Abstract: The use of light electric vehicles, such as e-bikes and e-scooters, is increasingly being adopted as a sustainable transport solution in urban areas. This is driven by the need for cleaner, faster, and space-efficient mobility solutions in urban areas. Although research on LEVs has grown over time, it remains fragmented across disciplines, creating a need for an integrated study on how LEVs contribute to sustainable transport in urban areas. This study conducted a bibliometric review to identify key themes in light electric vehicles and sustainable transport in urban areas and proposed future research agendas based on the conceptual patterns and research gaps. It utilised the Scopus database, focusing on the 552 publications from 2000 to 2025 retrieved on 30 September 2025. The Biblioshiny application (version 5.0) was used to perform bibliometric performance analysis and science mapping techniques. Results revealed that the publication trend steadily rose from 2015, with a significant upsurge after 2020, with an annual growth rate of 18.69%. Three dominant themes were identified: sustainability, integration with public transport, and technological innovations, alongside underexplored areas such as shared e-micromobility, freight delivery, as well as policy and governance. Future research should capture full lifecycle impacts, expand access to light electric vehicles beyond current user groups, and align rapid technological advances with inclusive governance frameworks.

Abstract: To deeply explore the mechanism of consumers' electric vehicle (EV) purchase behavior and address research gaps related to insufficient consideration of psychological latent variables and neglect of consumer heterogeneity in existing studies, this study constructs a latent class model (LCM) that integrates personal attributes, vehicle attributes, and six psychological latent variables: perceived usefulness, perceived ease of use, perceived risk, environmental awareness, purchase attitude, and purchase intention. Based on 1,044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that EV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: risk-avoidance type, moderate-low intention wait-and-see type, utility-oriented and low environmental concern type, high utility cognition and low risk proactive type, and all-dimensional high-intention core type. Socioeconomic and vehicle-related factors exert heterogeneous impacts on the psychological variables and purchase decisions of each profile. This study clarifies the intrinsic psychological mechanism of EV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote EV adoption and advance sustainable transportation development.

Abstract: Spatial mobility is vital for the well-being of older adults. Lack of adequate transportation can limit their access to healthcare, services, and social opportunities. For older adults who do not or no longer drive, receiving rides is their primary mode of transportation, but this reliance can burden their ride providers. Measuring and assessing the geospatial burden of providing rides is important for research and policies that aim to address the impact on ride providers and older adults’ unmet travel needs. In this manuscript, we propose an approach that collects data to assess ride providers’ geospatial activity spaces for their own routine activities and for providing rides. By comparing the two activities spaces, we suggest a possible method to operationalize geospatial ride-providing burden, using three potential burden indicators. Using data from a pilot study (N = 18 ride providers), we apply these burden indicators and correlate them to other indicators of burden (i.e., days/month giving rides and monetary costs, missed work, increased per-sonal stress). We conclude that the percentage of the ride-provision activity space that is outside the ride provider's regular activity space may be a useful indicator of geospatial burden of providing rides.

Abstract: To address the systemic issues of emergency medical resource allocation under multi-hazard coupling, this study constructed a hybrid rescue model combining fixed medical facilities and mobile rescue stations. Mixed-integer programming (MIP) was used to achieve three-dimensional optimization of “resource allocation-facility location-casualty transport,” with the objective function being to minimize the total rescue cost (including casualty transport time and waiting penalty costs). The uncertainty in disaster evolution is characterized using a scenario-based random demand representation method. Given the NP-hard nature of the model, PSO and VNS algorithms are designed to enhance solution efficiency through dynamic inertia weight adjustment and multi-modal neighborhood structure. Experimental validation confirms the effectiveness of the model and algorithms, providing practical insights for emergency management.

Abstract: Efficient last-mile delivery remains a major challenge for agriculture in rural regions such as the state of North Dakota in the United States. In these regions, farms are large, dispersed, and dependent on timely access to inputs. Truck–drone hybrid systems offer a potential solution by combining the long-haul capacity of trucks with the speed and flexibility of drones. Economic studies indicate that such proposed hybrid systems can enable faster, lower-cost, and more sustainable delivery of small, time-critical packages. This research further reviews the role of geographic information systems (GIS) in enabling these systems. A combined systematic and thematic review of 82 high-quality publications identifies five domains: GIS applications, truck–drone coordination, smart agriculture integration, rural implementation, and sustainability impacts. The findings show that GIS supports route optimization, drone launch-site selection, and real-time monitoring. Beyond the capacity of drones to extend reach and reduce delays, integrating IoT and AI platforms enhances decision-making and improves efficiency. However, constraints include federal regulations, payload limits, harsh weather (especially in rural areas), and cybersecurity risks. This review concludes that GIS-enabled truck–drone systems can transform agricultural logistics and rural resilience if providers can address regulatory, technical, and security challenges through coordinated innovation.

Abstract: Background: The maritime industry is experiencing a dual transformation driven by decarbonization imperatives and Industry 4.0 digitalization. Green Digital Shipping Corridors (GDSCs) is one of the initiatives that integrate zero-emission technologies to achieve shipping decarbonization. GDSCs utilize advanced digital systems and cross-sector collaboration to enable sustainable, efficient, and resilient green maritime transport. While technological architectures for GDSCs are well studied, the operational readiness of human actors—particularly seafarers and shore-based personnel—remains underexplored. Methods: This study adopts a layered, iterative methodology combining a systematic literature review, industry reports, and expert interviews. Strategic analyses were conducted using McKinsey’s 12 Elements of a Dynamic Operating Model and an upgraded Technology Readiness Level–Human Readiness Level (TRL–HRL) matrix. A five-layer Industry 4.0 architecture tailored to GDSCs was developed, alongside a comparative analysis of traditional and Industry 4.0-enabled maritime systems. A competency mapping framework was designed, aligned with STCW standards, and linked to a KPI-based evaluation and phased implementation roadmap. Results: The findings reveal significant gaps between technology maturity and human readiness, particularly in AI explainability, cognitive load compatibility, and multi-agent coordination. The proposed framework bridges traditional maritime skills with AI-enabled operations, emphasizing human–technology synergy, cybersecurity, sustainability competence, and adaptive training. Conclusion: Aligning technological deployment with structured human-factor readiness strategies is essential to realize the full potential of GDSCs. The integration of competency-based training, human-on-the-loop decision protocols, and continuous feedback mechanisms mitigates operational risks, enhances safety, and accelerates sustainable shipping transformation. The proposed model provides a replicable pathway for policymakers, training institutions, and shipping companies to implement AI-augmented GDSCs effectively.

Abstract: Smart mobility is widely promoted as a solution to the urban challenges of congestion, pollution, and inefficient transportation systems. Yet, its adoption remains inconsistent, particularly in developing cities where structural and systemic barriers are dominant. Prior research has examined enabling factors such as digital infrastructure and user perceptions, but has paid limited attention to the institutional, political, and socio-cultural constraints that influence adoption. Moreover, whenever considered, the barriers are studied in isolation, obscuring their systemic interactions. This study addresses this gap using Total Interpretive Structural Modelling (TISM) to hierarchically map the barriers. To complement the analysis, MICMAC classification is used to assess their driving and dependence power. Findings reveal that legacy paradigms in conventional transport planning, fragmented institutional mandates, and regulatory misalignment are the foundational barriers, reinforcing downstream challenges such as affordability constraints, limited service coverage, and persistent car-centric preferences. Anchored in Critical Urban Theory, the study depicts how smart mobility adoption is not a neutral technological process, but one deeply embedded in wider struggles over governance, equity, and urban development. The paper contributes to the literature by offering a theory-building framework that captures the interdependence of institutional, technological, and behavioral barriers. It also provides practical entry points for policymakers, planners, and mobility innovators seeking to target root cause interventions rather than symptoms, thereby enabling more equitable, scalable, and resilient smart mobility transitions.

Abstract: Bridges are critical nodes in freight networks, yet limited funding prevents agencies from maintaining all structures in good condition. This creates the need for a transparent and scalable method to identify which bridges pose the greatest risk to supply chain continuity. This study develops a bridge risk index using the threat–vulnerability–consequence (TVC) framework and validates its components with machine learning. Threat is defined as per-lane average daily traffic, vulnerability as effective bridge age (epoch), and consequence as detour distance, with traffic also contributing to disruption magnitude. The methodology applies log transformation and normalization to construct an interpretable multiplicative index, then classifies risk using Jenks natural breaks. Results show that epoch dominates vulnerability, detour distance amplifies consequence, and their interaction explains most of the risk variation. The highest-risk bridges are concentrated in rural areas and near major freight gateways where detour options are limited. The proposed TVC index provides a transparent, data-driven decision-support tool that agencies can apply nationwide to prioritize investments, safeguard freight corridors, and strengthen supply chain resilience.

Abstract: Cycling is recognized as a key strategy for the transition to sustainable urban mobility and for improving public health, yet it remains a marginal mode of transport in many cities. In an effort to promote cycling, some places offer financial incentives to encourage people to use bicycles as a mode of transportation. However, these policies raise social justice concerns regarding who benefits from such policies and who does not. This paper explores the operationalization of a policy designed to promote cycling through finan- cial incentives, specifically by compensating individuals for cycled kilometers with public transport credits. The study incorporates a social justice-oriented design and emphasizes the importance of diversifying cycling demographics, integrating cycling with public transport, and leveraging data to support fair urban mobility. Grounded in transport, mobility, and data justice literature, we introduce policy design guidelines for social-justice-informed ‘pay for cycling’ financial incentives, bridging social science and data science. These guidelines are illustrated through a pilot project for São Paulo’s “Bike SP” program, which includes app development, participant selection, and data collection. The pilot project reveals demographic and socioeconomic inequalities in cycling within São Paulo. It also provides a model for similar policies in other cities. The findings highlight the need for inclusive participant selection criteria and the potential of financial incentive policies to generate valuable cycling data, foster a cycling community, and integrate with broader mobility and public health policies. We argue that such policies should be part of a comprehensive strategy for creating an inclusive mobility environment.

Abstract: As of mid-2025, California maintains a target (and legal agreement with truck OEMs) to reach 100% zero-emission M/HD truck sales by 2036. The US federal government has relaxed its targets but maintains truck fuel economy standards, incentivizing EV uptake. To meet these ambitions, ZEVs require adequate charging infrastructure rollout at scale. This paper reviews studies that estimate the M/HD charging and investment needs in California and the US. This paper then develops a new matrix that entails charging needs by charging power for each truck type from Class 2b to Class 8, the charger-to-vehicle ratio for each truck type, and the charger investment costs. This paper projects that California may require about 151 to 156 thousand chargers on the road by 2030, and increase to 434 to 460 thousand chargers on the road by 2035. The associated charging infrastructure investment—including both new charger installation and charger replacement—can reach approximately $7.1 to $7.4 billion by 2030, and $16.4 to $17.8 billion by 2035. Thus, achieving the number of chargers and managing these investments needs will likely be challenging.

Abstract: Domestic e-cargo bikes could contribute to the much-needed transition away from individual car use to more sustainable urban mobility. However, while domestic e-cargo bikes are especially popular in some European countries, their adoption remains relatively low in the UK. Adoption can be hindered by negative perceptions and beliefs, but direct experience, such as through trials, can reduce these. This paper aims to understand barriers to e-cargo bike adoption in the UK through the study of perceptions. Our mixed-method approach combines open responses to a nationally representative survey with interview data collected as part of household long-term trial loans in three British cities. With this, we identify and compare barriers as imagined by individuals who have not used an e-cargo bike to the barriers as experienced by people who have used one. We identify three sets of barriers: spatial, material and societal. Our analysis suggests that trials might help address material and societal barriers, while spatial barriers persist among trial participants and require improving cycling infrastructure alongside policies to limit car use. This paper advances scholarship on trial programs as interventions to overcome imagined barriers to sustainable mobility modes, encourage their adoption, and foster sustainable urban mobility transitions more broadly. It also contributes to emerging scholarship on domestic cargo bikes, shifting from logistics focus.

Abstract: This study investigates cross-cultural differences in public perception of mobility electrification by applying Natural Language Processing (NLP) techniques to social media discourse in Germany and China. Using a large language model, we conducted sentiment analysis and zero-shot thematic classification on over 10,000 posts to explore how citizens in each country engage with the topic of electric mobility. Results reveal that while infrastructure readiness is a dominant concern in both contexts, German discourse places greater emphasis on environmental impact, often reflecting skepticism toward sustainability claims. In contrast, Chinese discussions highlight technological advancement and infrastructure expansion, with comparatively limited focus on environmental concerns. Sentiment in Germany tends to be more reserved and analytical, whereas Chinese discourse appears more expressive and emotionally varied. These findings underscore the importance of culturally tailored policy and communication strategies in supporting the public acceptance of electric mobility. By demonstrating how large-scale social media data can be used to analyze public sentiment across linguistic and cultural contexts, this study contributes methodologically to the emerging field of computational social science and offers practical insights for mobility policy in diverse national settings.

Abstract: Public transit subsidization often suffers from a double moral hazard problem, wherein both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts.Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge.This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators play an active role beyond mere funding. The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery.

Abstract: Improving energy efficiency of ships is one of the key strategies to decarbonise the maritime transport sector. Operational energy efficiency pertains to the ‘in-use’ efficiency of the ships which is directly related to the people that are involved in the operation of the ship, both onshore and onboard. This paper examines the measures available to improve energy efficiency from the perspective of onboard crew, the barriers associated with implementing the measures and how crew behaviour can be nudged to overcome the barriers using incentives. Speed reduction was seen as the single most important measure to optimise but also the most difficult to incrementally improve in practice due to several barriers including contractual, complex web of accountability and perverse incentives to increase speed. This leaves only a handful of other measures to optimise, such as trim-draft optimisation and auxiliary engine load optimisation, which have smaller efficiency gains, but when consistently applied through behavioural changes, encouraged through incentives, can lead to significant fuel savings over time. It is difficult to draw concrete conclusions on incentives, but preliminary findings suggest that there is room to consider alternatives to the current approaches, both monetary and non-monetary incentives were perceived to be important and going beyond the status quo of incentivising captains so that rewards are shared equitably amongst the crew.

Abstract: Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 minutes), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities.

Abstract: Wildlife strikes in aviation are among the most reported safety incidents. As such, strikes have become the fundamental unit of understanding of the risk posed by wildlife. However, with the management of wildlife strike risk shifting to a hazard management philosophy, this literature review considers the contention that current wildlife strike reporting systems are not suited to modern wildlife hazard management techniques. This review sourced academic literature from Web of Science (n=684) and, using bibliometric analysis software, identified relevant papers (n=257). Additional industry material completed the final catalogue (n=542). These papers were reviewed for their treatment and use of wildlife strikes with respect to modern risk and hazard management approaches. This analysis noted three potential challenges with current wildlife strike reporting systems, including the focus on collision events, the potential to introduce other adverse effects and the skewing of risk assessment results. The paper’s analysis was supplemented with a review of international standards and relevant national requirements and concludes that while academics and industry have adopted systemized safety and hazard management concepts and that international guidance material has been keeping pace, international standards, the foundation for many national reporting systems, remains decades behind.

Abstract: This study examines the annual carbon emission differences between privately owned electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) through the development of a usage-phase life cycle assessment (LCA) model, with a focus on the synergistic effects of grid carbon intensity, driving intensity (e.g., annual mileage), and vehicle efficiency. Through scenario analyses and empirical case studies in four Chinese megacities, three key findings emerge: (1) Grid carbon intensity dominates EV emission advantages—EVs retain significant carbon reduction benefits in low-CEG regions even with doubled annual mileage, while high-energy-consuming EVs risk emission reversals in coal-dependent grids under intensive usage. (2) Higher annual mileage among EV owners (1.5–2 times ICEV baselines) accelerates carbon accumulation, particularly eroding per-kilometer emission advantages in fossil-fuel-reliant regions. (3) Vehicle energy efficiency heterogeneity is critical: compact, low-energy EVs (e.g., A0-class sedans/SUVs) maintain advantages across all scenarios, whereas high-energy models (e.g., C-class sedans/SUVs) may exceed ICEV emissions in high-CEG regions. The study proposes a differentiated policy framework emphasizing synergistic optimization of grid decarbonization, vehicle-class-specific management, and user behavior guidance to maximize EVs’ carbon reduction potential. These insights provide scientific foundations for refining EV adoption strategies and achieving sustainable transportation transitions.

Abstract: This study recognizes the significance of young people as future consumers and industry decision-makers by examining their knowledge of and attitudes toward electric vehicles (EVs) in Bangladesh. People's awareness of and attitudes regarding electric cars (EVs) vary, according to preliminary findings from a poll of 102 participants. The study addresses the critical role of young people as future consumers and decision-makers by examining their knowledge and attitudes toward electric vehicles (EVs) in Bangladesh. Despite the global push for sustainability, awareness and acceptance of EVs in Bangladesh remain limited, partly due to a lack of targeted education campaigns. This underscores the need for targeted education campaigns. The study explores the relationship between acceptability and awareness of electric vehicles, identifying key factors influencing young people's perspectives. Beyond its scholarly significance, the research offers insights to industry stakeholders, educators, and legislators that will facilitate the development of tailored strategies to promote teenagers' use of electric vehicles. By encouraging optimistic and knowledgeable attitudes, this research aims to assist Bangladesh's transition to a greener, more sustainable future.

Abstract: The successful integration of autonomous vehicles (AVs) into society hinges on public acceptance, which is closely linked to trust. This study investigates the factors influencing initial trust and specific trust requirements for the acceptance of AVs among Spanish population. A national survey was conducted with 400 participants, selected to represent the demographic diversity of Spain. The survey assessed participants' prior experience with AVs, demographic characteristics, ethical concerns, and trust levels. The findings indicate that individuals with prior direct experience with AVs exhibit higher initial trust levels. Demographic variables such as age, gender, and education significantly influence trust requirements; notably, younger and higher-educated individuals demonstrate lower trust thresholds. Ethical concerns, including data privacy and algorithmic transparency, emerge as significant predictors of trust levels. When contextualized with international studies, these findings highlight unique cultural and regulatory influences on trust in AVs within Spain. These insights are crucial for policymakers and manufacturers aiming to enhance public trust promote the ethical development and public acceptance of AVs to facilitate the widespread adoption of AVs.

Abstract: The Department of Veterans Affairs (VA) transportation system plays an important role in ensuring access to transportation services for Veterans, particularly those in rural or underserved areas. However, concerns remain regarding the effectiveness of collaboration among the various VA transportation stakeholders. Persistent transportation challenges hinder Veterans' access to essential healthcare services and resources. Electric, Automated Ride-Sharing Services (ARSS) offer a promising opportunity to enhance transportation access, however their current limitations and the perspectives of VA transportation personnel must be considered. This study explored the current perspectives of the VA transportation system and assessed ARSS as an innovative and sustainable alternative through interviews with eight VA transportation stakeholders representing seven transportation sectors. Findings revealed the VA’s strengths, including personalized service, flexible accommodations, and collaborative care models, but also identified challenges, including limited funding, staff shortages, volunteer constraints, and restrictive eligibility criteria. The introduction of ARSS was identified as an opportunity to alleviate some of these constraints by reallocating human resources and improving access to essential services, although concerns remain regarding ARSS’s ability to accommodate Veterans with disabilities and address rural route complexities. Effective communication strategies and streamlined coordination were key recommendations for improving service delivery and expanding transportation access for Veterans.