technology (F2), and low-carbon operation (H2). These five factors are the transitional factors for the sustainable development of shared mobility, and managers need to consider these specific factors when making sustainability decisions. These factors constitute the middle layer of the whole structure and play a role in connecting the previous layer and the next layer. They affect elements at the top as well as those at the bottom. A3 provides shared mobility users with specific “green and sustainable” content in a visible range, such as providing a green landscape [104], a green travel logo [106], etc. B2 is one of the benefits of shared mobility. At the environmental level, it can protect the environment and reduce pollution [111]. At the physical level, vehicles such as shared bicycles can also improve and maintain health [110]. C1 is an important means to improve citizens’ participation in shared mobility. This influencing factor has a subtle impact on citizens’ travel and requires long-term operation. F2 is the use of technical means to ensure the scheduling and use of shared transportation. H2 aims to follow the core of low-carbon emissions and establish a sustainable operation mode throughout the whole process of shared mobility.

(4) Layer 4 and Layer 5 influencing factors (bottom influencing factors): shared mobility education (A1), shared mobility operating technology (F1), and organizational interaction (C2). These elements are the foundation of the whole system and can continue to influence higher-level factors. A1 is the most important means to establish citizens’ awareness of “shared mobility”, which can cultivate awareness of environmental protection and sustainable development and cultivate the habit of green travel [100]. F1 is the technical basis for realizing shared mobility. China’s shared mobility services are provided mainly by third-party companies, and the operating technologies of these companies determine their development direction and operation effectiveness. Therefore, these companies need to achieve their operational objectives through a variety of technological means to ensure the convenience and sustainability of shared mobility [113]. C2, as the lowest factor of this system, also shows that shared mobility is not a single sector or a single industry and requires interaction and integration at all levels of society to promote the sustainable development of shared mobility as a whole [114].

4.3. MICMAC Result Analysis

By adding the elements of the reachability matrix with a median value of 1, the driving power and the dependence of each factor are obtained (

Table 12. Explanation of the reachability matrix.

Factor	Factor (Algebra)	Reachable Set	Antecedent Set	Intersection Set	Level
A1	1	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,4,7,8,9,10,11,12,13,14,20,21	1,2,4,7,8,9,10,11,12,13,14,20,21	IV
A2	2	1,2,4,8,9,10,11,12,13,14,16,17,18,19,20,21	1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,19,20,21	1,2,4,8,9,10,11,12,13,14,16,19,20,21	II
A3	3	2,3,4,8,10,11,12,13,16,18,20,21	1,3,4,8,10,11,14,20,21	3,4,8,10,11,20,21	III
B1	4	1,2,3,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,7,8,9,10,11,12,13,14,15,16,19,20,21	1,2,3,4,8,9,10,11,12,13,14,15,16,19,20,21	II
B2	5	5,8,10,11,12,13,16,18,20,21	1,5,8,10,11,14,21	5,8,10,11,21	III
C1	6	2,6,8,10,11,12,13,16,18,20,21	1,6,8,10,11,21	6,8,10,11,21	III
C2	7	1,2,4,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,7,8	1,7,8	V
D1	8	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21	II
D2	9	1,2,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,4,7,8,9,10,11,12,13,14,15,16,19,20,21	1,2,4,8,9,10,11,12,13,14,15,16,19,20,21	II
D3	10	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	I
E1	11	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	I
E2	12	1,2,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	I
E3	13	1,2,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21	1,2,4,8,9,10,11,12,13,14,15,16,18,19,20,21	II
F1	14	1,2,3,4,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,4,7,8,9,10,11,12,13,14,15,19,20,21	1,2,4,8,9,10,11,12,13,14,15,19,20,21	IV
F2	15	2,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,4,7,8,9,10,11,12,13,14,15,19,20,21	4,8,9,10,11,12,13,14,15,19,20,21	III
G1	16	2,4,8,9,10,11,12,13,16,18,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,19,20,21	2,4,8,9,10,11,12,13,16,20,21	II
G2	17	10,11,12,17,20,21	1,2,4,7,8,9,10,11,12,13,14,15,17,20,21	10,11,12,17,20,21	I
H1	18	8,10,11,12,13,18,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21	8,10,11,12,13,18,20,21	I
H2	19	2,4,8,9,10,11,12,13,14,15,16,18,19,20,21	1,2,4,7,8,9,10,11,12,13,14,15,19,20,21	2,4,8,9,10,11,12,13,14,15,19,20,21	III
I1	20	1,2,3,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,8,9,10,11,12,13,14,15,16,17,18,19,20,21	I
I2	21	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21	1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21	I

and

Table 13. MICMAC quadrant analysis results.

	Driving Power	Dependence Power
A1	21	13
A2	16	18
A3	12	9
B1	18	17
B2	10	7
C1	11	6
C2	18	3
D1	21	20
D2	17	16
D3	20	21
E1	20	21
E2	17	21
E3	17	20
F1	19	15
F2	16	14
G1	12	19
G2	6	15
H1	8	20
H2	15	15
I1	18	21
I2	20	21

). The sum of the driving power is taken as the horizontal coordinate, and the sum of the dependencies is taken as the vertical coordinate. The average values were divided into quadrants, and the MICMAC quadrants were constructed (Figure 4). Figure 4 is divided into four quadrants, namely spontaneous factors, dependent factors, related factors, and independent factors.

The first quadrant is the autonomous cluster, which is characterized by low dependence power and low driving power. In the whole system, the connection with other influencing factors is simple and weak. One of the factors is improved health (B2), which is located in the middle layer and has weak association characteristics. With respect to the underlying shared mobility education (A1), health aspects need to be considered when education is conducted. Moreover, it also affects superficial financial subsidies (D1), the integration of transportation facilities (E3), and the effectiveness of application use (G1). Although B2 factors are spontaneous factors, it is necessary to focus on the correlation between the above relevant levels of factors and B2 factors. In the process of promoting the sustainable development of shared mobility, the management department adjusts other factors to strengthen the B2 factor. Similarly, the effect of B2 factors on strengthening other influencing factors is not ideal.

The second quadrant is the independent cluster, which has lower factor dependence and higher driving power. Shared mobility “environment” information (A3), organizational interaction (C2), and community organization and advocacy (C1) are all at the deep level of the system results, especially the C2 factor, which is at the bottom of the system. Among them, the C1 and C2 factors belong to community organization. As a result, community organization has high driving power to have a lasting impact on the system.

The third quadrant is the dependent cluster, with strong dependence and low driving power. All these factors belong to the surface layer of the structure, and it is difficult for them to have an effect on the following hierarchical factors. Among the factors are recycling and reuse (H1) and diversification of payment methods (G2). Both of these factors are highly dependent on technical support in the lower factor.

The fourth quadrant is the linkage cluster, which has high dependence and high driving power, and a change in this factor will cause changes in the linkages of other factors. It includes traffic planning and measures (D3), dedicated lanes (E1), parking facilities (E2), user feedback (I1), and user participation (I2) in the L1 layer. The linkage factors of this layer are divided into two main aspects: D3, E1, and E2 factors under the jurisdiction of the transportation department and I1 and I2 factors concerning shared mobility operators. Shared mobility promotion (A2), the shared mobility health database (B1), financial subsidies (D1), shared mobility rewards (D2), the integration of transportation facilities (E3), and the practical effects of applications (G1) are in L2. User detection technology (F2) and low-carbon operations (H2) are in layer 3. Shared mobility education (A1) and shared mobility operation technology (F1) are on level L5. In general, the relevant factors are mainly concentrated at the surface direct levels (L1, L2) and deep root levels (L5), which indicates that these factors have a greater impact on the whole system, especially the A1 and F1 factors at the L5 level, which are strongly correlated and strongly impact the upper-level factors. They play a driving role in the whole system and are deep core factors.

Overall, the number of sustainable development factors in shared mobility is relatively large. Therefore, managers should comprehensively consider the relevance of these factors when making decisions and adjust the use of factors from an overall perspective.

5. Discussion

This paper studies the influencing factors and strategy analysis of the sustainable development of shared mobility under the framework of TDM and describes how the FUZZY-DEMATEL-ISM-MICMAC fusion model is applied to the sustainable development of shared mobility. First, the methodologies of the FUZZY, DEMATEL, ISM, and MICMAC models are expounded, the calculation process and characteristics of each method are explained, and the idea of constructing a FUZZY-DEMATEL-ISM-MICMAC model is proposed. On the basis of this research, the influencing factors of shared mobility under the TDM theory are constructed and analyzed, and the influencing factor system of the sustainable development of shared mobility is formed. This paper is helpful for government management departments and the operating companies of shared mobility as a reference for decision-making. Understanding the logical relationships among the factors affecting the sustainable development of shared mobility will help managers design and formulate relevant strategies, adjust the strength of these factors in the context of TDM theory, and improve the popularity and utilization rate of shared mobility. Managers need to focus on community organizing aspects, such as organizational interaction (C2) and community organizing and advocacy (C1), especially if C2 factors are at the core of the system. The sustainable development of shared mobility can prioritize core factors at a hierarchical level. From the theoretical framework of TDM, the following findings can be found:

(1) Factors influencing soft strategies in the theoretical framework of TDM

In the theory of TDM, a soft strategy is not the “material” content of government policies, technologies, and facilities but rather the intangible impact on people’s consciousness and thinking. Specifically, it encourages citizens to change their traffic behavior through various means and improves the quality of alternative methods. TDM theory uses a variety of “hard” strategies in practical applications, which have a good effect on traffic behavior changes in short-term development but have little effect on long-term behavioral changes [157]. In China, when shared mobility was just emerging, the government’s policy support and preferential consumption of operating companies played a positive role in the initial expansion stage of shared mobility. However, shared mobility has undergone more than 10 years of development in China and needs to shift to a more sustainable model [158]. With the rapid development of urban transport in China and the further increase in public transport ridership, TDM strategies need to be further expanded [159]. Soft strategy behavior is effective at this stage [160,161,162,163].

In this study, soft strategy influencing factors include the following: shared mobility education (shared mobility education A1, shared mobility publicity A2, and shared mobility “environment” information A3); healthy travel (shared mobility health database B1 and improving health B2); community organization (community organization and advocacy C1 and organization interaction C2); social responsibility (recycling and reuse H1 and low-carbon operation H2); and user behavior (I1 and I2). Among them, A1, A3, B1, B2, C1, C2, and H2 are all causality factors. The soft strategy in TDM basically belongs to the “cause” side of the influencing factors. In the ISM model, A1, A3, B2, C1, C2, and H2 belong to the deep structure, whereas A3, B2, C1, and H2 are the system structures of L3 (mid-level levels) belonging to the middle layer factors and play a role in the link between the preceding and the following, and A1 and C2 are the system structures of L5. The deep root level (L6) is the underlying factor and is the basis for the sustainable development of shared mobility. Over time, factors that continue to affect the upper level and are easily ignored need to be brought to the attention of managers. Among them, B2 is a spontaneous factor and ranks 21st in terms of its centrality degree, which indicates that the B2 factor is not closely related to other factors and it can be redundant to consider this factor in the sustainable development of shared mobility at this stage.

In terms of soft strategy, we should focus on the two influential factors of “shared mobility education” and “community organization”. These two factors are the basis of the system. The government and operating institutions should strengthen the education of shared mobility. Previous studies have focused on the safety and security of shared mobility [164], which is only one aspect of education. In terms of education, there is more content related to transportation education [165], and there are relatively few educational studies on shared mobility. This study revealed that this influencing factor is a deep influencing factor, and at the same time, it plays a role in long-term influence. Therefore, it is necessary to attract the attention of management departments and formulate long-term sustainable development strategies. In terms of community organization, as an environmentally friendly city, community interaction plays an important role in the development of public travel [166]. As the demand for mobility increases, so do opportunities for community interaction, thus contributing to improved quality of life, well-being, and sustainable social development [167]. Community interaction, as a measure to change public travel behavior, is initiated more by users, communities, social platforms, and other influencing factors, which are also the most basic factors in the sustainable development system of shared mobility and need to attract sufficient attention.

(2) Factors influencing the hard strategy in the theoretical framework of TDM

The validity of the hard strategy in TDM theory has been discussed in previous studies. Examples include increasing traffic stops [168], paying for travel [169], and the impact of traffic policy [170] These are discussed as unilateral influencing factors in TDM. In this study, the influencing factors of the hard strategy are divided into government policies and incentives (fiscal subsidy D1, shared mobility reward D2, and transportation planning and measures D3). Infrastructure construction (dedicated lane E1, parking facility E2, and traffic facility integration E3); intelligent technology (shared mobility operation technology F1 and user detection technology F2); and APP function optimization (application effect G1 and diversified payment methods G2). In the DEMATEL model, the D3, E1, E2, E3, G1, and G2 influence factors are outcome factors, so “infrastructure construction” and “APP function optimization” are the main result factors. The overall degree of centrality ranks high. This shows that a hard strategy is the main manifestation of the sustainable development of shared mobility and often receives the attention of managers. According to the ISM hierarchy, D1, D2, E3, and G1 belong to the L2 layer and D3, E1, E2, and G2 belong to the L1 layer. This finding shows that the factors influencing hard policy are mainly found in surface direct levels. The surface level is the most easily discovered and concerning influencing factor, but it is not the basic factor for the sustainable development of shared mobility. Moreover, according to the MICMAC quadrants, almost all the hard strategies belong to this area and are related factors, which are more susceptible to the influence and restrictions of other factors. From the perspective of single factors, traffic planning and measures D3, dedicated lane E1, parking facilities E2, and financial subsidies D1 are the most influential factors in the sustainable development of shared traffic and the hard strategy of TDM. Managers can provide shared mobility results based on decisions that reinforce these factors. Among these influencing factors, the main factors that play a role are those under the jurisdiction of government management departments, such as traffic planning and measures D3, special lanes E1, parking facilities E2, and financial subsidies D1. Therefore, the government transportation department is the leading party in the hard strategy of TDM and needs to play a major role in the sustainable development of shared mobility. Among them, the influencing factors F1, F2, G1, and G2, which are led by the operating organization, are evenly distributed at each level of the entire structural system. Therefore, operating organizations need to make decisions at the micro level at all levels, and more attention needs to be paid to the detailed factors of shared mobility.

6. Conclusions

6.1. Theoretical Significance

The integration model of FUZZY, DEMATEL, ISM, and MICMAC is adopted to analyze the factors influencing the sustainable development of shared mobility from external to internal layers and from shallow to deep layers. According to the questionnaire prediction results of 10 experts, the influencing factors were screened, and 21 influencing factors from nine dimensions were determined according to the theoretical framework of TDM. Then, through an expert questionnaire survey, 21 factors were scored from 0 to 4, the objectivity of the questionnaire data was improved by the use of triangular fuzzy numbers, and a direct impact matrix was obtained. The DEMATEL method was used to analyze the influencing factors of shared mobility sustainable development at the macro level, and the degree of impact, degree of effect, and degree of influence were analyzed. The ISM method is used to establish a recursive structure model to explain the hierarchical relationships and influence relationships among influencing factors, which is the structural basis of shared mobility influencing factors. The MICMAC method was used to cluster and summarize the factors and explain the deep connotation of the structure.

First, the DEMATEL causal diagram shows that shared mobility education (A1), shared transportation operation technology (F1), and organizational interaction (C2) are all causality factors and are located at the bottom of the ISM recursive structure model. Therefore, they are the fundamental reasons for the sustainable development of shared mobility in the first stage. Moreover, the MICMAC quadrant diagram reveals that it has the characteristics of high driving power, which also explains why these three factors have an impact on the upper factors.

Second, the ISM recursive structure model is divided into five levels. The third layer is the middle layer, which includes shared mobility “environmental” information (A3), health improvement (B2), community organization and advocacy (C1), user detection technology (F2), and low-carbon operations (H2). The DEMATEL analysis revealed that these influencing factors are all causality factors. Moreover, according to the MICMAC quadrant diagram, A3, H2, and F2 are high driving power factors and have a large influence on the upper layer (L2). The validity and correctness of the method are verified.

Third, the top layer of the ISM recursive model includes traffic planning and measures (D3), dedicated lanes (E1), parking facilities (E2), diversified payment methods (G2), recycling and reuse (H1), user feedback (I1), and user participation (I2). The DEMATEL model revealed that these influencing factors are all outcome factors, and their centrality degree score is high, which indicates that these factors cannot affect other factors but have high importance. The MICMAC chart also shows that these factors are linked clusters, which are related to high dependence power and high driving power, showing its robustness.

6.2. Key Factors That Require Attention

The results of this study show that the current shared mobility industry in China is still highly dependent on “hard strategies” at the government level. The government should increase industrial support and improve the construction of shared mobility facilities.

According to the “hard strategy” under the TDM framework, it is necessary to focus on the following key factors:

(1) Transportation planning and measures: The government should scientifically plan the transportation network to ensure that shared mobility facilities are coordinated with urban development. The travel demand should be forecast through big data analysis, the layout of bus routes and stations should be optimized, and seamless connections among multiple travel modes, such as shared bicycles and online car hailing, should be promoted.

(2) Dedicated lanes: Establish or optimize dedicated lanes for shared mobility, such as bus lanes and bicycle lanes, to ensure the priority right of way for shared mobility tools. This will not only improve the speed and safety of shared mobility but also guide more citizens to choose green travel methods and ease urban traffic pressure.

(3) Parking facilities: Build sufficient shared mobility parking facilities, such as shared bicycle parking areas and electric vehicle charging piles, to make parking easier for users. Moreover, technological means such as intelligent parking systems are used to improve the utilization rate and management efficiency of parking facilities.

(4) Financial subsidies: The government supports the development of shared mobility enterprises through financial subsidy policies. This includes subsidies for business operating costs and incentives for technological innovation and research and development investment. Financial subsidies can reduce user travel costs, improve the market competitiveness of shared mobility, and guide social capital to invest in the field of shared mobility.

(5) Operation technology: The shared mobility platform uses advanced operation technology, such as big data analysis and artificial intelligence scheduling, to achieve optimal allocation and efficient utilization of vehicle resources. These technologies can accurately predict travel demand, dynamically adjust the volume of vehicles, improve operational efficiency, and reduce resource waste.

(6) User detection technology: Advanced user detection technology, such as facial recognition and behavioral analysis, is used to ensure the authenticity of user identity and behavioral compliance. This will not only improve the user experience but also effectively prevent fraud, maintain a healthy operating environment for the platform, and provide security for the sustainable development of shared mobility.

(7) Improve the user experience: Improving the ease of use and stability of the application. User satisfaction can be improved by continuously optimizing the application interface design, improving the system response speed, and adding convenient features (such as the ability to click to call a car, trip sharing, etc.). In addition, we cannot ignore the convenience of payment. Users can be incentivized through preferential activities, point redemption, and other ways to increase user stickiness.

According to the “soft strategy” under the TDM framework, several aspects can be focused on:

(1) Shared mobility education: Shared mobility education is the key to improving public awareness and acceptance of shared mobility. Through lectures, workshops, online courses, and other means, the concept, advantages, and usage methods of shared mobility can be popularized, and people’s environmental awareness and green travel habits can be cultivated.

(2) Organize interaction: Communities can organize shared mobility activities to encourage residents to participate together and form a good green travel atmosphere. Social media platforms expand the influence of shared mobility and attract more users to join by means of information dissemination and user interaction. Moreover, these organizations can collect user feedback to provide improvement suggestions for shared mobility platforms and promote continuous improvement in service quality.

(3) User characteristics: Areas with high population density, such as large cities, have greater demand for shared mobility because of traffic congestion and limited parking spaces. Using shared transportation can reduce the use of private cars in these areas. In areas with low population density, such as rural or suburban areas, where residents live in dispersed locations, the cost of shared mobility may be relatively high, and its usage rate may be relatively low. Young people are more receptive to new technologies and modes of transportation, and they constitute the main user group of shared mobility. Their dependence on smartphones and the internet makes it easier for them to use shared mobility services. Older people may have a lower acceptance of new technologies and are more inclined to use traditional modes of transportation. Differentiated shared mobility services can be designed for different population groups; small shared vehicles (such as shared bicycles and electric scooters) and convenient ride hailing services can be promoted in high-density urban areas. In low-density rural or suburban areas, community car sharing can be promoted to meet the long-distance travel needs of residents. The government should promote the integration of shared mobility with the public transportation system, such as setting up shared vehicle parking points at public transportation hubs to facilitate user transfers. Organizational interaction (C2), as an important way to link and share traffic and users, needs to be further strengthened at present.

(4) User psychology: The level of trust that users have in the shared mobility platform affects their willingness to use it. Concerns about platform service quality, privacy protection, and data security may lead to users being unwilling to use shared mobility services. The protection of user data on the platform should be strengthened, and a user trust mechanism should be established. This approach provides smarter and more user-friendly mobile applications to increase user convenience and satisfaction. Users are very sensitive to the safety of shared transportation vehicles, such as background checks for ride hailing drivers and the maintenance status of shared bicycles. Strict background checks and safety checks on ride-hailing drivers and shared vehicles should be conducted. Accident insurance and emergency rescue services should be provided to increase users’ sense of security. This requires that the operation technology of shared mobility (F1) plays an important role at this stage.

6.3. Research Limitations and Future Possibilities

Due to the use of expert questionnaires in this study, there may be potential bias in research conclusions regarding sampling methods, participant characteristics, or potential misreporting. In order to reduce the impact of reaction bias on this study, the description of the questionnaire was revised through reverse questioning and avoiding certain wording. Meanwhile, as an expert survey questionnaire (with a small sample of participants), this may not directly reduce reaction bias. However, it can help improve the quality and accuracy of data collection. In the process of conducting research and investigation, we also strictly followed the effective management of the investigation, including training survey personnel, ensuring the confidentiality of participants’ answers, and conducting research in a relaxed and comfortable environment.

However, in this study, due to the expert questionnaire approach, some potential biases are inevitable. For example, the aspect of “AI and autonomous driving”, which is a hot topic in the research field nowadays, is seldom used in shared mobility in China. For consumers, “AI and autonomous driving” makes traveling more convenient; but for practitioners, “AI and autonomous driving” brings fear. The reason for this is partly due to the prejudice against emerging technologies [171]. On the other hand, “AI and autonomous driving” in China affects the income of practitioners in real life (the price of services provided by driverless car companies in China is much lower than the market price, which is a very important reason), and even causes traffic jams, car accidents, and other traffic accidents.

At the same time, for Chinese managers, “social harmony and market stability” are the most important factors, and if “AI and autonomous driving” technologies are not yet mature, managers will be cautious about putting them to use. If “AI and autonomous driving” technologies are not yet mature, administrators are wary of putting them to use. This study also covers aspects of user behavior, such as “tolerance of shared mobility”, which is more likely to be a factor of user willingness [172]. Due to the participant characteristics of this study (relatively high income and had more choices in travel behavior), less attention was paid to the influences of “disadvantaged travelers” [35]. In this study, our questionnaire group consisted of mainly experts in the field. This may result in some “response bias” in this study, as the participants think about the content of the study more from the perspective of managers. Although, on a macro level, the sustainable development of shared mobility can be promoted, it also reveals that potential factors in shared mobility management have been neglected. These potential factors are also issues that require attention in this study.

Owing to the limitations of the current research resources and methods, this paper also has certain limitations. First, although this study uses the advantageous method of integrating four models, in the factor screening stage, there is inevitably subjectivity that cannot be identified, such as all the factors for the sustainable development of shared mobility. Second, new influencing factors will appear with the development of China’s transportation policy, and there are uncertain factors affecting this research, which will also change over time. Finally, since the support data in this study came from expert interviews, future studies can use exploratory factor analysis (EFA) to find the factor number of multiple variables and explain the internal relationships between factors. It is also possible to use the LOGIT-ISM-MICMAC combination model for effective research [173]. Therefore, as a systematic factor research method, how to effectively combine the ISM with other models is also a new direction for future research. In the face of different urban environments, it is necessary to conduct research on urban transportation characteristics to evaluate the factors influencing sustainable development, and this micro-level research is more effective for urban case studies. Therefore, in future research, the localization characteristics of urban transportation constitute one of the development directions of this study, requiring serious consideration by scholars.