Search Results (806)

The electrification of public transport with battery electric buses (BEBs) poses technical, regulatory, and environmental challenges. This paper analyzes the impact of BEB charging on a Brazilian urban medium-voltage (MV) feeder using a novel methodology to convert utility GIS data into OpenDSS simulation models. The study utilizes Geographic Database of the Distribution Company (BDGD) data from the Brazilian Electricity Regulatory Agency (ANEEL) and OpenDSS simulations. Motivated by Cuiabá’s proposal to electrify its public bus fleet, four realistic scenarios were simulated, incorporating distributed photovoltaic (PV) generation and vehicle-to-grid (V2G) operation. Results show that up to 118 BEBs can be charged simultaneously without voltage violations. However, thermal overload occurs beyond 56 units, requiring conductor upgrades or load redistribution. PV systems can supply up to 64% of the daily energy demand but introduce reverse power flows and overvoltages, indicating the need for dynamic control. V2G operation enables peak shaving but also leads to overvoltages when more than 33 buses inject power concurrently. The findings suggest that while the current infrastructure partially supports fleet electrification, future scalability depends on integrating smart grid features and reinforcing the system. Although focused on Cuiabá, the methodology offers a replicable approach for low-carbon urban mobility planning in similar developing regions. Full article

The penetration rate of battery electric buses (BEBs) continues to rise, and the design of BEB networks has become the foundation for establishing efficient and sustainable public transportation systems. Improving the equity of bus network and reducing the total cost of the bus system are taken as the targets, a multi-objective programming model for TNDP is proposed in this study. Among them, the Gini coefficient of bus travel times during peak hours and the direct travel proportion of the elderly during non-peak hours are used to describe the equity of the bus network. When calculating the comprehensive cost, factors such as the fleet size of battery electric buses, charging facilities requirements, and charging costs are taken into account. To enhance the reliability of the obtained results, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to generate the Pareto-optimal solution set. The Mandl’s benchmark network is used for comparative validation, and a case study based on the road network of Zhengzhou is undertaken. Calculation results indicate that the proposed model not only minimizes the total travel costs but also significantly reduces the Gini coefficient of the transportation mode distribution. Under the constraint of overall expenses, it effectively improves the equity and the direct travel proportion of the elderly served by the bus system. The results can provide quantitative support to formulate livelihood transportation policies for local government and optimize the allocation of public transportation resources. Full article

Cities are increasingly using electric buses as a viable alternative to diesel buses. This is a crucial undertaking to achieve sustainability in the transport sector. However, integrating them in transport systems in developing countries such as Sri Lanka, which is characterized by environmental and economic challenges, is complex. This work examines the factors that influence the shift from diesel to electric buses with particular attention to the stakeholders, their motivations, and how they seek to achieve their objectives regarding each other, both conflicting and cooperative angles. This study adopts a comprehensive stakeholder-centric methodology to analyze electric bus adoption in the public transit system in Sri Lanka. The research employs a mixed-methods approach that combines qualitative stakeholder analysis with quantitative barrier prioritization, following established project management principles. Based on the case study of Sri Lanka, the research investigates how the electric bus transition can be expedited by leveraging such alliances while considering local challenges like infrastructural deficits, policy gaps, and funding limitations. Lessons learned and best practices from international case studies are considered to provide strategic recommendations to policymakers and other stakeholders to promote the electric bus. By mapping out the interactions between various stakeholders and outlining where key leverage exists, the research provides a roadmap for introducing electric buses. This will be aligned with the sustainability targets and the vision to deliver sustainability goals for the long term. Full article

Traffic calming zones (TCZs) are increasingly being implemented in urban areas to enhance road safety, reduce vehicle speeds, and support sustainable mobility. However, their impact on public transport (PT) operations, particularly bus services, remains underexplored. This study examines the impact of classifying streets into TCZs on bus transport performance in Poland’s ten largest cities. Geospatial analysis and a custom R algorithm delineated areas suitable for TCZs based on road class and administrative category. GTFS data were analysed for almost 1000 bus lines to evaluate the overlap of their routes with TCZs. The findings reveal that in several cities, a significant portion of bus operations would run through TCZs, with the average route segment affected notably by city and zone classification methods. Differences in TCZ size and shape across cities were also statistically significant. This study concludes that although TCZs contribute to safer and more liveable urban environments, their influence on bus speeds, which can lead to changes in fuel or energy consumption, and route design must be carefully managed. Strategic planning is essential to find a balance between the benefits of traffic calming and the operational efficiency of PT. These insights offer valuable guidance for integrating TCZs into sustainable urban transport policy without compromising PT performance. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, affecting millions of people and challenging the public health framework globally. While the definitive cause of AD remains unclear, researchers are concentrating their efforts on several prominent theories. Currently, there are very few FDA-approved medications for AD, and these primarily alleviate symptoms rather than alter the disease’s progression. In response, research efforts focus on developing new medicines that address the complex nature of AD through multi-targeted approaches. Multitarget-directed ligands (MTDLs) are a promising treatment strategy for AD, despite the significant challenges they pose. This review examines recent advancements in designing prospective targeted MTDLs to combat AD, with a focus on categorizing the lead generation process and investigating the integration methods of key pharmacophores within the 2024–2025 timeframe. The review highlights numerous examples of novel MTDLs that address various AD hallmarks, demonstrating their broad therapeutic potential. These targets and activities include cholinesterase (AChE and/or BuChE) inhibition, monoamine oxidase (MAO-A and/or MAO-B) inhibition, antioxidant activity, amyloid-beta (Aβ) aggregation inhibition, tau protein aggregation inhibition, glycogen synthase kinase 3β (GSK-3β) inhibition, calcium channel blockade, anti-inflammatory activity, and other hallmarks. Full article

Automated, electric shuttles are expected to be key for the future of public transportation, providing a safe, efficient, and robust operation with a minimum carbon footprint. However, in complex, urban environments, their reliable operation is particularly challenging and shows a lack of performance and comfort. This study presents a quantitative benchmark of an automated shuttle compared to a conventional, human-operated bus on the same route. Speed and acceleration across geofenced segments are systematically analyzed based on over 12 million GNSS and IMU data points. The results show that the automated shuttle operates at about half the average speed of the bus. Furthermore, frequent abrupt decelerations are reducing passenger comfort, while the main distributions and mean values of the measured acceleration indicate a smooth operation of the automated shuttle; outliers reveal critical braking events. The presented methodology enables objective performance tracking and supports the iterative improvement of autonomous shuttles through datadriven optimization. Full article

European energy and climate policies have enabled reductions in greenhouse gas emissions across many sectors, with transport standing out as an exception. In this area, one of the most promising solutions is the electrification of vehicles. In urban contexts, the shift towards electrifying transport—particularly local public transport (LPT)—can yield significant benefits, especially when paired with an increasingly decarbonized electricity mix, effectively reducing tailpipe emissions of both greenhouse gases and other pollutants. Nevertheless, it is essential to assess whether eliminating tailpipe emissions simply shifts environmental impacts to other stages of a vehicle’s life cycle. The Life Cycle Assessment (LCA), employing a comprehensive cradle-to-grave approach, serves as the principal tool for such evaluations. In this framework, this study focuses on the Italian situation by using a dynamic LCA for the electricity mix. Results show that the electric bus reduces the impact on climate change (28.5 gCO₂eq/pkm vs. 66.7 gCO₂eq/pkm for Diesel, −57%), acidification, photochemical ozone formation, particulate matter, and the use of fossil resources. However, it presents higher impacts in terms of human toxicity (both carcinogenic and non-carcinogenic) and the use of mineral and metal resources, mainly due to battery production and the use of metals such gold, silver, and copper. Full article

Enhancing fuel efficiency in public transportation requires the integration of complex multimodal data into interpretable, decision-relevant insights. However, traditional analytics and visualization methods often yield fragmented outputs that demand extensive human interpretation, limiting scalability and consistency. This study presents a multi-agent framework that leverages multimodal large language models (LLMs) to automate data narration and energy insight generation. The framework coordinates three specialized agents, including a data narration agent, an LLM-as-a-judge agent, and an optional human-in-the-loop evaluator, to iteratively transform analytical artifacts into coherent, stakeholder-oriented reports. The system is validated through a real-world case study on public bus transportation in Northern Jutland, Denmark, where fuel efficiency data from 4006 trips are analyzed using Gaussian Mixture Model clustering. Comparative experiments across five state-of-the-art LLMs and three prompting paradigms identify GPT-4.1 mini with Chain-of-Thought prompting as the optimal configuration, achieving 97.3% narrative accuracy while balancing interpretability and computational cost. The findings demonstrate that multi-agent orchestration significantly enhances factual precision, coherence, and scalability in LLM-based reporting. The proposed framework establishes a replicable and domain-adaptive methodology for AI-driven narrative generation and decision support in energy informatics. Full article

Microscopic traffic simulation has a wide range of applications due to its high precision. However, the accuracy of such simulation is influenced by many factors during the simulation establishment process. This paper explores the impact of various factors on simulation results by comparing real-world traffic data, simulated data and simulations configured with different factors. The impact of these factors on simulation accuracy is evaluated by analyzing the traffic volume passing through a congested intersection in each direction. The results indicate that map correction, route iteration, and the inclusion of bus routes significantly affect simulation accuracy. An inaccurate map reduces traffic by 42%, while not-iterated routes prevent 6.6% of vehicles from using their original routes. Omitting bus routes increases the number of trips for private cars by 47%. Conversely, the inclusion of school zones has minimal impact, omitting them only reduces trips by 0.37%. Interestingly, integrating real traffic light data did not enhance simulation accuracy, likely due to discrepancies in junction turning percentages between the simulation and reality. This paper provides guidance for building accurate simulation maps using public data, enabling the creation of relatively precise models with minimal data and effort. Full article

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

Food insecurity is a pervasive public health issue in the United States. While food pantries attempt to alleviate this issue, their effectiveness is limited by structural and logistical barriers that affect service accessibility. Transportation is a frequently underexamined barrier for individuals trying to access food aid. The purpose of this study is to assess the interplay of client- and pantry-level characteristics and their influence on food aid accessibility across several transportation modalities. This cross-sectional survey study collected data from 430 food pantry clients concerning their demographics, transportation methods, and perceptions of transportation barriers. Pantry characteristics were also collected focusing on transportation infrastructure and operational policies. Individual and grouped comparisons were made between transportation methods in relation to pantry visitation, with those walking, biking, and taking a bus to the pantry grouped to compare to those taking a car. Higher food insecurity score, smaller household size, single relationship status, and race were independently associated with increased odds of walking, biking, or taking a bus to the pantry. Having closer bus stops, more bus lines, and no monthly use limits were independently associated with increased odds of walking, biking, or taking a bus to the pantry. Several characteristics were associated with specific transportation modalities when accessing food aid. Our results are particularly concerning given the increased food insecurity and additional vulnerabilities seen in those who walk, bike, or take the bus to the pantry. Transportation disadvantage may be ameliorated by less restrictive pantry use policies and more robust public transit. Full article

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers. Full article

The growth of e-commerce has imposed new challenges on urban supply chains, especially in the middle mile, which still lacks structured, sustainable and scalable logistics solutions. This study investigates the feasibility of using the Bus Rapid Transit (BRT) system, widely present in cities of emerging economies, as an urban logistics platform for the transport of light and traceable goods. This research adopts a qualitative approach, with analysis of international experiences and development of a methodological framework based on three main components: technical, economic and governance. The results reveal that the use of idle operating windows, load compatibility and institutional articulation are key factors for the implementation of the system. The proposal represents a logistical innovation aligned with the new paradigms of urban resilience and the multifunctionality of public infrastructure. This study suggests that BRT could serve as a potential logistics platform for the middle mile, under specific operational and governance conditions. Full article

The transportation sector is undergoing a rapid energy transition. Electric Vehicles (EVs) are gradually replacing conventional ones in many different sectors, but battery management still represents a critical limitation of this process. Consequently, research in this area is expanding, aiming to develop solutions that enhance performance while minimizing environmental impact. This study addresses the application of Machine Learning (ML) techniques to estimate the battery State of Charge (SoC) for a full-electric bus fleet operating public service. The methodology is built based on the available driving data disclosed from the fleet monitoring system. The ML methods are assessed starting from model-based (MB) observers assumed as reference and performances are compared upon this basis. The datasets are retrieved from a public repository or derived from real cases, particularly referring to an electric bus fleet operating for an urban public service. The most critical limitation is the absence of the electrical input data coming from the battery, typically required by model-based approaches. Despite this, the proposed ML model achieved sufficient accuracy levels (RMSE < 0.3%) comparable to those of traditional observers. These outcomes demonstrate the potential of data-driven approaches to provide scalable and more straightforward tools for battery monitoring. Full article