How Autonomous Vehicles Shape Urban Traffic Sustainability: An Empirical Study Based on Structural Equation Modeling

Abstract

Currently, urban traffic flow is an increasing problem, and achieving sustainability in urban mobility is essential for enhancing both mobility and the quality of life. This study investigates the factors influencing urban traffic sustainability from two critical perspectives: autonomous vehicle policy governance and citizen perception. By utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), this study analyzes data from 502 valid responses gathered through online surveys. The findings reveal that, from the citizen perception perspective, both accessibility and safety play a significant role in advancing urban traffic sustainability, while smart mobility modes were found to have no substantial impact. From a policy governance standpoint, land transportation planning did not significantly influence citizens’ perceptions of accessibility, safety, or smart mobility. In contrast, vehicle management measures were positively correlated with these dimensions. Furthermore, the scope of service point support was found to enhance accessibility and smart mobility but did not have a noteworthy effect on safety. Vehicle deployment allocation, however, was positively associated with improvements in accessibility, safety, and smart mobility. This study offers valuable insights for urban traffic policy formulation, emphasizing the pivotal role of regulatory frameworks, vehicle management strategies, and urban infrastructure in fostering sustainable urban mobility.

1. Introduction

As urbanization and economic development accelerate, the urbanization levels in many countries have steadily risen, leading to a significant increase in the number of private vehicles [1]. This intensification of urbanization has driven up the demand for urban transport, making mobility challenges more pressing [2]. In major cities and transportation hubs, traffic volumes have already reached or surpassed the design capacity of roads, resulting in severe congestion [3,4]. Specifically, Lomax, Turner [5] predicted that by 2020, traffic congestion in the United States would cause travelers to lose up to 8.4 billion hours and consume an additional 4.5 billion gallons of fuel, amounting to an economic loss of $199 billion. This highlights not only the economic burden but also the significant environmental and societal consequences, such as increased energy consumption and reduced well-being for urban residents. Consequently, the development of a sustainable urban transport system that balances individual mobility with broader societal welfare has become a critical issue.

With the acceleration of urbanization, public transportation systems have played a pivotal role in alleviating traffic congestion and reducing carbon emissions [6]. For instance, they can help address the “last mil”. solution, assisting users from home to bus or subway stations, thereby enhancing the sustainability and appeal of urban transport [7]. Traditional public transportation modes often fail to cover the origin and destination points of residents’ trips, leading many passengers to rely on private cars or other unsustainable modes of transport to complete their last-mile travel.

Autonomous vehicles (AVs), as an emerging mode of transportation, have the potential to address this issue. Through flexible routing and on-demand services, AVs can offer passengers a seamless travel experience, reducing the reliance on private cars and enhancing the overall efficiency of public transport [8]. Autonomous vehicle services, such as “RoboTax”., represent a cutting-edge technological and service model that offers a safer and more reliable transportation alternative [9]. Autonomous vehicles are equipped with the ability to sense their environment and communicate seamlessly with surrounding vehicles and infrastructure. They optimize traffic signal control, enhance road capacity, and reduce accidents, thus making a substantial contribution to alleviating traffic congestion [10]. Additionally, autonomous vehicles alleviate the pressure on parking demand [11]. Their efficient parking methods, including stacked parking systems, greatly increase the utilization of parking spaces, thus reducing parking demand in commercial areas, freeing up curb spaces for public use, and improving overall road capacity [10,12]. In parking management, autonomous vehicles integrate IoT sensors and centralized management platforms, allowing for dynamic pricing adjustments and optimizing supply–demand matching, thus enhancing urban mobility and land use efficiency [13]. Furthermore, these vehicles can coordinate with smart infrastructure to enable smarter route selection, quicker response times, and denser vehicle spacing, all of which mitigate the impact of rising traffic demand [14]. Autonomous vehicles are also expected to promote flexible, on-demand car-sharing services, reducing reliance on private vehicles and their associated energy consumption [14,15]. As such, the advent of autonomous vehicles not only helps ease urban traffic challenges but also introduces a transformative mobility experience for travelers.

In recent years, autonomous vehicles have been subjected to large-scale trials in several cities, influencing future strategies for sustainable investments in urban transport [16]. Numerous studies have explored the advantages of autonomous vehicles in reshaping smart city mobility, particularly in terms of enhancing safety, road efficiency, driver productivity, and energy conservation [17,18,19]. Some studies have empirically examined how autonomous vehicles influence individuals’ travel mode choices and assessed their potential impacts on sustainability [20]. Other studies position autonomous vehicles as a key technological solution for smart city mobility, advancing both sustainable urban transportation and overall smart city development [21]. These studies collectively confirm that integrating autonomous vehicle technology into the transportation system has significant implications for smart city mobility and urban traffic sustainability. However, current research primarily focuses on technological advancements, but there remains a dearth of discussion on how to effectively incorporate autonomous vehicles into urban transportation policies and governance frameworks. Moreover, much of the literature on urban traffic development has concentrated on the positive impacts of technological progress, often overlooking the potential negative consequences. For example, predictive studies suggest that the convenience of autonomous vehicles may encourage increased vehicle usage and longer travel distances, leading to higher energy consumption and pollution emissions, along with adverse social and environmental consequences [22]. On the one hand, future urban transportation solutions will increasingly rely on public transport systems, comprising subways, buses, light rail, and trams, that not only cater to diverse travel demands and optimize road resource utilization but will also play a pivotal role in fostering the development of low-carbon urban mobility [6]. On the other hand, propelled by technological advancements, the rise in autonomous vehicles (AVs) is instigating a paradigm shift in urban transportation systems. As a critical element of urban mobility, public transport systems are inevitably undergoing structural transformations driven by AV technology [23]. Autonomous vehicles and public transport are not mutually exclusive; rather, AVs should complement public transport networks by addressing gaps. For instance, AVs can provide a solution to the “last mil”. challenge, assisting passengers from their homes to bus or subway stations, thereby enhancing the sustainability and overall appeal of urban transport [7].

Additionally, the ability of autonomous vehicles to navigate complex urban traffic and extreme scenarios remains limited, with any failure in vehicle-to-vehicle or vehicle-to-infrastructure communication systems potentially resulting in severe consequences [24]. Furthermore, the current legal framework for autonomous vehicles is incomplete, with significant gaps in areas such as accident liability, usage regulations, ownership rights, and integration into urban transport systems [25]. Given these environmental and regulatory challenges, the development and implementation of autonomous vehicles must be rigorously regulated. Municipal managers and urban transport planners face the critical challenge of ensuring that the benefits of autonomous vehicles are fully realized while minimizing their potential negative effects [26]. Docherty, Marsden [27] highlight the widening gap between rapid technological advancements and the capacity of governance systems to address the emerging challenges, with autonomous vehicles serving as a prominent example. The swift progress of autonomous vehicle (AV) technology has introduced significant governance challenges, such as the need for updated regulations, optimized infrastructure, and enhanced public acceptance. To tackle these issues, policymakers must implement targeted policies that not only facilitate the integration of AVs into urban transportation systems but also promote urban sustainability. For instance, Golbabaei, Yigitcanlar [28] emphasized the importance of policy integration in multi-modal transport systems, service accessibility, and infrastructure optimization to achieve sustainable mobility. Similarly, Lyons [29] argued that intelligent city transportation should be affordable, efficient, attractive, and sustainable. Although AVs offer the potential to resolve current traffic challenges, policymaking must also address public concerns about safety, as well as demographic factors such as age and gender [30]. This underscores the necessity for policymakers to consider both public safety and demographic influences.

In this study, government policies play a crucial role in the promotion of autonomous vehicles (AVs). Through policy support such as land transport planning and vehicle management measures, urban infrastructure can be optimized, including the creation of dedicated lanes and intelligent traffic signals, thereby enhancing the operational efficiency and safety of AVs [31].From the perspective of land transport planning, policymakers are investigating how autonomous vehicles will influence transportation and land use patterns, with the goal of improving existing transport systems while minimizing the challenges associated with the transition to autonomous mobility [14]. Governments are also encouraging the adoption of autonomous vehicles through policy incentives. Studies show that autonomous vehicles have the potential to provide significant environmental benefits to urban transport systems. Many studies emphasize the importance of strategic plans formulated by urban decision-makers for the successful implementation of autonomous vehicles, ensuring that their full potential is realized to foster more sustainable urban mobility [17,32,33,34]. Therefore, it is imperative for governments to balance public needs and interests while reinforcing regulations and providing guidance to foster the healthy development of autonomous vehicle technology, ensuring that citizens enjoy safer, more accessible, and smarter travel experiences. To address public concerns about privacy and mitigate technological skepticism surrounding autonomous vehicles, governments and research institutions are actively working to bolster public trust in this emerging technology. This is being achieved through the implementation of robust privacy protection policies, enhanced data security management, and the promotion of transparent technological regulations. These governance strategies aim to increase public willingness to adopt and support autonomous vehicles [35].

The sustainable development of urban transport systems requires not only effective policy governance to support autonomous vehicle infrastructure but also the consideration of public acceptance [36]. The widespread adoption of autonomous vehicles will profoundly affect users’ travel habits and lifestyles [37]. Their operational models and interactions with urban infrastructure will reshape future urban transport patterns [38]. The increased accessibility and convenience of autonomous vehicles (AVs) can significantly reduce citizens’ dependence on private gasoline-powered cars, thereby lowering carbon emissions and enhancing air quality [28,39,40]. Furthermore, by integrating multi-modal transportation systems and smart infrastructure (e.g., AV-specific lanes and real-time carpool matching algorithms), the travel experience can be optimized, leading to greater resource efficiency, reduced traffic congestion, and improved overall sustainability of urban transport [41]. For instance, integrating AVs with public transportation systems not only mitigates the environmental impact by reducing emissions but also fosters social equity by providing affordable and accessible transportation options for all population groups [28,40]. Autonomous vehicles, as an emerging mode of transport, have the potential to resolve this issue. Through flexible routing and on-demand services, AVs can offer passengers a seamless travel experience, reducing reliance on private cars and enhancing the overall efficiency of public transportation [8]. Consequently, urban decision-makers can leverage policy governance to enhance accessibility and convenience, offering more diverse and sustainable travel options [42]. In addition, citizens’ experiences with barrier-free and environmentally friendly travel can enhance social equity, improve urban environments, and optimize the “last mil”. travel experience, thereby increasing the sustainability of urban transportation.

Building on this, the present study investigates the factors influencing the overall sustainability of urban transportation within the realm of AVs, with a focus on government transportation policies, specifically land transport planning, vehicle management measures, service point coverage, and vehicle distribution, as well as the individual dimensions of citizens’ travel perceptions. This study takes a deeper, more detailed approach in exploring these factors from both macro and micro perspectives.

This study addresses the following core question: how do government transportation policies at the macro level and citizens’ travel perceptions at the micro level jointly shape the factors influencing urban transportation sustainability (as illustrated in the theoretical framework, where relationships between variables are clearly presented in a diagram)? By constructing relevant latent variables for both government policies and citizen perceptions, this study employs the PLS-SEM method to explore the policy management dimension of AV implementation, the travel perception dimension of urban residents, and the factors influencing urban transportation sustainability. Ultimately, seven key factors were identified: land transportation planning, vehicle management measures, service point coverage, vehicle distribution, accessibility, safety, intelligence, and urban transportation sustainability. This study confirms that factors such as citizen perceptions, policy measures, and technological characteristics significantly influence urban transportation sustainability, with further exploration needed regarding how policy implementation impacts citizens’ AV travel experiences.

The significance of this study is multi-fold: The introduction of the user-centric perception dimension into the study of urban transportation sustainability offers solutions for enhancing accessibility, intelligence, and safety. By analyzing citizens’ perceptions of autonomous travel services and improvements to current traffic flow, this study advances planning for further transportation sustainability. Moreover, this research is the first to integrate the government policy planning dimension into urban transportation sustainability, offering new perspectives and insights for both academic discourse and practical applications. Lastly, this study provides valuable insights and recommendations for AV technology developers and transportation policy planners, encouraging the development of AV infrastructure that aligns more closely with citizens’ travel needs. This approach fosters a balanced development across environmental, resource, social, and economic dimensions, contributing to the creation of a more comprehensive and sustainable transportation system.

2. Research Hypotheses

2.1. Public Perception of Travel and Urban Transportation Sustainability

The public’s acceptance of autonomous vehicles plays a crucial role in shaping policy governance and cannot be overlooked. This acceptance hinges on the public’s awareness, attitudes, and values regarding new technologies. The dimension of public travel perception pertains to the attitudes and sentiments passengers have toward the quality and performance of existing urban transportation services. Residents’ attitudes are closely linked to the implementation and success of sustainable urban transportation strategies [43]. Sustainable urban transportation aims to provide convenience, affordability, accessibility, and ease of travel while minimizing adverse environmental and societal impacts [42]. Additionally, we have broadened our discussion of sustainability to encompass environmental, social, and economic dimensions. For instance, the integration of autonomous vehicles (AVs) with public transportation systems not only mitigates the environmental impact by reducing emissions but also fosters social equity by offering affordable and accessible transportation options to all demographic groups [28,40]. Furthermore, promoting shared ownership and vehicle usage models, such as car-sharing and ride-hailing, can optimize vehicle utilization, reduce the total number of vehicles on the road, and alleviate the demand for parking spaces, thereby advancing resource efficiency and urban sustainability [29].

This connection indicates that increased accessibility and convenience of autonomous vehicles for citizens can reduce their reliance on private gasoline-powered cars, leading to lower carbon emissions and improved air quality [28,39,40]. Additionally, by integrating multi-modal transportation systems and intelligent infrastructure, such as AV-specific lanes and real-time carpool matching algorithms, the travel experience can be optimized, significantly boosting resource efficiency, alleviating traffic congestion, and enhancing the overall sustainability of urban transportation [41]. Simultaneously, the public’s perception of autonomous vehicles is becoming increasingly important, as residents must embrace new public transport alternatives and reduce their dependence on private vehicles [44]. To achieve these goals, public policies must cultivate the acceptance of sustainable transportation modes to ensure that transportation systems effectively meet the needs of citizens [36].

2.1.1. Accessibility

Accessibility serves as a bridge between transportation planning and residents’ convenience. Generally, it refers to an individual’s (or group’s) ability to reach various destinations seamlessly through a combination of transportation modes [45]. Autonomous vehicles offer considerable advantages to users of mobile devices by providing diverse mobility options, facilitating seamless access [46]. Although autonomous vehicles (AVs) present significant advantages in enhancing traffic efficiency and safety, their technological vulnerabilities must not be underestimated. For instance, AVs depend on real-time data processing and continuous network connectivity, meaning that delays or network disruptions could lead to functional failures [47]. These vehicles represent a leap forward in intelligent mobility, as demonstrated by the public’s ability to access real-time travel information, which in turn leads to time savings, improved journey experiences, cost reductions, and lower carbon emissions [48]. Autonomous vehicles also enhance accessibility for individuals with limited transportation alternatives [49]. Thus, when autonomous vehicles meet the accessibility needs of users, they positively impact urban transportation sustainability. This leads to Hypothesis 1 (H1):

H1. 

The accessibility of autonomous vehicles positively influences urban transportation sustainability.

2.1.2. Safety

Autonomous vehicles, or self-driving cars, are capable of executing driving tasks safely without human intervention [50]. The route design of these vehicles can be managed online, allowing for real-time adjustments based on traffic and road conditions to select optimal paths that minimize travel time and energy consumption [51,52]. In traditional driving, human factors often dominate safety concerns, with individuals relying on proximity to other vehicles or traffic to assess safety [49]. Autonomous vehicles equipped with advanced sensors and algorithms detect obstacles and can stop or reroute when necessary, significantly reducing traffic accident risks.

Autonomous vehicles (AVs) replace human drivers with automated systems that leverage superior environmental recognition, decision-making abilities, and driving skills to control the vehicle, enhancing road traffic safety [14]. We have also incorporated relevant case studies that highlight the potential vulnerabilities of AVs. Despite the intelligent advantages of AVs, one notable weakness lies in their reliance on onboard networks, which are vulnerable to interference. While AVs benefit from advanced digital technologies that reduce human error, they also face increasingly complex cybersecurity threats [53]. Hackers could potentially exploit these network vulnerabilities, posing significant risks to personal security and privacy, thereby underscoring the inherent cybersecurity concerns surrounding AV technology. Onboard networks are exposed to a range of threats and attacks that could cause the vehicle’s onboard computer to issue erroneous commands, directly compromising traffic safety. Therefore, in the event of any malfunction or compromise of components, it is crucial to implement robust safety and security measures to prevent or mitigate such risks [54,55].

By optimizing driving strategies and energy management, autonomous vehicles use energy more efficiently, lower emissions, and reduce environmental impacts. Therefore, when autonomous vehicles meet users’ safety needs, they positively influence urban transportation sustainability. This leads to Hypothesis 2 (H2):

H2. 

The safety of autonomous vehicles positively influences urban transportation sustainability.

2.1.3. Smart Mobility

Autonomous vehicles can perform driving tasks with high safety and detect environmental obstacles, stopping or rerouting when necessary [56]. Smart transportation systems, through distributed real-time environmental sensing, allow vehicles to cooperate with traffic management systems, promoting safe and eco-friendly travel [57]. Many cities are now striving for “smartnes”. to improve energy efficiency, traffic management, and public services [58]. By integrating new technologies into urban management, smart cities offer innovative solutions to challenges related to sustainability, equity, and economic growth [59,60,61]. In other words, the smart city agenda is designed to address a wide range of urban challenges, boosting economic growth, competitiveness, and innovation, while achieving sustainability goals like reducing emissions, enhancing energy efficiency, and improving the quality of life [62]. “Intelligent transportation is one of the primary features of smart citie”. [50], playing a key role in urban transformation [63]. It not only fosters urban expansion and development but also interacts with spatial policies to shape the dynamics of urbanization [64]. Thus, when autonomous vehicles meet the demand for smart mobility, they positively influence urban transportation sustainability. This leads to Hypothesis 3 (H3):

H3. 

The smart mobility of autonomous vehicles positively impacts urban transportation sustainability.

2.2. Land Transportation Planning

Land use and transportation planning are key strategies for regulating traffic and addressing urban challenges while promoting sustainability [65]. Alessandrini, Campagna [66] explored the integration of autonomous vehicles with land use, emphasizing the impact of these vehicles on travel patterns and land use, which will, in turn, shape future urban environments. With the widespread adoption of autonomous vehicles, parking demand will decrease; economic activity density will increase, and productivity will rise [67]. The implementation of autonomous vehicles (AVs) will have profound effects on urban public transport, land rents, and commuting distances, ultimately influencing residents’ lifestyles. AVs will reshape urban parking facility layouts and land use patterns, prompting adaptive shifts in the way people live. Different households may adjust their residential location strategies based on the accessibility of AV services. Some may choose to live near city centers [68], as AVs can alleviate the issue of limited parking spaces, while others may prefer more remote suburban areas, taking advantage of the enhanced commuting convenience offered by shared autonomous vehicles [69].

In line with previous studies, this research suggests that autonomous vehicles’ integration into land transportation planning can enhance residents’ travel accessibility.

Zakharenko [67] studied the effects of autonomous vehicles on urban form, examining commuting patterns, land use, worker welfare, city size, and transportation coverage. In land transportation planning, dedicated emergency lanes and safe zones could be designated, allowing autonomous vehicles to park optimally during the day and ensuring safe driving and parking in city centers [67]. This could reduce traffic accidents, safeguard pedestrians and passengers, and improve overall road safety. Land transportation planning can further support this technology by creating dedicated lanes and optimizing traffic signs and markings, enhancing driving safety for autonomous vehicles. In line with prior research, this study suggests that land transportation planning for autonomous vehicles (AVs) positively influences the safety of residents’ travel. Designating dedicated AV lanes within land planning will reduce conflicts between AVs and other modes of transport, thereby improving their operational efficiency and safety [31]. Updating traffic signs and road markings, such as using high-contrast colors for signage, will make it easier for AV sensors and cameras to detect and interpret them [70]. Furthermore, allocating designated spaces within urban land planning for AV parking, charging, and maintenance, such as establishing AV mid-zone areas near transportation hubs for vehicle scheduling and charging, will further enhance safety and operational efficiency [71]. Adjusting road geometries, such as lane widths, turning radii, and intersection layouts, to accommodate AV driving characteristics will improve both their safety and efficiency [72]. Installing dedicated traffic signals and sensors at intersections and complex road sections to support accurate AV navigation and decision-making is also crucial. Consistent with previous studies, this research affirms that land transportation planning for autonomous vehicles can significantly enhance travel safety for residents.

Land transportation planning can provide crucial infrastructure to support the adoption of autonomous vehicles. As a key component of smart parking systems, autonomous vehicles introduce innovative solutions like remote valet parking, which improves the convenience of parking and optimizes parking and charging experiences [73]. In addition to reducing parking demand per vehicle, autonomous vehicles (AVs) can also reduce the total number of vehicles through shared mobility models, such as ride-hailing services [74,75]. When multiple passengers share a single AV, the total number of vehicles required in the city will decrease significantly, thereby further reducing the demand for parking spaces. During peak hours, most AVs will be used for commuting, but during off-peak periods, empty AVs will need to be parked somewhere. To avoid occupying valuable parking spaces in city centers, AVs can be directed to park in peripheral areas while awaiting new passenger requests. This dynamic scheduling strategy requires the support of intelligent transportation systems to optimize AV parking and dispatching efficiency. As autonomous vehicles become more common, individuals may no longer need to own private parking spaces, as these vehicles can autonomously find parking or return to parking lots when not in use, freeing up more land for other purposes [76]. These discussions lead to Hypotheses 4 (H4), 5 (H5), and 6 (H6):

H4. 

Land transportation planning for autonomous vehicles positively influences accessibility.

H5. 

Land transportation planning for autonomous vehicles positively influences safety.

H6. 

Land transportation planning for autonomous vehicles positively influences smart mobility.

2.3. Vehicle Management Measures

Research on the controllability of interconnected autonomous vehicles in mixed-traffic environments has shown that these vehicles have immense potential to facilitate smooth traffic flow. Consistent with previous studies, this research similarly suggests that implementing vehicle management measures for autonomous vehicles can enhance the accessibility and convenience of transportation systems [77]. Autonomous vehicle management systems can dynamically adjust resources based on real-time road conditions, vehicle density, and area-specific demands, optimizing vehicle efficiency, reducing traffic congestion, improving road safety, and enhancing user experience [66]. The Internet of Vehicles (IoV) enables autonomous vehicles to communicate in real time with other vehicles and infrastructure, improving traffic management efficiency, reducing accidents, and creating a safer environment for autonomous vehicle operations [78,79]. These discussions lead to Hypotheses 7 (H7), 8 (H8), and 9 (H9):

H7. 

Vehicle management measures for autonomous vehicles positively influence accessibility.

H8. 

Vehicle management measures for autonomous vehicles positively influence safety.

H9. 

Vehicle management measures for autonomous vehicles positively influence smart mobility.

2.4. Scope of Service Point Support

Service points such as charging stations, maintenance centers, and technical support hubs are critical for the operation of autonomous vehicles [80]. Expanding the coverage of service points and implementing operations and maintenance systems enhance the efficiency of autonomous vehicle dispatching and contribute to the optimization and maintenance of urban operations, supporting environmental protection efforts [50]. This study asserts that the land transportation planning for autonomous vehicles can enhance the safety of residents’ mobility. A dense network of service points ensures that when autonomous vehicles encounter failures or need maintenance, they can swiftly access support, boosting user confidence in their reliability. Thus, this study posits that the scope of service point support positively impacts smart mobility. These discussions lead to Hypotheses 10 (H10), 11 (H11), and 12 (H12):

H10. 

The scope of service point support for autonomous vehicles positively impacts accessibility.

H11. 

The scope of service point support for autonomous vehicles positively impacts safety.

H12. 

The scope of service point support for autonomous vehicles positively impacts smart mobility.

2.5. Vehicle Deployment Allocation

Autonomous vehicles, by catering to users’ self-directed travel demands, can reduce the number of vehicles needed by households. However, socio-economic factors and convenience may still reduce the willingness to purchase private vehicles [81]. The impact of autonomous vehicles on urban transportation not only enhances traffic convenience and accessibility but also potentially increases travel frequency. The autonomous vehicle system is expected to meet growing transportation demand by improving matching efficiency while reducing vehicle ownership [82,83,84]. Additionally, a “private + share”. ownership model allows individuals to combine traditional vehicle ownership with shared mobility, enabling vehicles to operate autonomously during idle times and serve others [75]. This research suggests that the vehicle deployment allocation of autonomous vehicles can enhance residents’ mobility accessibility.

Moreover, with increasing demand for shared mobility services, the operation of autonomous vehicles has captured significant attention in academia [84]. Autonomous vehicles offer various benefits, including systems that match travel needs with vehicle availability, optimizing travel plans and improving efficiency. Carpooling systems help minimize empty seats, reduce vehicle requirements, and achieve optimal resource allocation [74,75]. In this context, by integrating passenger trips, autonomous vehicles (AVs) can significantly reduce the total number of vehicles in urban areas [41]. This reduction not only alleviates traffic congestion and decreases energy consumption but also reduces the demand for parking spaces. For instance, shared mobility models, such as ride-hailing services, can minimize vehicle vacancy rates, ensuring that each AV is maximally utilized, thereby reducing the need for parking spaces in cities and achieving optimal resource allocation [74,75]. These discussions lead to Hypotheses 13 (H13), 14 (H14), and 15 (H15):

H13. 

Vehicle deployment allocation of autonomous vehicles positively impacts accessibility.

H14. 

Vehicle deployment allocation of autonomous vehicles positively impacts safety.

H15. 

Vehicle deployment allocation of autonomous vehicles positively impacts smart mobility.

Based on the 15 hypotheses outlined above, this study establishes a model to shape the sustainability of urban transportation with autonomous vehicles, as illustrated in Figure 1.

3. Research Design and Methodology

3.1. Questionnaire Design

Taking into account the unique characteristics of autonomous vehicle travel services, the questionnaire for this study was designed and refined based on prior research findings. The relevant literature supporting the development of the questionnaire items is presented in

Table 1. Various factors and their definitions in the context of this study.

Factor	Code	Definition
Land transport planning	LTP	Refers to the strategies developed by governments and transportation authorities for land use and transportation infrastructure to optimize road resource allocation, improve traffic efficiency, and support the safe and sustainable operation of autonomous vehicles (AVs). Effective land transport planning can alleviate urban congestion, reduce environmental impacts, and enhance the integration of public transport and smart transportation systems.
Vehicle management measures	VM	Refers to various management strategies implemented by policymakers and traffic authorities to optimize the operational efficiency, road usage, and environmental impact of autonomous vehicles (AVs). These measures aim to improve urban transportation sustainability, reduce congestion, increase resource utilization, and lower carbon emissions.
Scope of Service Point Support	SPS	Refers to the coverage, reasonable distribution, and accessibility of service points (such as charging stations, parking lots, maintenance centers, and transfer hubs) for autonomous vehicles (AVs) provided by urban transportation planning and infrastructure. The effective layout and functionality of these service points are crucial for AV operation efficiency, user convenience, and overall transportation sustainability.
Vehicle deployment allocation	VDA	Refers to the optimized deployment and scheduling strategies of autonomous vehicles (AVs) within the urban transportation system to improve traffic efficiency, reduce congestion, optimize resource utilization, and support sustainable travel modes. Rational deployment involves adjusting vehicle density, demand matching, and collaboration with existing transportation modes across different regions and times.
accessibility	AC	Refers to the ability of the urban transportation system to provide convenient, safe, and efficient travel services to residents, particularly in the context of autonomous vehicles (AVs). Accessibility measures how easily different population groups, urban areas, and transportation modes can be accessed, as well as how AVs can optimize commuting time, reduce travel barriers, and improve the travel experience.
safety	SF	Refers to the ability of autonomous vehicles (AVs) to ensure the safety of passengers, pedestrians, other road users, and themselves during operation. This includes the reliability of autonomous driving technology, traffic accident prevention capabilities, emergency response mechanisms, and network and data security measures to reduce traffic risks and improve the overall safety of urban transportation.
Smart mobility	SM	Refers to the realization of efficient, safe, and environmentally friendly urban transportation systems based on autonomous driving technology, digital traffic management, shared mobility, and multimodal travel integration. Smart mobility uses data-driven optimization strategies to improve traffic flow, reduce congestion, and enhance citizens’ travel experiences.
Urban mobility sustainability	UBS	Refers to the achievement of an efficient, low-carbon, and equitable urban transportation system through autonomous driving technology, intelligent traffic management, and sustainable policies. Sustainability is reflected in reducing carbon emissions, improving travel efficiency, optimizing resource allocation, and ensuring that all social groups have equal access to convenient transportation services, thereby promoting long-term urban sustainable development.
Factor	Operational Definition and Item Content		Source
Land transport planning	Optimizing the traffic network based on the current land use and future traffic demand, with the premise of meeting transportation needs. LTP1: “I think convenient traffic planning and regular maintenance of autonomous vehicles are helpful”. LTP2: “I believe that dividing road traffic by functional areas helps improve the service quality of autonomous vehicles”. LTP3: “I think that, considering the economic level, the fare for autonomous vehicles is reasonable”. LTP4: “I think that with the improvement of traffic planning laws and regulations, the arrangement of autonomous vehicles has become more orderly”.		[85]
Vehicle management measures	Road management agencies systematically manage the regulations and enforcement processes related to the registration, supervision, and other aspects of autonomous vehicles. VM1: “I believe that dynamic information of autonomous vehicles is effectively regulated”. VM2: “I believe that the safety facilities of autonomous vehicles have been properly managed”. VM3: “A well-established autonomous vehicle information management system can allocate and select appropriate vehicle resources for us”. VM4: “I believe that autonomous vehicle resources are placed in the right locations”.		[86]
Scope of Service Point Support	Autonomous vehicle users can receive timely and comprehensive services from a series of service points established by service providers. SPS1: “The autonomous vehicle operating company has an adequate number of service points to provide us with more attentive maintenance services”. SPS2: “The autonomous vehicle service points offer relevant technical training services for users”. SPS3: “The staff at the autonomous vehicle service points are highly professional and have a positive service attitude, being responsible and patient in answering my questions and difficulties, and providing the corresponding help and support”. SPS4: “In the event of an accident, I believe the support services of autonomous vehicles are timely”.		[87]
Vehicle deployment allocation	The extent to which resources and technological infrastructure are used to support the electronic diagnostic technology of autonomous vehicles. VDA1: “The autonomous system will dynamically adjust vehicle deployment based on real-time road conditions, vehicle numbers, and areas”. VDA2: “The autonomous system will deploy vehicles flexibly and appropriately based on road conditions and the public’s acceptance level”. VDA3: “The autonomous system will dynamically adjust vehicle operating times and areas based on the vehicle’s health status and real-time load conditions”.		[88]
Accessibility	In public transportation planning, measuring accessibility plays an important role in assessing the distribution across the entire region. AC1: “Autonomous vehicles can take me to the places I want to go”. AC2: “Autonomous vehicles can provide 24-h uninterrupted travel services”. AC3: “Autonomous vehicles can accurately understand my requirements and intentions, and take me to my destination accurately and on time”. AC4: “Autonomous vehicles arrive to pick me up quickly and conveniently”. AC5: “I can easily call an autonomous vehicle using a mobile app”.		[89]
Safety	Perceived safety issues for users traveling with autonomous vehicles: SF1: “When using autonomous vehicles, I often consider traffic safety issues”. SF2: “When using autonomous vehicles, I feel very safe”. SF3: “I think driving at night is unsafe, so I would choose to travel in an autonomous vehicle”. SF4: “Compared to driving myself, I believe using an autonomous vehicle reduces the likelihood of a car accident”. SF5: “I am very concerned about traffic safety issues”.		[90]
Smart mobility	Utilizing smart technology to achieve efficient and sustainable mobility services, meeting current and future travel needs: SM1: “Autonomous vehicles have excellent perception capabilities, enabling them to clearly understand the surrounding environment and identify obstacles”. SM2: “Autonomous vehicles can make correct decisions when facing complex traffic scenarios (such as intersections or sudden pedestrians)”. SM3: “Autonomous vehicles respond quickly to sudden situations, ensuring the safety of passengers”.		[91]
Urban mobility sustainability	Achieving sustainable mobility within urban transportation systems: UBS1: “I believe the use of autonomous vehicles reduces private car usage, contributing to the sustainable development of the city”. UBS2: “Using autonomous vehicle services provides sustainable solutions for urban transportation”. UBS3: “Autonomous vehicles offer more choices to urban residents, enhancing the diversity and sustainability of urban mobility”.		[92]

. The survey employed in this study is divided into two sections: demographic characteristics of respondents and measurement items assessing how autonomous vehicles influence the sustainability of urban transportation, with responses rated on a 7-point Likert scale.

3.2. Data Collection

A convenience sampling strategy was employed for sample selection. Data were collected online via social media platforms and automotive forums, ensuring a diverse and representative sample. Upon collection, the data underwent meticulous cleaning to identify and exclude potentially invalid responses, particularly those exhibiting unusually rapid response times or identical answers. This process was critical in ensuring the accuracy and reliability of the final dataset. Data were gathered from both offline experiences and online surveys. A total of 535 participants were invited, each of whom was required to have prior experience with autonomous vehicles. After removing low-quality responses (e.g., identical answers across all items), the final sample comprised 502 valid responses. The basic characteristics of the survey sample are summarized in

Table 2. Demographic information of respondents.

Sample	Category	Number (n = 502)	Proportion (%)
Gender	Male	244	48.6
	Female	258	51.4
Age	18–25	161	32.1
	26–35	265	52.8
	36–45	65.26	13
	46–55	6	1.2
	56–60	4	0.8
	61 above	5	0.1
	High school/Technical school	100	20
Education	Undergraduate	289	57.5
	Master	113	22.5
Experienced time period	0–3 months	210	41.9
	1 months	160	31.8
	3 months	66	13.2
	6 months	66	13.1

.

4. Data Analysis and Results

This study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) as the primary research methodology due to its exceptional ability to handle small sample sizes and non-normal data distributions. PLS-SEM is a structural equation modeling technique based on partial least squares, and it is particularly well suited for exploratory research and the development and validation of complex models. Its primary advantage is its capacity to simultaneously address the relationships between multiple latent and observed variables while also offering flexibility in terms of data distribution requirements, making it especially ideal for studies with small sample sizes. Additionally, PLS-SEM excels in predictive power and model interpretability, providing more reliable results for the study.

The research focuses on users of autonomous vehicle (AV) services. Given the relatively small market size and dispersed nature of the audience for autonomous vehicle services, obtaining large sample sizes is challenging. Consequently, the use of PLS-SEM, which is designed for small sample scenarios, presents significant advantages. Not only does PLS-SEM effectively manage small sample data, but it also accommodates non-normally distributed data, making it the perfect analytical tool for this study. Furthermore, PLS-SEM enables the analysis of complex relationships between multiple latent variables simultaneously, offering valuable insights into the key factors influencing users’ acceptance of autonomous vehicle services.

4.1. Measurement Model Evaluation

The measurement model’s performance was assessed based on two criteria: reliability and validity. Reliability was evaluated using Cronbach’s α coefficient and composite reliability (CR), which measure internal consistency and the factor loadings of each item.

Table 3. Reliability and validity tests.

Construct	Item	Standardized Factor Loading	VIF	Cronbach’s Alpha	CR	AVE
Land transport planning	LTP1	0.819	1.953	0.872	0.865	0.712
	LTP2	0.845	2.104
	LTP3	0.858	2.240
	LTP4	0.852	1.979
Vehicle management measures	VM1	0.821	1.893	0.858	0.860	0.701
	VM2	0.849	1.994
	VM3	0.846	2.085
	VM4	0.832	1.978
Scope of Service Point Support	SPS1	0.894	3.109	0.902	0.902	0.773
	SPS2	0.869	2.447
	SPS3	0.887	3.001
	SPS4	0.868	2.426
Vehicle deployment allocation	VDA 1	0.883	2.160	0.841	0.841	0.759
	VDA 2	0.853	1.822
	VDA 3	0.878	2.083
Smart mobility	SM1	0.887	2.143	0.859	0.860	0.780
	SM2	0.881	2.196
	SM3	0.881	2.152
Safety	SF1	0.889	2.065	0.926	0.927	0.770
	SF2	0.870	2.104
	SF3	0.883	1.357
	SF4	0.872	2.846
	SF5	0.875	2.810
Accessibility	AC1	0.783	1.873	0.887	0.887	0.689
	AC2	0.830	2.281
	AC3	0.869	2.607
	AC4	0.853	2.342
	AC5	0.813	1.993
Urban mobility Sustainability	UBS1	0.911	2.776	0.890	0.924	0.820
	UBS2	0.911	2.741
	UBS3	0.895	2.381

Note that the associated constructs exhibit good convergent validity.

provides descriptive statistics for each indicator item, along with factor loadings, all of which exceed the recommended threshold of 0.70 [93]. Table 2 presents the Cronbach’s α coefficients and CR values for each construct, with all α values surpassing the established threshold of 0.70 [94] and all CR values exceeding the recommended level of 0.70 [94].

A construct is deemed to exhibit good discriminant validity if the square root of the average variance extracted (AVE) exceeds its correlation with any other construct, as outlined by Fornell and Larcker [93]. All factor loadings for the items exceeded the 0.70 benchmark, and the AVE values for all constructs surpassed the critical value of 0.5 [93]. As shown in Table 3, the fit indices for convergent validity yielded satisfactory results, confirming adequate validity. Additionally,

Table 4. Discriminant validity using the Fornell–Larcker criterion.

	SM	SF	AC	UBS	LTP	VM	SPS	VDA
SM	0.883
SF	0.728	0.878
AC	0.718	0.702	0.830
UBS	0.642	0.711	0.709	0.905
LTP	0.589	0.466	0.602	0.551	0.844
VM	0.737	0.615	0.684	0.620	0.696	0.837
SPS	0.712	0.548	0.688	0.613	0.757	0.787	0.879
VDA	0.764	0.652	0.702	0.638	0.627	0.745	0.769	0.871

Note that the associated constructs exhibit good discriminant validity. Note: The items on the diagonal on bold represent the square roots of the AVE.

presents the bold diagonal values, which denote the square root of the AVE for each construct. As all these values exceed their respective correlation coefficients, they confirm strong discriminant validity among the constructs examined in this study. The heterotrait–monotrait (HTMT) ratio was employed as an additional measure of discriminant validity based on the work of Henseler, Ringle [95]. The HTMT ratios for all constructs, as shown in

Table 5. Discriminant validity using the heterotrait–monotrait (HTMT) ratio matrix.

	SM	SF	AC	UBS	LTP	VM	SPS	VDA
SM
SF	0.815
AC	0.821	0.773
UBS	0.733	0.783	0.797
LTP	0.677	0.511	0.682	0.624
VM	0.856	0.684	0.782	0.708	0.807
SPS	0.807	0.596	0.769	0.684	0.858	0.896
VDA	0.899	0.738	0.812	0.737	0.732	0.875	0.883

, did not exceed the threshold of 0.9, as proposed by Gold, Malhotra [96]. In conclusion, the evaluation analysis provides strong evidence of the reliability and validity (both convergent and discriminant) of the model proposed in this study.

4.2. Structural Model Evaluation

Figure 2 presents the proposed structural model, and

Table 6. Path analysis results.

Hypothesis	Path	StdBeta	t-Value	p-Value	Results
H1	LTP→AC	0.074	1.388	0.165	Not Support
H2	LTP→SF	0.071	0.008	0.994	Not Support
H3	LTP→SM	0.056	0.014	0.998	Not Support
H4	VM→AC	0.069	3.272	0.001	Support
H5	VM→ SF	0.081	3.894	0.000	Support
H6	VM→SM	0.061	5.028	0.000	Support
H7	SPS→AC	0.084	2.074	0.038	Support
H8	SPS→SF	0.072	0.735	0.462	Not Support
H9	SPS→SM	0.059	2.401	0.016	Support
H10	VDA→AC	0.070	4.782	0.000	Support
H11	VDA→SF	0.071	6.490	0.000	Support
H12	VDA→SM	0.058	7.386	0.000	Support
H13	AC→UBS	0.081	4.616	0.000	Support
H14	SF→UBS	0.081	4.666	0.000	Support
H15	SM→UBS	0.066	1.497	0.134	Not Support

summarizes the results of the hypothesis testing. The findings support all hypotheses proposed in this study, with the exception of H1, H2, H3, H8, and H15.

At this stage, a comprehensive evaluation was conducted, focusing on two key metrics: the coefficient of determination (R²) and predictive relevance (Q²), both emphasized by Henseler, Ringle [97]. The R² value serves as an indicator of the explanatory power of each construct and the model as a whole, with values ranging from 0 to 1. R² values are typically classified into three categories—0.25, 0.50, and 0.75—representing weak, moderate, and strong explanatory power, respectively, as established by Hair, Risher [98]. Higher R² values indicate greater explanatory power. As shown in

Table 7. Results of R² and Q².

Constructs	R2	Q2
SM	0.653	0.531
SF	0.463	0.645
AC	0.571	0.528
UBS	0.596	0.601
LTP		0.509
VM		0.493
SPS		0.606
VDA		0.496

, R² values for the constructs in this study ranged from 46.3% to 65.3%. A value exceeding 26% is considered significant, as suggested by Henseler, Ringle [99]. Additionally, Q² values were calculated for each dependent construct, with all values being non-zero, thereby confirming that the proposed model meets the accepted standards for predictive relevance, in line with Cohen [100] perspective.

5. Discussion

The results indicate that the accessibility and safety perceptions of urban citizens have a significant impact on urban transportation sustainability (supporting H1 and H2). Autonomous vehicles not only reshape travel and transportation patterns but also offer new solutions to current traffic problems, heralding a fundamental transformation in mobility [101]. Autonomous vehicles represent a technological leap, with the potential to reduce traffic accidents, optimize overall traffic flow, alleviate congestion, and shorten travel times, ultimately improving the quality of life [102]. Effective city planning and citizen acceptance of autonomous vehicles are key strategies for ensuring the successful implementation of sustainable urban development. Only through the deep integration of autonomous vehicles with the urban transportation system can a revolutionary transformation in safety, efficiency, and sustainability be achieved [103]. Autonomous vehicles contribute to citizen accessibility through improvements in transportation policies, adjustments to traffic and spatial planning, and enhancements in the equity of transportation accessibility while mitigating the negative impacts of motorized transport [104,105]. This has a positive effect on the sustainability of urban transportation.

On the other hand, the impact of smart mobility enabled by autonomous vehicles on urban transportation sustainability was not significant (not supporting H3). This finding diverges from Lyons [29], who emphasized the interdependent and mutually reinforcing relationship between intelligence and sustainable urban transportation. To achieve effective smart urban mobility, it is essential to consider not only technological capabilities but also the broader interactions among various stakeholders and processes, as stated by Lyons [29]. Upon reviewing the research context, we concluded that in the digital age, the role of technological intelligence in shaping transportation sustainability has been overstated. Urban transportation sustainability encompasses multifaceted development across environmental, resource, social, and economic dimensions.

The lack of applicability in our study arises from the specific context of autonomous vehicle research, where the attainment of “intelligenc”. is not always reliant on cutting-edge technologies. Some high-tech solutions, such as advanced shared parking applications, despite their technological sophistication, may inadvertently encourage increased private car usage, counteracting the initial objective of promoting sustainability [29]. Scholars have also critically examined the relationship between smart cities and urban sustainability, questioning the effectiveness of technological innovations, such as autonomous vehicle technology, in addressing urban challenges and highlighting the potential limitations of such approaches [58,106,107]. The development of vehicle automation and the emergent sharing economy in transportation is reshaping the interaction between public and private transport systems [108]. Although autonomous driving technology may enhance accessibility and travel experience, its negative effects on walking, cycling, and public transportation, as well as the substantial rise in demand for leisure travel, could undermine transportation sustainability. Furthermore, widespread adoption of autonomous vehicles could potentially lead to increased energy consumption and carbon emissions, adversely affecting urban sustainability [109].

The impact of land transportation planning on the accessibility, safety, and smart mobility of autonomous vehicles was found to be insignificant (i.e., H4, H5, and H6 are not supported). This contrasts with earlier studies, such as Curtis [110], who argued that land use planning and transportation planning are closely intertwined, collectively shaping urban transportation networks and service structures, thereby influencing autonomous vehicle accessibility. These findings suggest that autonomous vehicles could potentially impact land development by optimizing routes and concentrating parking facilities, unlocking high-value urban spaces and altering urban land use [76].

In the context of our study, the lack of a significant impact from land transportation planning on autonomous vehicle accessibility may stem from the peripheral development of urban areas driven by autonomous vehicle technology, which contributes to the dispersion of travel distances [111]. Autonomous vehicles (AVs) can reduce parking demand in congested urban areas, freeing up space for residential and other developments, potentially fueling further urban sprawl [112]. The dilution of population density and the spread of mobility-based residential patterns weaken the accessibility of AVs spatially.

The influence of land transportation planning on the safety of autonomous vehicles was also found to be insignificant (i.e., H5 is not supported). Discrepancies with previous findings may be attributed to differences in sample characteristics. The participants in this study were drawn from regions where land transportation planning may not have fully addressed the specific requirements of autonomous vehicles (e.g., dedicated lanes and intelligent traffic systems). In contrast, some advanced cities have optimized their land transportation planning for AVs, significantly improving their safety [113]. The safety of autonomous vehicles depends not only on land transportation planning but also on the maturity of the technology and the extent of policy support. In this study, AV technology may not yet be sufficiently mature, and policy support may be lacking, contributing to the absence of a significant impact from land transportation planning.

The lack of significant influence from land transportation planning on the smart mobility of autonomous vehicles (i.e., H6 is not supported) contrasts with earlier research, which suggests that effective urban design and the optimization of multimodal networks through digital technologies improve connectivity and reduce mobility externalities by integrating social norms [114]. This study’s sample primarily comes from areas where autonomous vehicles have not yet been widely deployed, meaning user awareness and acceptance of AVs may be low. Consequently, the impact of land transportation planning on smart mobility has not yet materialized in these regions. In contrast, existing studies often focus on areas where AV technology is more mature, where the influence of land transportation planning is more pronounced.

The absence of significant effects of land transportation planning on the accessibility, safety, and smart mobility of autonomous vehicles underscores the need for land use policies to evolve in tandem with technological advancements. However, land use patterns often lag behind policy development, while autonomous driving technology is advancing rapidly and diversifying. Urban planners must closely monitor trends in AV technology, adapt transportation strategies accordingly, and explore the impacts of AVs on traffic flow and land use patterns. This proactive approach will help optimize existing transportation systems, enhance their efficiency, and mitigate potential negative consequences as autonomous vehicles become increasingly prevalent [14].

Vehicle management measures significantly impact the accessibility, safety, and smart mobility dimensions of urban citizens’ perceptions (supporting H7, H8, and H9). The effective regulation of dynamic vehicle information is crucial for interconnected and autonomous vehicles, as automation and connectivity can change mobility patterns. Vehicle-to-vehicle (V2V) communication allows for the coordination of multiple vehicles, enabling them to exchange status information and reduce travel time. Connectivity also supports numerous services and features, including emergency calls, tolls, and infotainment, all of which contribute to increased safety, performance, and vehicle cooperation [115,116].

The range of service point support significantly impacts the accessibility and smart mobility perceptions of urban citizens (supporting H10 and H12). Service points such as charging stations, maintenance centers, and technical support facilities provide essential infrastructure for autonomous vehicle operation. As noted earlier, autonomous vehicles incorporate smart mobility, with the public’s access to real-time information about vehicle operations playing a key role [48]. There is a natural synergy between autonomous and electric vehicles, as the “smar”. nature of autonomous technology helps address the limitations of current non-autonomous electric vehicles, including range, charging station density, and charging time management. Research has shown that smart fleet management for autonomous vehicles, which accurately predicts travel demand and optimizes the location of charging stations, can make smart mobility more accessible [117]. This synergy not only enhances the convenience of smart mobility but also profoundly impacts citizens’ perceptions of service point accessibility.

The impact of service point support on citizens’ perceptions of safety was not significant (H11 not supported). This is unexpected, as the service point network ensures that autonomous vehicles can receive support in the event of malfunctions or maintenance needs. Autonomous vehicles may face technical failures or human–machine interaction issues, including the standardization of AI systems for safety. Due to the complexity of autonomous vehicles, complete testing may not be possible [118]. As a result, the construction of service points may not be fully implemented, and during the trial operation phase, service coverage might not extend to all urban areas, leading to some residents being unable to access service point support. Even if service points are present, the quality of services may be limited, failing to meet all safety needs. To improve citizens’ perceptions of safety, governments and social institutions need to consider these factors and take effective measures to strengthen the construction and quality of service points, improving public awareness and trust in safety.

Vehicle deployment distribution significantly impacts the accessibility, safety, and smart mobility perceptions of urban citizens (supporting H13, H14, and H15). Autonomous vehicles are capable of performing transportation tasks such as movement and braking without human control [56]. Studies have shown that autonomous vehicles equipped with in-vehicle communication systems can exchange information with surrounding vehicles (e.g., traffic conditions and road conditions), enhancing safety [78]. Compared to traditional vehicles, autonomous vehicles do not require human intervention and can significantly reduce traffic accidents caused by human error [17].

6. Conclusions and Limitations

6.1. Theoretical Contributions

This study applies policy planning governance to the shaping of urban transportation sustainability influenced by autonomous vehicles. Few studies have explored the potential of using policy planning governance to shape urban transportation sustainability in the context of autonomous vehicles. By incorporating the dimensions of policy planning governance and citizen perceptions, this research provides a multidimensional analysis of the factors influencing urban transportation sustainability. This new perspective, combined with the traditional technological viewpoint, offers a more comprehensive framework for understanding the macro and micro aspects of implementing autonomous vehicles in shaping urban transportation sustainability. This study provides new insights into transportation sustainability and supports traffic flow from the perspectives of regulatory mechanisms and urban planning.

6.2. Practical Significance

This study offers valuable insights and recommendations for urban transportation departments on policy planning for autonomous vehicles. The integration of autonomous vehicles into urban transportation holds the potential for future sustainable development. The findings can help transportation planning departments make more effective decisions regarding land transportation planning and the regulation of autonomous vehicles. The following recommendations are provided.

Based on data analysis results, the transportation department should promote successful smart city cases through public network video campaigns to increase public awareness and participation. During the implementation phase, the government should strengthen policy guidance and support, increase R&D investment, improve infrastructure, and enhance public awareness and acceptance.

Urban transportation departments should focus on vehicle distribution, vehicle management measures, and efficient road utilization to optimize the transportation system, enhance traffic efficiency, and contribute to energy conservation and environmental protection. To implement vehicle distribution, a central vehicle dispatch system should be established to allocate autonomous vehicles based on different traffic periods and user needs.

The coverage of autonomous vehicle service points should be improved by optimizing vehicle routes and station layouts. Service points should be added in high-demand areas based on regional traffic needs and population density. Maintenance stations and repair facilities should be expanded to reduce the risk of traffic accidents due to vehicle malfunctions and ensure that autonomous vehicles are effectively integrated into the public transportation network, improving citizens’ safety perceptions.

6.3. Limitations and Future Research

(a)

Geographic Scope Limitation: The survey was conducted only in certain cities in China. Autonomous vehicle pilot cities in China are limited, primarily concentrated in first-tier cities with high levels of infrastructure, regulatory support, and industry development, which may not represent the transportation levels of all cities in China.

(b)

External Variables Limitation: The conditions of autonomous vehicle pilot cities vary, influencing citizens’ subjective evaluations of the technology. Public perceptions can differ depending on road conditions, traffic signs, lane markings, and other factors.

(c)

Sample Limitation: The survey predominantly attracted younger participants, especially those aged 18–35. Younger people are more accepting of emerging transportation technologies and show greater enthusiasm for autonomous vehicles. However, the sample may not fully represent the entire population, and future research should aim for a more balanced age distribution to capture a wider range of attitudes and mobility characteristics.

Although autonomous vehicle pilot cities are crucial, they face several challenges such as regional development disparities, limited regulatory scope, and high testing costs. Future research should further enhance inter-regional cooperation and address public acceptance of autonomous vehicles, focusing on education and awareness campaigns. Policymakers need to enhance public understanding of autonomous vehicle technology’s principles, safety, and convenience and ensure its responsiveness in emergency situations to build trust and improve public acceptance.