Search Results (344)

This study assessed the concentrations and health risks of heavy metals in cabbage (Brassica oleracea) and lettuce (Lactuca sativa) cultivated across three urban land surface temperatures in the Greater Accra Metropolitan Area (GAMA): Atomic (low land surface temperature, LST), Ashaiman (moderate LST), and Korle-Bu (high LST). The objective was to assess the influence of urban land surface temperature on heavy metal accumulation and associated human health risks. Results revealed that arsenic (As) and mercury (Hg) levels were consistently low (≤0.002 mg/kg) and remained below the maximum residue limits (MRLs) at all sites. However, cadmium (Cd), lead (Pb), and nickel (Ni) concentrations exceeded MRLs in both vegetables. Cd ranged from 1.40 ± 0.27 mg/kg (lettuce, Ashaiman) to 3.13 ± 0.99 mg/kg (cabbage, Atomic), while Pb varied between 0.90 ± 0.84 mg/kg (lettuce) and 2.62 ± 1.22 mg/kg (cabbage). Ni concentrations exceeded the permissible limit (0.2 mg/kg) across all LST zones, with the highest at Korle-Bu (0.65 ± 0.07 mg/kg). Cumulative heavy metal concentrations increased significantly (p < 0.005) with rising LST, particularly in cabbage. Noncarcinogenic risk assessment indicated that Cd and Ni were the dominant contributors to human health risk, with target hazard quotients (THQ) and hazard indices (HI) exceeding the safety threshold (HI > 1) for both adults and children, especially in Atomic and Korle-Bu. Children were more vulnerable, exhibiting higher exposure levels. Carcinogenic risk (CR) analysis further identified As, Cd, and Ni as the main carcinogens, with total cancer risk (TCR) values across all sites and age groups exceeding the USEPA acceptable range (1 × 10⁻⁶–1 × 10⁻⁴). The findings suggest that increasing urban temperatures exacerbate heavy metal accumulation in leafy vegetables, posing significant noncarcinogenic and carcinogenic health risks, particularly to children. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

This paper analyzes the dynamic behavior of a passenger bus running on a raised crosswalk. The main objective was to evaluate the vertical displacements and accelerations caused by the change in elevation, and to determine the potential for suspension damage. The study involved a numerical approach to the examination of a vehicle’s displacement related to the profile pavement by the implementation of a single body finite element module with suspension subjected to the effect of road unevenness. The so-obtained dynamic behavior with this model was implemented in MATLAB software, and the results were compared with the corresponding real-world accident data record and with an experimental study carried out with a bus running on a raised crosswalk at prescribed velocities. The velocity on the day of the accident was then calculated by computational simulations using the software PC-Crash^®. The results show that the vertical displacement caused by the raised crosswalk can vary according to the bus velocity and the raised crosswalk height. Moreover, the results show that reducing the height of the raised crosswalk and redesigning it for a smoother transition with the pavement can help in minimizing the negative effects from impacts on the bus body. The findings of this study provide valuable insights for engineers and transportation planners, and can be used to improve the design and placement of raised crosswalks in the future. Full article

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience. Full article

In this study, a novel methodology is proposed to evaluate automated driving safety in mixed traffic environments, including autonomous vehicles (AVs) and manually driven vehicles (MVs). An open-source AV dataset obtained from a real-world autonomous mobility testbed in Korea was used for methodology development and evaluations. The driving behavior was evaluated using well-known promising indicators, including the standard deviation of the vehicle speed, acceleration noise, standard deviation of the lane offset, time to collision (TTC), and deceleration to avoid a crash (DRAC). Min-max and max-min normalization was performed to unify the units of the evaluation indicators. The importance of each driving safety indicator was derived through the Analytical Hierarchy Process (AHP) performed by traffic experts, and the weights were estimated based on the average of the collected importance. The normalized indicators were integrated to obtain the automated driving risk score (ADRS), which is regarded as a measure of automated driving safety. The automated driving safety degraded considerably in road sections where right turns were made at intersections and that had a bus stop. Hazardous driving events of AVs were visualized, which is useful for monitoring mixed traffic safety and developing effective countermeasures for proactive road safety management. Full article

The first extensive Bus Rapid Transit (BRT) system in the Philippines, the EDSA Busway, was put into place as a result of Metro Manila’s ongoing traffic congestion. This study uses an integrated framework that combines cost–benefit analysis (CBA), commuter perception survey, and traffic simulation to assess its economic, social, and environmental implications. The operational viability and traffic impact of the planned Magallanes BRT station were evaluated through simulation using PTV VISSIM. A total of 385 commuters participated in a survey measuring their impressions of safety, accessibility, and satisfaction using a four-point Likert scale. The Busway’s excellent economic feasibility was confirmed by the CBA results, which showed a Benefit–Cost Ratio (BCR) of 15.38 and a Net Present Value (NPV) of ₱778.64 billion. Results from the simulation showed a 24% decrease in PM₂ emissions, a 75% increase in throughput, and a 64% reduction in bus trip time. According to survey results, 61% of commuters said accessibility had improved and 62% said travel satisfaction had increased. The study supports the EDSA Busway’s status as a feasible model for future BRT expansion in Metro Manila and other emerging metropolitan regions by showing how it greatly improves environmental sustainability and mobility efficiency. Full article

The objective of advancing sustainable public transportation extends beyond merely reducing pollution; it also aims to enhance the comfort and well-being of both passengers and drivers. This research investigates the influence of the dynamic characteristics of diesel and electric city buses on human comfort, focusing specifically on vibration analysis. Vibrations have a significant impact on the durability of vehicle structures, passenger safety, and drivers’ working conditions, and long-term exposure can have negative health consequences. Based on experimental measurements and mathematical modeling, a dynamic model of a city bus was created, allowing us to assess the damping properties of suspension elements and the effect of load on vibrations. The findings of the study indicate that the judicious implementation of structural solutions and technological measures enhances the reliability of the transport system while simultaneously fostering the advancement of more sustainable and safer public transport options. The acquired data hold significance for both the development of new electric buses and the refurbishment of existing vehicles, aiming to integrate energy efficiency, comfort, and sustainable mobility. 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

The increasing integration of decentralized technologies such as photovoltaic (PV) systems and electric vehicles (EVs) poses significant challenges to the reliable operation of radial distribution grids. In this paper, we study Active Power Curtailment (APC), which is a cost-effective method that maintains grid safety by temporarily reducing power injections. However, APC can place disproportional curtailment burden on grid buses that may in fact undermine the continuous adoption of PVs and EVs. In this work, we propose different novel APC methods that incorporate fairness properties for radial grids with PVs, EVs, and battery energy storage systems (BESSs). In addition, we integrate BESSs and show their benefits in lowering APC levels and achieving better PV and EV utilization while enhancing fairness. The proposed APC designs allow for fast decision making and can be generalized to unseen grids. To do so, a two-step solution is adopted, where in the first step, a reinforcement learning (RL)-based agent determines uniform per-feeder APC and BESS actions, and in the second step, heuristic controllers disaggregate these actions into tailored per-bus decisions while incorporating fairness features. Through simulations, the controllers are shown to mitigate over $99\%$ of constraint violations and significantly enhance fairness in curtailment distribution. BESSs are shown to improve the violations count and APC trade-off, leaning towards reduced APC percentages. Finally, we exemplify how the solution generalizes effectively to unseen grid configurations. Full article

Large-scale blackouts in power systems are often triggered by weak links susceptible to cascading failures. As the concentrated reflection of the system’s weak links, identifying key transmission sections and further implementing safety control measures are of great significance for ensuring the stable operation of the system. This paper proposes a multi-criteria-based method for identifying key transmission sections and an optimal strategy for the prevention–emergency coordinated control of key transmission sections. Firstly, a line criticality index based on three characteristics—topology, power flow, and voltage—has been established to identify critical lines. Furthermore, search for all initial transmission sections that include the critical line, and form the initial transmission section set for each critical line, then, based on the analysis of the Theil index of power flow impact rate distribution after the failure of critical lines, a key transmission section identification method integrating multiple criteria is proposed. Then, based on the anticipated faults of key transmission sections, an optimization model for the prevention–emergency coordinated control of key transmission sections is established. A constraint relaxation factor is introduced to divide the above model into two independent sub-problems, then the golden section method is applied to update the value of constraint relaxation factors, so as to iteratively search for the optimal solution of the model. Finally, the feasibility and correctness of the proposed method are verified through the simulation and analysis of the IEEE 39-bus system. The results demonstrate that the proposed method can effectively identify the key transmission sections of the system and improve the operational safety of the system through the prevention–emergency coordinated optimal control strategy. 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

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids. Full article

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics. Full article

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles. Full article

Bus stops in Riobamba had significant deficiencies in safety, accessibility, and comfort, which limited the effective use of public transport and affected the urban mobility of the population. Improving these conditions was crucial to promote sustainable, inclusive and safe mobility in the city. This study was quantitative and descriptive, based on 420 user surveys and the direct observation of 140 stops, complemented with georeferencing and comparative review of specialized literature. The findings showed that most of the stops lacked adequate lighting, shelter, signage and universal access, with 68% of users perceiving low safety. The most in-demand technologies included real-time information systems (72%) and video surveillance (65%). The proposed model of smart stops will improve accessibility, safety and comfort for users, encouraging greater use of public transport. By addressing the main infrastructure and technology gaps, the intervention contributed to inclusive and safe urban mobility, directly contributing to Sustainable Development Goal 11 and offering a replicable framework for other medium-sized cities seeking to optimize their public transport systems. Full article