Search Results (40)

This paper develops a dynamic repositioning mechanism for shared autonomous vehicles (SAVs) driven by travel demand. A prediction model for SAV travel demand is constructed by the proposed GRU-FC network. On this basis, an integer programming model for empty-vehicle dispatching which aims to maximize the SAV revenue while minimizing the costs of vehicle relocation and operation is formulated. The results indicate that, relative to relying solely on natural vehicle dispatching, the proposed dispatching scheme reduces empty vehicle dispatches by 21.00% and increases total system profit by 38.89%. The findings theoretically improve the dynamic optimization theory of SAV dispatching and provide theoretical support for algorithm design based on the “demand-pull” principle. The method proposed in this paper is beneficial to optimizing the dynamic vehicle dispatching theory of SAVs. It helps to boost system revenue, reduce empty driving costs, alleviate traffic pressure, and lower energy consumption and environmental pollution, thereby fostering sustainable urban mobility and supporting the Sustainable Development Goals of clean energy and sustainable cities. Full article

Urban mobility in Lisbon faces persistent constraints driven not only by congestion, parking scarcity, and emissions but also by deeper structural issues such as fragmented governance and limited cross-peripheral public transport connectivity. These shortcomings hinder integrated mobility planning and motivate the exploration of Shared Autonomous Vehicles (SAVs) as a complementary urban transport solution. Existing SAV frameworks rarely integrate governance coordination, data interoperability, and contextual adaptation for medium-sized European cities. This study addresses this gap by designing and validating a reference model for the deployment of SAVs in Lisbon using a design–science approach combining a literature review, enterprise architecture modelling, and stakeholder validation. The proposed model contributes the following: (i) a governance coordination framework for multi-actor urban mobility ecosystems; (ii) an integrated digital and application architecture supporting multimodal services and user trust mechanisms; and (iii) a technology layer enabling V2X communication and interoperable mobility data flows. The model is demonstrated through Lisbon-specific scenarios aligned with local sustainable mobility strategies. Scenario interpretation is informed by literature-based performance benchmarks—including travel-time reductions of 13–42%, energy-use reductions of 12%, and GHG reductions of 5.6%—which are used as reference indicators rather than simulation outputs. The resulting framework bridges strategic policy and implementable system architecture, supporting the transition towards integrated, sustainable, and autonomous mobility in medium-sized European cities. Full article

Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals. Full article

Rapid urbanization and limited land resources have intensified parking shortages in China’s core and old urban districts, highlighting the tension between parking supply and public space. This study investigates the staged impacts of shared autonomous vehicles (SAVs) on private car ownership and parking demand within the context of urban renewal. Using a case study of Qintai Community in Wuhan, we combined resident surveys (135 valid samples), on-site parking facility assessments, and demand forecasting models to evaluate changes in parking requirements across different timeframes. Results indicate that SAVs can substantially reduce private car ownership and reshape parking demand structures, with short-term transitional pressures followed by long-term demand contractions. Furthermore, SAV adoption offers opportunities to reallocate parking land for multifunctional urban uses, alleviating land-use conflicts in high-density neighborhoods. The findings contribute to a dynamic framework for staged parking optimization, integrating technological innovation with community-level urban renewal strategies. This study underscores the importance of linking residents’ behavioral shifts with infrastructure adaptation, providing evidence-based guidance for sustainable urban transport and space management. Full article

As Shared Autonomous Vehicles (SAVs) emerge as a transformative force in urban mobility, integrating them into dense, historic urban environments presents distinct spatial and planning challenges—such as narrow street patterns, irregular road networks, and the need to protect cultural heritage. This study investigates the spatial adaptability of SAVs in Suzhou old town, a representative example of East Asian heritage cities. To assess spatial readiness, a hybrid weighting approach combining the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) is used to evaluate 22 spatial indicators across livability, mobility, and spatial quality. These weighted indicators are mapped using a spatial density analysis based on Point of Interest (POI) data, revealing urban service distribution patterns and spatial mismatches. Results show that “Accessibility to Transportation Hubs” receives the highest composite weight, emphasizing the priority of linking SAVs with existing subway and bus networks. Environmental comfort factors—such as air quality, noise reduction, and access to green and recreational spaces—also rank highly, reflecting a growing emphasis on urban livability. Drawing on these findings, this study proposes four strategic directions for SAV integration that focus on network flexibility, public service redistribution, ecological enhancement, and cultural preservation. The proposed framework provides a transferable planning reference for historic urban areas transitioning toward intelligent, human-centered mobility systems. Full article

Autonomous public transport (APT) is revolutionizing urban mobility by integrating advanced technologies, including electric autonomous buses and shared autonomous vehicles (SAVs). This paper examines the historical evolution of APT, from early automation efforts in the 1920s to the deployment of autonomous shuttles in contemporary cities. It highlights technological milestones, legislative developments, and shifts in public perception that have influenced the adoption of APT. The research identifies key benefits of APT, including enhanced road safety, reduced greenhouse gas emissions, and improved cost-efficiency in public transport operations. Additionally, the environmental potential of SAVs to reduce traffic congestion and emissions is explored, particularly when integrated with renewable energy sources and sustainable urban planning. However, the study also addresses significant challenges, such as handling emergencies without human intervention, rising cybersecurity threats, and employment displacement in the transportation sector. Social equity concerns are also discussed, especially regarding access and the risk of increasing urban inequality. This paper contributes to the broader discourse on sustainable mobility, transportation innovation, and the future of smart cities by providing a comprehensive analysis of both opportunities and obstacles. Effective policy frameworks and inclusive planning are essential for the successful implementation of APT systems worldwide. Full article

The integration of shared autonomous vehicles into historic urban areas presents both opportunities and challenges. In heritage-rich environments like very old Asian (such as Suzhou old town, which serves as a use case example) or European (especially Mediterranean coastal cities) areas—characterized by narrow alleys, dense development, and sensitive cultural landscapes—shared autonomous vehicle adoption raises critical spatial and social questions. This study employs a qualitative, user-centered approach based on the ripple model to examine residents’ perceptions across four dimensions: residential patterns, parking land use, regional accessibility, and street-level infrastructure. Semi-structured interviews with 27 participants reveal five key findings: (1) public trust depends on transparent decision-making and safety guarantees; (2) shared autonomous vehicles may reshape generational residential clustering; (3) the short-term parking demand remains stable, but the long-term reuse of space is feasible; (4) shared autonomous vehicles could enhance accessibility in historic cores; (5) transport systems may evolve toward intelligent, human-centered designs. Based on these insights, the study proposes three strategies: (1) transparent risk assessment using explainable artificial intelligence and digital twins; (2) polycentric development to diversify land use; (3) hierarchical street retrofitting to balance mobility and preservation. While this study is limited by its qualitative scope and absence of simulation, it offers a framework for culturally sensitive, small-scale interventions supporting sustainable mobility transitions in historic urban contexts. Full article

Passengers’ trust in Shared Autonomous Vehicles (SAVs) can be affected by different factors, such as their attitudes toward new technologies and perceptions of the vehicles’ reputation. While the existing literature has begun to explore these issues, there is limited research investigating how industrial design in SAVs can enhance passengers’ trust levels. To address this gap, this study responds to the central question: How can passengers’ trust in the vehicle itself and in fellow passengers be enhanced through design intervention? This question conceptualises trust in the vehicle and trust in strangers as an integrated trust issue within the SAV context. To fill this gap, this study adopts a project-grounded methodology. The design work is guided by five trust principles: anthropomorphic design, a defensible space, system transparency, personalisation features, and a restorative environment. Drawing on insights from an auto-ethnography of current ride-sharing services, these principles are further explored and applied to identify design opportunities for both the physical and digital elements of SAVs. The final conceptual SAV design demonstrates how different design elements can be orchestrated to engender user trust. The outcome contributes to ongoing design practices and helps researchers and designers better understand trust design for SAVs. Full article

Shared autonomous vehicles (SAVs) are emerging as a potential tool for sustainable transportation, yet their impact on travel behavior and environmental outcomes remains uncertain. This review evaluates the sustainability implications of SAV adoption, including its potential to reduce emissions through optimized fleet operations, enhance social equity by improving mobility access, and increase economic efficiency through resource-sharing models. This systematic literature review examines 107 articles from English and Chinese databases, focusing on SAVs’ effects on total travel demand, mode choice, and in-vehicle time use. Findings indicate that SAVs could increase vehicle miles traveled due to unoccupied relocation and new demand from previously underserved demographics, though advanced booking and dispatch systems may mitigate this increase. The study identifies 59 factors influencing SAV adoption, categorized as user-centric, contextual, and psycho-attitudinal. Analysis of in-vehicle time use shows varied activities, from productivity to leisure, with contradictory findings in the value of travel time (VOT) compared to conventional vehicles: while some studies report up to 34% lower VOT for SAVs due to multitasking opportunities, others find up to 29% higher VOT. Privacy and personal space emerge as important factors, with users showing a high willingness to pay to avoid additional passengers. The review highlights underexplored variables and methodological limitations in current research, including psychological influences and mode substitution dynamics. These insights inform policymakers and urban planners on how to integrate SAVs into sustainable transportation systems by mitigating their environmental impact, promoting equitable access, and ensuring alignment with smart urban planning strategies. Full article

The usage rates of shared autonomous vehicles (SAVs) have become a pressing concern following their increased deployment. While prior research has focused on initial user acceptance, post-adoption behavior remains underexplored. As SAV deployment matures, public concerns have expanded beyond safety to encompass service requirements, challenging the relevance of earlier findings to current commercialization efforts. This study investigates the factors shaping SAV utilization through an empirical study in Norway, where autonomous buses have operated for several years. Through mixed methods, we first analyzed responses from 106 participants to 43 SAV users and 63 witnesses of SAV operations. The results revealed that concerns had shifted from technological anxiety to service-related factors. Through purposive interviews with individuals who showed acceptance of SAVs but did not adopt them as their primary mode of transportation, we explored the gap between high acceptance and low usage. Our findings provide insights into long-term SAV deployment and guidelines for improving usage rates, highlighting the importance of addressing service characteristics such as information transparency, vehicle appearance, speed, and convenience, rather than focusing solely on safety in commercial settings. Full article

While extensive research has examined how major life events affect travel habits, less attention has been paid to the impact of minor environmental changes on commuting behavior, particularly regarding shared autonomous vehicles (SAVs). This study investigated how daily disruptions and incremental environmental changes influence commuter behavior patterns and SAV adoption in Shanghai, applying the theory of interpersonal behavior framework. The study surveyed 517 Shanghai residents, examining travel satisfaction, commuting habits, psychological factors (such as habit strength and satisfaction), and attitudes towards SAVs. Structural equation modeling was employed to test hypotheses about psychological factors influencing SAV adoption, while logistic regression analyzed how these factors affected mode choice across different disruption contexts. Analysis revealed that psychological factors, particularly habit and satisfaction, were stronger predictors of SAV adoption than attitude-based factors. Route obstructions and workplace relocations significantly increased SAV consideration. Even minor, recurring disruptions, such as construction zones, showed strong effects on commuting behavior, supporting the habit discontinuity hypothesis and emphasizing the importance of minor disruptions in driving behavioral change. The study extends the theory of interpersonal behavior by integrating habit discontinuity theory to explain how minor disruptions drive SAV adoption. This research provides actionable insights for urban planners and policymakers, recommending that SAV trials and targeted interventions be implemented during infrastructure changes or other commuting disruptions to promote SAV adoption and foster more sustainable transportation systems. Full article

Integrating shared autonomous vehicles (SAVs) in urban transportation systems holds transformative potential but is accompanied by notable challenges. This study, conducted in Saudi Arabia (KSA), aims to address these challenges by identifying and prioritizing the key barriers and policies that are necessary if we are to successfully adopt SAVs. A comprehensive analysis was performed through a literature review and expert consultations, revealing 24 critical barriers and 10 policies for solving them. The research employed a three-phase methodology to evaluate and rank the policies proposed to overcome these barriers. Initially, the study assessed the specific barriers and policies related to SAVs. Subsequently, the Fuzzy Analytic Hierarchy Process (FAHP) was employed to evaluate the relative importance of these barriers. Finally, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) was applied to rank the policies; the process identified government-backed investment, urban planning integration, and funding for research and development in sensor and hardware technologies as the most effective policies. The study underscores the importance of targeted policies in addressing technical and infrastructural challenges. Emphasizing system reliability, cybersecurity, and effective integration of SAVs into urban planning, the findings advocate for robust government support and continued technological innovation. These insights offer a roadmap for policymakers and industry leaders in the KSA to foster a more sustainable and resilient urban transportation future. Full article

The integration of automated vehicle (AV) technology in public transportation systems offers promising opportunities to improve the flexibility and safety of the traffic environment. However, user acceptance remains a critical challenge in the field of human-machine interaction for the effective deployment of shared autonomous vehicles (SAVs). This study presents a design framework aimed at enhancing user acceptance through human-machine interface (HMI) design tailored to SAVs. The framework is developed in adherence to relevant interaction design principles, following a systematic approach encompassing three key steps: analysis, synthesis, and evaluation. It integrates user acceptance factors into the design process, providing a structured method for designers. The framework was iteratively refined through interviews with three international domain experts; a focus group discussion with 10 researchers and professionals specializing in automotive interaction designers; and a workshop with 30 students and designers. The results demonstrate the framework’s ability to guide the development of user-acceptable HMI solutions. The paper concludes by emphasizing the need for further exploration into how user acceptance factors evolve over time and how real-world testing can validate the framework’s effectiveness in promoting user acceptance and satisfaction. Full article

As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system. Full article

While the emergence of shared autonomous vehicles can be an effective solution to improve transport issues and achieve sustainable development, the benefits associated with shared autonomous vehicles can only be realized when the public intends to use them. Therefore, it is necessary to conduct an in-depth study on the public’s intention to use shared autonomous vehicles and identify the key influencing factors. This study mined social media data to obtain real public perceptions. A qualitative exploratory analysis was used to identify thematic variables regarding social media data on shared autonomous vehicles, from which a research model of the public’s intention to use SAVs was proposed. Then, a questionnaire survey was conducted, and the structural equation model and Bayesian network were used to analyze the questionnaire data quantitatively. The findings reveal how perceived risk, social information, trust, perceived usefulness, and personality traits affect the public’s intention to use shared autonomous vehicles, and how to enhance the public’s intention to use them. This study will enrich the research on traveler psychology in the context of intelligent travel and provide theoretical basis and decision support for future policies to promote shared autonomous vehicles. Full article