Differentiated impacts of sustainable transport policies on urban residents’ travel behavior: A social network perspective

Transforming urban transport systems toward sustainability requires a critical re-evaluation of the appraisal methods used in policy analysis. While cycling infrastructure offers clear environmental, health, and livability benefits, its economic evaluation through cost–benefit analysis (CBA) remains underdeveloped. This paper presents a CBA of the “E-Bike City” concept in Zurich, which involves a radical reallocation of road space from cars to bicycles and e-bikes, implemented within a large-scale MATSim agent-based transport model. We address methodological challenges in applying CBA to cycling projects, specifically demand forecasting, valuing consumer surpluses, and treating subjective safety. Consumer surpluses are calculated using both the conventional value of travel time savings (VTTS) rule-of-half approach and the logsum method derived from discrete choice models. We compare results with and without assuming behavioral change in response to the new transport supply. Our findings demonstrate that the transport demand model as well as the consumer surplus methodology significantly affect appraisal outcomes. Without accounting for preference change, both methods yield negative net present values (NPVs). In contrast, when behavioral adaptation is included, the logsum method produces strongly positive NPVs. The analysis also reveals substantial reductions in external costs, crashes, and greenhouse gas emissions. However, long-term decarbonization goals remain out of reach without further systemic changes, given projected population growth. We conclude that CBAs focusing on transformative, sustainable mobility policies require methodologies that reflect long-term behavioral adaptation and utility beyond travel time savings, making the logsum method a more suitable tool for sustainable transport appraisal.

Electric bicycle play a vital role in public transportation. It provides sustainable transportation with zero COx emissions. Further, pedaling allows the user to do physical exercise while travelling to the destinations. Electric bicycle allows the user to ride in different modes one is the complete manual physical mode and second one is the complete throttle mode without any physical effort and third one is the pedal assist mode. Pedal assist mode of riding allows the user to ride the bicycle in a comfortable manner as it senses the pedaling effort and gives the required motor speed in a proportional manner. Pedal assist mode may not suitable for many riders due to the reduced comfort. The major drawback in electrical bicycle is limited mileage sine it uses low capacity battery due to bicycle weight and regulations. Improving the mileage in an electric bicycle is a challenging task. There are many methods to improve the mileage such as regenerative braking, a dynamo attached to the cycle tube, hub dynamo etc. All these methods will give low output and will not be sufficient to charge the battery while the bicycle is in motion. Further, because of these methods, the friction will further increase on bicycle and finally there will be more losses as compared to without any of these methods. To address this, a novel friction less low rpm permanent magnet generator has been developed indigenously which will eventually provide improved bicycle mileage, more battery life, less friction and suitable for long rides. The novelty of this proposed method is that the rider can do the pedaling even in throttle mode to save the battery energy. The generator can charge the battery while the bicycle is in throttle mode and also in manual mode with a very low friction. Compared to the energy loss due to the friction, the energy recovery will be more with the proposed methodology. The hardware results also demonstrate that, the proposed methodology is a feasible solution to self-charge the battery while minimum friction on wheel.

This study aims to examine the impact of green transport on sustainable tourism development, focusing on the tourist destination of Rugova. Tourism is an important sector for economic development and environmental protection, and the use of sustainable transport has an important role in minimizing negative impacts on the environment a nd improving the quality of life for residents. This study will analyze the green transport practices that have been implemented in Rugova and will assess the impact of these practices on preserving natural resources, promoting eco-tourism, and improving the image of the destination as a sustainable place for tourists. The methodology used in this study will include analysis of primary and secondary data, including interviews with tourism industry stakeholders and representatives of transport institutions. Local policies and strategies that support the use of sustainable transport will also be examined. This study has shown that, altho ugh there is great support and significant potential for the development of green transport in Rugova, there are still major challenges that need to be addressed. The expected results will help identify opportunities and challenges related to the integration of green transport in the development of sustainable tourism in Rugova, providing recommendations for improving transport infrastructure and policies that support sustainable tourism development in the region. This study will contribute to the field of tourism and environmental management by providing an in-depth analysis of the impacts that green transport can have on sustainable tourism development, with a special focus on an important destination like Rugova.

The transition toward sustainability has become the major challenge of the twenty-first century. The transport sector is responsible for nearly one-quarter of carbon dioxide emissions, and the core of current decarbonization strategies is electrification. This study develops an analytical model to explain how fleet operators determine the optimal mix of internal combustion engine (ICE) and electric vehicles (EVs) under various policy and behavioral influences. Unlike classical firm models focusing solely on profit maximization, the proposed framework reflects the evolving corporate behavior in the sustainability era, where firms derive utility from both economic performance and their environmental and social standing. By extending a risk-adjusted Cobb–Douglas utility function to incorporate environmental awareness and uncertainty, the model explains how carbon taxation, decarbonization subsidies, perceived risk influence fleet composition. The results indicate that carbon taxes discourage ICE usage, subsidies and infrastructure investments accelerate electrification, environmental awareness amplifies these effects. Beyond economic incentives, the findings highlight the strategic importance of ESG commitments and risk management in corporate decision-making during the Sustainability era. This model contributes by developing a theoretical framework that integrates microeconomic optimization, behavioral economics, and policy design, providing guidance for both firms and regulators in advancing sustainable transportation systems.

ABSTRACT Transportation Mode Identification (TMI) based on GPS trajectory data is vital for data-driven urban mobility analysis and sustainable transportation planning. Despite recent advances in deep learning-based spatiotemporal modeling, effectively integrating heterogeneous contextual information into TMI remains a challenge. This paper proposes CALRCN, a Context-aware Attentive Long-term Recurrent Convolutional Network that jointly models spatial, temporal, and contextual characteristics of human mobility. The framework employs a structured input representation that separates GPS-based motion features from GIS-based contextual features. Spatial patterns are extracted using a convolutional module with channel-wise attention, while long-term temporal dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) with additive attention. Weather information is further incorporated to enhance contextual awareness. Experiments on the Geolife dataset demonstrate that CALRCN achieves an accuracy of 95.7%, outperforming existing deep learning baselines. Further evaluation supports the framework’s generalizability, while ablation studies validate the effectiveness of context fusion and attention mechanisms.

In the context of sustainable transportation development, reducing carbon emissions, energy waste, and noise pollution caused by traffic congestion has become an urgent task for achieving environmental and social sustainability. The key to this goal lies in mitigating and preventing traffic congestion, for which high accuracy traffic condition prediction models serve as essential tools. During the reconstruction and expansion of highways and urban arterial roads, frequent adjustments to traffic organization and changes in geometric alignment introduce dynamic and uncertain characteristics into the traffic system. Existing methods struggle to accurately predict traffic conditions in the modified sections. To address this challenge, this study proposes a Dynamic Bayesian Graph Convolutional Neural Network (DBGCN). The model incorporates road geometric parameters and dynamic traffic organization changes as key inputs. It employs a Dynamic Bayesian Network (DBN) to model multi-source dynamic information and infer a dynamic adjacency matrix that reflects latent spatiotemporal dependencies between nodes. This dynamic adjacency matrix is then input into a Graph Convolutional Network (GCN), which fuses spatiotemporal features with traffic flow data to achieve accurate traffic conditions prediction for upgraded sections. Validation on the Wuxuan highway demonstrates that the proposed method outperforms benchmark models in traffic conditions prediction accuracy and produces traffic conditions propagation diagrams with high interpretability.

ABSTRACT This study explores the institutional factors influencing electric bus adoption across 23 countries from 2010 to 2023. Utilizing a newly developed panel dataset, the research integrates and analyses data to identify key trends. Principal Component Analysis is applied to construct indices based on Scott’s institutional pillars, including Regulative, Technological Normative, Social Normative, Business Normative, and Cultural-Cognitive. Estimations are conducted using static econometric models, including fixed effects, random effects, and pooled Ordinary Least Squares, with robustness checks through specification tests and heterogeneity analysis, categorizing countries by median carbon emissions. Findings indicate that Technological, Social, and Business Normative factors significantly enhance electric bus production and sales, while Regulative factors negatively impact adoption, emphasizing the need for targeted electric bus policies. Cultural-Cognitive influences, though significant, present mixed effects, highlighting the need for further variable-specific investigation. This study offers valuable insights for policymakers and stakeholders, supporting the transition toward sustainable urban transportation.

ABSTRACT Increasing attention to the environmental impacts of the transport sector has given rise to a range of studies dedicated to identifying policy instruments that can promote a shift towards more sustainable land use and transport planning. However, little is understood about the calibration of policy instruments to support this shift. This article seeks to illustrate the impacts of instrument calibration in reducing transport emissions and volumes. It does so by studying the evolution of policies for sustainable mobility in the city of Espoo (Finland) from 2013 to 2025. The empirical material highlights the significance of the calibration of policy instruments in the process of transitioning to more sustainable forms of urban mobility.

ABSTRACT This paper examines pathways toward sustainable and healthy cities in Saudi Arabia in the context of rapid urbanization and climate change. It adopts an urban metabolic perspective that conceptualizes cities as dynamic systems shaped by flows of energy, materials, and resources, with infrastructure acting as a key structural component influencing these flows. Infrastructure is understood in its material and urban systems sense as the physical and organizational networks that shape resource flows and environmental exposures. The study investigates this by (i) analyzing the impacts of urbanization on climate change and public health, (ii) reviewing innovative initiatives in global cities that have mitigated environmental and health impacts, and (iii) assessing how these lessons can be adapted to the Saudi Arabian context. Drawing on a systematic literature review guided by the PRISMA framework and comparative case study analysis, the study finds that green infrastructure, sustainable transportation, and community health initiatives represent key pathways for advancing healthier cities. The analysis highlights the importance of integrated urban planning, infrastructure systems, and public engagement as enabling factors. The study contributes to the limited literature on healthy cities in Saudi Arabia and provides policy implications for more resilient, sustainable, and health-focused urban development.

Exploring the drivers of PM2.5 under different pollution durations using an advanced modeling framework in an industrial inland city of China.

The transition toward sustainable cities requires integrated energy planning frameworks that coordinate multiple technologies, policy instruments, and social considerations. This study proposes a robust optimization framework for rich-renewables eco-sustainable urban communities, where multi-energy hubs including electricity, thermal, cooling, and hydrogen systems are jointly managed under uncertainty. A scenario-independent static robust model is developed to ensure reliable operation under renewable intermittency, supported by sensitivity analyses. The framework introduces hydrogen chemistry consortium processes, integrating electrolyzers, methanation, fuel cells, and carbon capture, utilization, and storage to enhance renewable utilization and reduce emissions. Both stationary storage systems and electric public transportation fleets are incorporated to provide distributed and mobile energy flexibility. Demand-side management and policy mechanisms, including carbon taxation and cap-and-trade, are embedded to align operations with environmental targets. A digital-social welfare layer evaluates affordability and equitable access. Simulation results across multiple scenarios demonstrate that the proposed framework reduces operational costs by over 45%, improves grid independence by more than 35%, and achieves emission reductions exceeding 90%. Welfare indicators also show significant improvement, confirming the effectiveness of the integrated approach.

The growing need for energy around the world is putting pressure on established power sources like fossil fuels, making renewable energy solutions more concern. The high calorific value of hydrogen has made it an attractive technique for increasing combustion rates. To get the best possible results from a dual biodiesel blend of juliflora and kapok, this research seeks to improve the hydrogen enrichment ratio. When compared to diesel, the combined effects of 12H2 + JK B20 increased BTE by 11.5%, CP by 6.9%, and HRR by 5.9%. The use of H2 and JK B20 improved combustion, leading to a decrease of 8.5% in BSFC, 10.9% in CO and HC emissions, 11.5% in smoke, and 14.6% in both. The increase in NOx emissions is a result of the trade-off. The RSM results demonstrate that the test blend B20, which included 12% H2 at 100% load, exhibited an attractiveness index of 0.995. An effective and trustworthy prediction framework for optimizing CI engine properties, the ANN model was fitted using experimental data and validated. Sustainable transportation and environmental protection stand to benefit greatly from the synergistic effects of H2 enrichment and dual biodiesel.

Charging stations powered by renewable energy are foundational for fostering sustainable transportation by reducing the strain on fossil fuel reserves amid rising energy needs. Thereby, incorporating Renewable Energy Sources (RESs) like Photovoltaic (PV) systems and Wind Energy Conversion Systems (WECS) into Electric Vehicle (EV) charging stations ensure sustained powering facilities, reduced environmental fragilities, and enhanced grid utilities. However, the efficient functioning of these systems depends heavily on the DC-DC converter, which regulates power flow, stabilizes voltage, and adapts to dynamic energy sources. Hence, a novel Hybrid SEPIC-Landsman Bidirectional Converter (HSLBC), which combines the high boosting capability of the SEPIC configuration with the continuous current output characteristics of the Landsman converter, is presented in this work. To ensure effective operation, the converter is equipped with an advanced closed-loop control system utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Chicken Swarm Algorithm (CSA). The Doubly Fed Induction Generator (DFIG) based WECS is interfaced to the DC-link via a PWM rectifier. The intermittency of both these variable power sources is addressed through grid integration in the proposed system. Experimental and simulation results confirm the converter's functionality, showcasing reduced THD, high energy efficiency, and reliable handling of power flow.

Driving systems transition through learning: a case study for net zero school transport in rural Scotland

In the context of the Internet of Things (IoT) and Connected and Automated Vehicles (CAVs), eco-driving has become a key strategy for promoting sustainable and intelligent transportation by leveraging vehicle-to-everything (V2X) communication to optimize driving behavior, reduce energy consumption, and minimize environmental impact. However, achieving a dynamic balance among energy efficiency, traffic flow, safety, and driving comfort remains challenging in complex and congested urban environments such as roundabouts. This paper proposes an IoT-enabled multi-objective eco-driving framework, termed DDPG-KAN, which integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with Kolmogorov–Arnold Networks (KANs) to enhance nonlinear feature representation and policy learning capability under mixed-traffic conditions. A multi-objective reward function is designed to jointly optimize safety, energy efficiency, traffic smoothness, and driving comfort, while an Action Inspector ensures collision avoidance and a Model Predictive Controller (MPC) guarantees smooth and stable control execution. Leveraging IoT-based connectivity, the framework allows cooperative perception and adaptive decision-making among CAVs in real time. Simulation results show that the DDPG-KAN approach reduces energy consumption by 31.87%, improves traffic efficiency by 10.93%, decreases carbon emissions by 31.86%, lowers collision warnings by 38.34%, and enhances driving comfort by 53.46% compared to rule-based methods. These findings demonstrate the potential of DDPG-KAN as an effective IoT-driven solution for achieving low-carbon, safe, and comfortable mobility in intelligent connected vehicle systems, contributing to the realization of sustainable and green transportation networks.

Mapping emerging trends in hydrogen fuel cell technology for sustainable transportation: Insights from bibliometric and topic modeling analyses

The accelerating transition toward sustainable transportation has led to a rapid deployment of Plug-in Hybrid Electric Vehicles (PHEVs), introducing significant operational challenges for active distribution networks, particularly in terms of voltage regulation and network flexibility. High and spatially concentrated charging demand, combined with stochastic vehicle behavior, can substantially reduce voltage headroom and compromise grid integrity if not properly managed. To address these challenges, this paper proposes a novel Active Distribution Network Management (ADNM) framework based on the Voltage Network Flexibility Index (VNFI) for coordinated PHEV charging and discharging. The VNFI is employed as an actionable steering signal to identify voltage-critical buses and time periods, enabling flexibility-aware scheduling decisions under strict network-security constraints. Stochastic PHEV arrival, departure, and energy demand are modeled using probabilistic distributions, and the proposed framework is implemented and validated through a high-fidelity MATLAB–OpenDSS co-simulation on a modified IEEE 33-bus distribution system. Numerical results demonstrate that the VNFI-driven coordination improves the voltage flexibility margin by up to 44.2% and reduces total power losses by 29.4% compared with a conventional TOU-based charging strategy. Moreover, even under 100% PHEV penetration, the maximum voltage deviation remains within 0.055 p.u., confirming the robustness and scalability of the proposed approach. The results highlight the effectiveness of VNFI-based management in transforming PHEVs into flexibility resources for future smart grid operations. • The study examines the operational challenges posed by electric vehicles (EVs) on distribution systems, focusing on voltage control and power losses. • A probabilistic model for aggregating plug-in hybrid electric vehicles (PHEVs) is proposed, based on parameters derived from the National Household Travel Survey (NHTS). • The concept of voltage flexibility is introduced, with the evaluation of a newly proposed index to assess it. • A smart charging/discharging approach is developed for optimal PHEV scheduling within active distribution networks (ADNs), addressing power demand throughout different hours. • A nonlinear optimization method for mixed integers is used to formulate and solve the scheduling problem. • The proposed method is tested on an IEEE 33-bus distribution system, demonstrating its effectiveness and validating the proposed index against previously reported strategies.

Electric vehicles are transforming urban mobility by providing a cleaner alternative to conventional combustion engines. However, challenges related to route optimization and access to reliable charging infrastructure continue to limit their performance in many developing countries. This study addresses these issues through an enhanced electric vehicle path planning with time frame (EVPPTF) approach that focuses on minimizing travel time and energy use while meeting user deadlines and accounting for the spatial distribution of charging stations. Although Dijkstra’s algorithm and decision tree models are well-established methods, the main contribution of the paper lies in the development of a hybrid and time-aware framework that integrates these techniques in a coordinated manner to support real-time path planning and charging decisions for electric vehicles. The proposed method improves the classical Dijkstra search by embedding energy consumption constraints, traffic-aware weights, and charging station accessibility checks. In addition, the decision tree model is extended to predict optimal charging stops using features derived from real-time data, including traffic flow, station availability, and remaining battery levels. The study also presents a comparative performance evaluation using real traffic patterns from Indian cities, which demonstrates that the hybrid method outperforms traditional techniques, including decision trees, random forests, k-nearest neighbors, and support vector regression. Validation in cities such as Hyderabad and Bengaluru shows that the proposed system consistently reduces travel duration and energy use under realistic conditions. The findings contribute to smarter and more sustainable urban transportation by supporting autonomous driving applications, guiding the placement of charging stations, and offering a scalable approach that can be adopted in developing countries facing similar EV mobility challenges.

Energy production, storage and management for sustainable VTOL aircraft and advanced air mobility