Search Results (295)

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

European cities have adopted different solutions to address the challenges of charging infrastructure for electric vehicles, depending on their specific characteristics and needs. The widespread adoption of effective solutions could accelerate the transition towards more sustainable urban mobility. However, as cities differ in socio-economic, infrastructural, and environmental aspects, a one-size-fits-all approach may not be suitable. Currently, there is a lack of studies in the literature that identify similarities among cities to support the development of shared strategies for sustainable electric mobility. This paper contributes to filling this gap by proposing a methodology based on Key Performance Indicators (KPIs) to classify and compare cities according to their electric vehicle infrastructure. Using quantitative data from 80 European cities across civil, social, and transport-related factors, as well as electric vehicle charging characteristics, we identified five reference city clusters. A sensitivity analysis, conducted across 30 scenarios, validated the robustness of the KPI framework. This approach provides a tool for policymakers to monitor the evolution of charging infrastructure, supporting data-driven decision-making for sustainable urban mobility. By promoting efficient and adaptable electric vehicle policies, this study aligns with the objectives of the 2030 Agenda for Sustainable Development, particularly in fostering sustainable cities and clean energy adoption. Full article

Data relative to the nitrogen dioxide concentrations of seventeen fixed monitoring stations in three provinces in Po Valley in Northern Italy are analysed. NO₂ is considered to be one of the most harmful air pollutants, mainly due to vehicle traffic in urban environments. According to the EU directives, different categories of monitoring sites are identified as being representative of different situations. The main objective of this study was to check the agreement of the classification of monitoring sites with the chemical measurements of the actual atmospheric NO₂ pollution. The analysis considered the whole year of 2022 and even included the consideration of single months; the aim was to identify seasonal effects. The data were treated using different chemometric techniques, and the results obtained from significance tests were consistent with those of cluster analyses. In particular, both approaches to the treatment of the chemical data indicate that the level of NO₂ pollution may be significantly different from one station to another, even within the same category, and may produce results that are more similar to those of different categories, both inside the same province and in different provinces. Beyond local interest, this work also acts as a case study, attempting to offer a path for a common approach to effective reproducible monitoring procedures toward shared environmental sustainability. Full article

With increasingly diverse customer demands and the rapid growth of the new energy logistics industry, establishing a sustainable and responsive logistics network is critical. In a multi-depot logistics network, adopting collaborative distribution and resource sharing can significantly improve operational efficiency. This study proposes collaboration and resource sharing for a multi-depot electric vehicle (EV) routing problem with time windows and dynamic customer demands. A bi-objective optimization model is formulated to minimize the total operating costs and the number of EVs. To solve the model, a novel hybrid algorithm combining a mini-batch k-means clustering algorithm with an improved multi-objective differential evolutionary algorithm (IMODE) is proposed. This algorithm integrates genetic operations and a non-dominated sorting operation to enhance the solution quality. The strategies for dynamically inserting customer demands and charging stations are embedded within the algorithm to identify Pareto-optimal solutions effectively. The algorithm’s efficacy and applicability are verified through comparisons with the multi-objective genetic algorithm, the multi-objective evolutionary algorithm, the multi-objective particle swarm optimization algorithm, multi-objective ant colony optimization, and a multi-objective tabu search. Additionally, a case study of a new energy logistics company in Chongqing City, China demonstrates that the proposed method significantly reduces the logistics operating costs and improves logistics network efficiency. Sensitivity analysis considering different dynamic customer demand response modes and distribution strategies provides insights for reducing the total operating costs and enhancing distribution efficiency. The findings offer essential insights for promoting an environmentally sustainable and resource-efficient city. Full article

Urban freight transportation is facing significant challenges due to increasing demand, driven by globalization, e-commerce growth, and the adoption of just-in-time logistics. These trends have led to rising vehicle flows in urban areas, negatively impacting sustainability, economic efficiency, and road safety. In response, cities are exploring innovative last-mile delivery strategies that emphasize sustainability, flexibility, and cost efficiency. Among these strategies, cargo bikes—particularly electric cargo bikes (e-cargo bikes)—are emerging as promising low-emission solutions for urban freight distribution. However, despite their potential, a generalized methodology for estimating their demand share in urban contexts remains underdeveloped. This study proposes a comprehensive modelling framework to evaluate the freight demand share that can be addressed by e-cargo bikes, integrating quantity, restocking service, modal, and delivery sub-models, calibrated using data from a case study in Italy. The results demonstrate that e-cargo bikes could fulfil up to 20% of urban freight demand, depending on the category of goods transported, and underscore the feasibility of integrating e-cargo bikes into urban logistics systems. However, critical challenges related to scalability and cost-effectiveness persist, highlighting the need for further research and reliable cost data to support broader implementation. Full article

Complete Streets (CS) are defined as streets that accommodate all types of users, regardless of ability, safely and equitably allowing for the presence of pedestrians, bicyclists, transit users, and vehicle drivers to share the roadway. Several agencies have developed CS policies as a vital strategy to create more inclusive and accessible environments for all road users. CS are an efficient way to support the implementation of a multimodal transportation system, providing alternatives to car-oriented roadway designs. The Kentucky Transportation Cabinet recently developed the Complete Streets, Roads, and Highways Manual, aiming to implement a safe and equitable transportation system throughout the state. However, there is a need to evaluate the benefits of CS regarding their safety performance. This study aims to present crash data and summary statistics for CS projects that have been completed in Kentucky. The methodology involves a comparative analysis of safety data collected before and after the implementation of these projects. The results reveal that CS can be an effective approach to improve safety for all road users, including vulnerable and motor vehicle users. The findings also contribute to the existing knowledge on CS, offering insights into their impact on safety performance. Given that transportation agencies continue to prioritize sustainable and inclusive transportation solutions, the outcomes of this study will provide practical guidance for urban planners, policymakers, and transportation engineers seeking evidence-based solutions for creating safer roads. Full article

Micromobility is a topic of growing interest, powered by the introduction of shared electric bicycles and, especially, e-scooters. This type of mobility has recently gained a lot of popularity in large cities, bringing many benefits, such as greener mobility, a connection for first- and last-mile trips, and on-demand transportation alternatives. However, it also comes at the cost of inadequate infrastructure and laws. This created problems, mainly a concerning rise in accidents and consequent injuries. This study first identifies the main causes of accidents and injuries by defining key aspects such as vehicle types, user demographics, and prevalent injuries. Head injuries emerge as the most critical concern, largely due to low helmet usage across various studies. To address this issue, the barriers to helmet adoption are explored in order to develop a new concept aligned with micromobility needs. The proposed helmet design also prioritises sustainability by replacing petroleum-based materials with expanded cork. This alternative reduces carbon emissions while maintaining the desired performance. Additionally, the design follows principles of disassembly, eliminating adhesives and permanent joints to enhance recyclability. The result is a malleable structured helmet that adapts to user requirements while supporting the United Nations’ 2030 sustainability development goals. Full article

Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems. Full article

This paper presents an improved model of the interaction among transport system elements, which identifies six levels of interaction and their analysis. A framework of eight factors (criteria) and their interactions that improve the quality of life is developed. The Analytic Hierarchy Process, Average Rank Transformation into Weight, and rank correlation methods were used to calculate the normalized weights and compatibility indicators of the 54 expert criteria. The results show that the most significant improvements in quality of life are the development of transport infrastructure and its quality, the reduction of road traffic accidents, and the reduction of environmental pollution caused by the transport sector, all of which directly contribute to a sustainable urbanized society. The improvement in quality of life is least affected by the increase in the number of vehicles and their technical parameters, as well as by the increase in the share of the country’s Gross Domestic Product generated in the transport sector. The variance of the weights of the criteria was shown to depend on the different magnitudes of the fundamental scale of the pairwise comparison chosen by the experts, the variance (stability) of the experts’ personal opinions, and the net variance of the weights assigned to the criteria by the expert team. The results of this study can be used to develop a national or regional transport sector development strategy that takes into account the factors that have the greatest impact on quality of life. 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

This study outlines the creation and lab verification of a low-voltage direct current (LVDC) back-to-back (B2B) converter intended as a versatile connection point for low-voltage users. The converter configuration features dual inverters that regulate the power distribution to AC loads and grid connections through a shared DC circuit. This arrangement enables the integration of various DC generation sources, such as photovoltaic systems, as well as DC consumers, like electric vehicle chargers, supported by DC/DC converters. Significant advancements include sensorless current estimation for grid-forming inverters, which removes the necessity for conventional current sensors by employing mathematical models and established system parameters. The experimental findings validate the system’s effectiveness in grid-connected and isolated microgrid modes, demonstrating its ability to sustain energy quality and system stability under different conditions. Our results highlight the considerable potential of integrating grid-forming functionalities in inverters to improve microgrid operations. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article

Data scarcity and privacy concerns in various fields, including transportation, have fueled a growing interest in synthetic data generation. Synthetic datasets offer a practical solution to address data limitations, such as the underrepresentation of minority classes, while maintaining privacy when needed. Notably, recent studies have highlighted the potential of combining real and synthetic data to enhance the accuracy of demand predictions for shared transport services, thereby improving service quality and advancing sustainable transportation. This study introduces a systematic methodology for evaluating the quality of synthetic transport-related time series datasets. The framework incorporates multiple performance indicators addressing six aspects of quality: fidelity, distribution matching, diversity, coverage, and novelty. By combining distributional measures like Hellinger distance with time-series-specific metrics such as dynamic time warping and cosine similarity, the methodology ensures a comprehensive assessment. A clustering-based evaluation is also included to analyze the representation of distinct sub-groups within the data. The methodology was applied to two datasets: passenger counts on an intercity bus route and vehicle speeds along an urban road. While the synthetic speed dataset adequately captured the diversity and patterns of the real data, the passenger count dataset failed to represent key cluster-specific variations. These findings demonstrate the proposed methodology’s ability to identify both satisfactory and unsatisfactory synthetic datasets. Moreover, its sequential design enables the detection of gaps in deeper layers of similarity, going beyond basic distributional alignment. This work underscores the value of tailored evaluation frameworks for synthetic time series, advancing their utility in transportation research and practice. Full article

Mobility hubs are meeting points for shared and active mobility within the existing public transport system. Despite an extensive public transportation network, private vehicle dependency remains high, indicating a critical need to enhance alternative transport modes. The aim of this study was to investigate public acceptance of smart and green mobility hubs, a crucial parameter for the successful transition from passenger cars to public transport. To achieve this objective, a stated preference survey was developed and distributed to a sample of 152 participants at two stations, namely Voula and Irakleio, that correspond to different public transport modes, tram and metro. Results indicated a generally positive attitude towards the development of green and smart mobility hubs. The survey revealed variations in commuter preferences between the two locations, with green spaces and smart charging benches being highly valued. Ultimately, this research illustrated that well-designed mobility hubs are instrumental in creating efficient, sustainable and livable urban environments, setting a new standard for future urban planning and development. The proposed interventions are expected to substantially contribute to the promotion of sustainable urban mobility in the respective areas and the broader city, enhancing quality of life and reducing the environmental footprint. Full article

Micro-mobility, which includes small, lightweight vehicles such as bicycles, electric scooters, and electric bikes, has emerged as a key component of modern urban transportation over the last decade. ESs have transformed how people navigate cities by offering an eco-friendly alternative to traditional transport, improving last-mile connectivity, and reducing traffic congestion. However, they also present challenges related to safety, infrastructure, and regulation. The rising crash rates involving electric scooters pose a significant public safety concern, driven by their novelty and limited research on associated risks. This study investigates factors influencing the adoption and use of electric scooter-sharing services, emphasizing risk perception, cultural norms, technological familiarity, and physical infrastructure. It also examines travel behaviors, common risks, and barriers to adoption. Using data from 254 Israeli participants, including 50 electric scooter users, the research highlights that 48% of users experienced near-miss incidents, and 38% used scooters on vehicular roads. The primary risk was identified as dangerous driver behavior on these roads, while the key barrier to adoption was a high perception of risk or low sense of safety. A structural equation model revealed that risk perception is influenced by gender-related attitudes and subjective norms, which indirectly diminish positive attitudes toward electric scooters and willingness to share and use them. The findings emphasize the importance of a safe physical infrastructure in fostering positive attitudes and promoting electric scooter use. This research provides valuable insights into mitigating risks and improving the adoption of electric scooters as a sustainable micro-mobility option. Full article