Search Results (111)

With the acceleration of urbanization and the continuous rise in private car ownership, urban traffic congestion has become a critical issue constraining sustainable development. As an important extension of carbon reduction policies, the personal carbon trading mechanism provides a new approach to regulate travel behavior through economic incentives. This study constructs a game model incorporating stakeholders from both government and private car owners, explores their decision-making behaviors under the personal carbon trading mechanism, and conducts simulation analysis of evolutionary paths using MATLAB 2019a. The findings reveal that choosing public transportation results from interactive strategic interactions between government and private car owners. Proactive implementation of personal carbon trading policies by the government can accelerate private car owners’ adoption of public transportation strategies. Reducing government implementation costs of personal carbon trading (PCT), increasing carbon trading costs for private cars (through higher carbon prices or lower allowances), and improving public transit comfort are key factors in achieving equilibrium between government and private car owners’ strategies. Carbon trading costs exhibit differentiated impacts on the convergence speed of both parties’ states. This research aims to provide decision-making references for governments in formulating and implementing personal carbon trading systems, as well as motivating private car owners to adopt green and environmentally friendly travel behaviors. Full article

With the rapid development of microgrids (MGs) and electric vehicles (EVs), leveraging the flexibility of EVs in MG optimization scheduling has attracted significant attention. However, existing research does not consider the impact of EV scheduling potential on MG uncertainty or the avoidance of conflicts in EV users’ mobility needs and their charging/discharging activities. Therefore, this paper proposes a two-stage microgrid energy scheduling model integrated with the conditional value-at-risk (CVaR) and dynamic cooperative game theory. In addition, the aforementioned issues are specifically addressed by considering both EV scheduling potential and travel risk. The day-ahead model minimizes the MG’s operational costs, where a CVaR-based uncertainty model for MG net load is established to quantify risks from both renewable energy generation and load. The EV dispatchable potential is calculated using Minkowski summation theory. In the real-time stage, the adjustment of participating EVs and optimal incentive compensation costs are determined through the proposed EV travel risk model and dynamic cooperative game, aiming to minimizing the MG’s real-time adjustment costs. The simulation results validate the effectiveness of the proposed method, which can help to reduce the operational costs of MGs by 4%, reduce real-time adjustment costs by about 85%, and decrease load variability by 3%. For the main grid, the proposed method can avoid the “peak-on-peak” phenomenon. For EV users, travel demands can be fully satisfied, charging costs can be reduced for 34% of users, and 2.4% of users gain profits. 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

Background/Rationale: Transnational caregiving may be influenced by religious beliefs and cultural traditions that frame elder care as both a moral and religious obligation. While migration alters caregiving dynamics, religious teachings and cultural expectations remain central in guiding transnational caregiving practices. This study examines how Christian Nigerians who have immigrated to Canada navigate caregiving responsibilities within a transnational context, integrating their religion, cultural values, and the practical realities of crossing borders. Methods: This study employed a predominantly qualitative narrative approach, drawing on in-depth interviews with Nigerian Christian immigrants (N = 10) providing transnational care. Data collection involved a pre-interview survey and semi-structured interviews, providing the opportunity for participants to share their lived experiences. Thematic analysis was used to identify recurring themes related to the role of religion and culture in caregiving, ensuring a comprehensive exploration of participants’ perspectives. Findings: Caregiving is shaped by religious duty and cultural obligation, reinforced by biblical teachings and cultural values. Participants view elder care as a moral responsibility, tied to spiritual rewards and familial duty. Despite migration demands, family-based care remains preferred over institutional care, with social stigma attached to neglecting elders. Conclusions: Religion and culture remain integral to transnational caregiving practices, sustaining caregiving responsibilities despite migration-related realities. While religious teachings provide moral guidance and emotional support, cultural expectations reinforce caregiving as a collective and intergenerational duty. Policies and resources are needed that support transnational caregivers, ensuring they can fulfill their caregiving roles while adapting to new sociocultural environments. Policymakers should prioritize the implementation of policies and programs to support transnational caregivers, including family reunification measures, caregiving-related travel provisions, culturally tailored eldercare services, diaspora–local collaborations, organized caregiver support groups, and financial mechanisms such as tax incentives for remittances dedicated to elder care. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

This study investigates consumer behavior in electric vehicle (EV) adoption, focusing on how factors like convenience, accessibility, technological perception, and cost influence the travel patterns and usage behavior of EV drivers in Thailand. This study aims to address the research gap in the comparative behavior between electric vehicles and public transport in a developing country. Using a quantitative approach, the study collected data via surveys distributed online and face-to-face interviews with a stratified sample of 398 respondents. The survey assessed the relationships between convenience and accessibility, technology perception, cost of ownership, and travel patterns using structural equation modeling (SEM). The findings reveal that convenience and accessibility significantly affect consumer perceptions of technology and the cost of ownership, which, in turn, influences travel patterns. Technology perception and performance serve as partial mediators, suggesting that improving the infrastructure enhances EV adoption. Additionally, the cost of ownership, including long-term savings, positively impacts usage behavior. This study provides key insights for policymakers and urban planners aiming to promote the adoption of EVs. Enhancing charging infrastructure, offering government incentives, and improving public awareness of long-term cost benefits are recommended strategies. These findings are particularly relevant in urban environments and offer guidance for developing infrastructure policies that align with consumer preferences. Full article

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. Full article

This study investigates the factors that influence passengers’ attitudes and behavioral intentions toward sustainable air travel in Thailand, emphasizing the critical role of environmental awareness. Using a structured questionnaire survey of 400 airline passengers from Thai Airways and Bangkok Airways, this research employs structural equation modeling (SEM) to analyze the relationships among key constructs based on the Theory of Planned Behavior (TPB). The results reveal that environmental awareness significantly impacts green attitude, perceived airline responsibility, and perceived behavioral control, which in turn influence behavioral intention. Notably, green attitude has a direct positive effect on support for sustainable travel actions, whereas perceptions of airline responsibility and behavioral control do not significantly affect behavioral intentions in this context. The findings highlight the importance of environmental education, transparent communication, and accessible offset programs to foster a green mindset among travelers. Policy implications include developing targeted communication strategies, incentive mechanisms, and industry collaborations to promote eco-friendly travel practices. This study concludes with recommendations for policymakers and airlines for enhancing efforts in cultivating environmental awareness, thus supporting Thailand’s commitment to sustainable aviation and global climate goals. Full article

Cancer clinical trials contribute significantly to healthcare-related greenhouse gas emissions, highlighting the need to address sustainability in this area as the climate crisis intensifies. This study provides the first national assessment of sustainability awareness, attitudes, and practices within the Irish cancer clinical trials community. A 21-item cross-sectional survey was distributed to 613 cancer research professionals affiliated with Cancer Trials Ireland, including clinicians, research nurses, trial coordinators, patient advocates and industry staff, yielding a 20.6% response rate. Survey items assessed awareness of sustainability tools, perceived carbon contributors, training received, confidence in implementing green practices, and perceived barriers and enablers to sustainability. Awareness of existing carbon footprint tools was low, with only 21% familiar with the Sustainable Clinical Trials Group guidelines and fewer than 6% aware of the National Institute for Health and Care Research calculator. Despite limited training and low confidence in implementing carbon-reductive measures, 86% of respondents expressed willingness to engage with sustainability initiatives. Trial-related travel, sample kit waste, and trial set-up were perceived as the highest contributors to emissions, though perceptions did not always align with published data. Key barriers included lack of education, institutional support, and regulatory clarity, while financial incentives and training were identified as enablers. Coordinated, system-wide interventions are needed to embed sustainability into cancer clinical trial design, governance, and funding processes. Full article

To address the substantial disparities in operational characteristics between social vehicles and dedicated bus lanes, as well as the sub-optimal coordination control effects, a comprehensive approach is proposed. This approach integrates social vehicle arterial coordination with bus priority control in dedicated bus lanes. Initially, an analysis of the differences in travel time distribution on both types of roads is conducted. The likelihood of buses passing through upstream and downstream intersections without stopping is also assessed. This analysis aids in determining the correlated traffic states and the corresponding signal adjustment strategies for arterial coordination. Subsequently, an incentive mechanism is established by quantitatively analyzing vehicle delay losses and bus priority benefits based on the signal adjustment strategy. Finally, a deep reinforcement learning framework is proposed to solve, in real-time, the optimal signal adjustment strategy. Simulation experiments indicate that, in comparison to the arterial coordination of social vehicles and dedicated bus arterial coordination control, this method significantly reduces the average per capita delay by 38.63% and 27.43%, respectively, under conventional traffic flow scenarios. This is in contrast to the separate arterial coordination for social vehicles and dedicated bus lanes. Furthermore, it leads to a reduction of 52.17% in the number of bus stops at intersections when compared solely with the arterial coordination of social vehicles. In saturated traffic flow scenarios, this method achieves a reduction in average per capita delay by 29.7% and 9.6%, respectively, while also decreasing the number of bus stops at intersections by 39.5% and 8.7%, respectively. Full article

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

The transition to sustainable transport poses significant challenges for urban mobility, requiring shifts in fuel consumption, emissions reductions, and economic adjustments. This study conducts a scenario-based analysis of Budapest’s transport energy consumption, emissions, and monetary implications for 2020, 2030, and 2050 using the Budapest Transport Model (EFM), which integrates COPERT and HBEFA within PTV VISUM. This research examines the evolution of diesel, gasoline, and electric vehicle (EV) energy use alongside forecasted fuel prices, using the ARIMA model to assess the economic impact of transport decarbonisation. The findings reveal a 32.8% decline in diesel consumption and a 64.7% drop in gasoline usage by 2050, despite increasing vehicle kilometres travelled (VKT). Electricity consumption surged 97-fold, highlighting fleet electrification trends, while CO₂ emissions decreased by 48%, demonstrating the effectiveness of policies, improved vehicle efficiency, and alternative energy adoption. However, fuel price forecasts indicate significant cost escalations, with diesel and gasoline prices doubling and CO₂ pricing increasing sevenfold by 2050, presenting financial challenges in the transition. This study highlights the need for EV incentives, electricity price regulation, public transport investments, and carbon pricing adjustments. Future research should explore energy grid resilience, mobility trends, and alternative fuel adoption to support Budapest’s sustainable transport goals. Full article

Commuting plays a critical role in shaping sustainable transport systems, yet understanding the diverse preferences of commuter groups remains a challenge for policymakers. As cities aim to promote sustainable transport, it is essential to better understand the factors influencing travel behaviors. This study investigates the commuting preferences and behaviors of urban employees in Italy, focusing on identifying distinct user profiles and their implications for policy development. Using a dataset of 2301 participants from Italian cities, the research analyzed transport mode choices, willingness to adopt sustainable transport options, and perceptions of public transport (PT) services, including factors such as travel time, proximity to PT stops, cost, and comfort, rated on a four-point Likert scale. K-modes clustering was employed to segment participants into three clusters based on their travel behaviors. The results revealed three distinct user profiles: (1) car-dependent users with negative perceptions of PT, driven by family obligations and dissatisfaction with PT services; (2) individuals who primarily use cars but are somewhat open to improvements in PT; (3) individuals willing to adopt alternative mobility options, including active and shared transport modes. Significant differences were found across clusters in terms of mode choices, willingness to use sustainable transport, and satisfaction with PT services. Notably, employees showed limited interest in alternative sustainable transport modes such as e-scooters and walking, with 73% and 66% of participants expressing little or no interest, respectively. Despite incentives such as company subsidies for purchasing bicycles or e-scooters, 58% of employees remained uninterested in adopting these alternatives. Additionally, employees’ perceptions of PT services revealed dissatisfaction with factors such as travel time, comfort, and punctuality, with over 70% rating these aspects as “Poor” or “Fair”. These findings suggest that improving the quality of PT services, particularly in terms of travel time, punctuality, comfort, and cost, should be a priority for enhancing user satisfaction. This research provides valuable insights for policymakers seeking to reduce car dependence and promote sustainable urban transport planning. Full article

Progressive advancements in the global economy and technology have propelled human civilization forward; however, they have also inflicted significant harm on the global ecological environment. In the present era, electric vehicle (EV) technology is playing a vital role due to its environmentally friendly technological advances. However, widespread adoption of EVs has been hindered by their limited travel range, inadequate charging infrastructure, and high costs. This can be closely observed when we assess the adoption of electric vehicles (EVs) among motorcycle taxi drivers, commonly called ‘pilots,’ in Goa, India. Motorcycle taxis are crucial in Goa’s transportation network, providing affordable, efficient, and door-to-door services, especially in regions with limited public transport options. However, the rising costs of petrol and vehicle maintenance have adversely affected the income of these pilots, prompting concerns about their willingness to adopt EVs. This study aims to analyze the factors prompting the behavioral intention to adopt EVs by motorcycle taxi pilots in Goa, India, focusing on six key determinants: charging infrastructure, effort expectancy, performance expectancy, price value, social influence, and satisfaction with incentive policies. A quantitative approach was employed, utilizing stratified proportionate random sampling techniques to collect data from 242 motorcycle taxi pilots registered with the Goa State Government Transport Department. It was analyzed using partial least squares-structural equation modeling (PLS-SEM) through Smart-PLS 4.0 software. The research highlights that performance expectancy and price value are the potential motivators for the adoption of electric vehicles. These findings suggest that pilots are more likely to embrace EVs when they perceive tangible benefits in performance and find the cost reasonable in relation to the value offered. The results offer actionable insights for policymakers, manufacturers, and other stakeholders. These insights can guide strategic decisions and policy frameworks aimed at fostering a sustainable and user-centric transportation ecosystem. Full article