Search Results (1,726)

The transport sector is responsible for about 60% of emissions in the atmosphere due to the exhaust-polluting gases of internal combustion engine (ICE) vehicles. An effective solution to this issue is the electrification of the transport means, which can significantly reduce pollution, especially in urban areas. Apart from the necessary technological advancements that must improve the battery performances, the diffusion of electric vehicles (EVs) must be further supported and facilitated by new dedicated services and tools for electric vehicle users and operators aiming at improving the travel and charging experience. To this goal, this paper proposes new models based on Timed Colored Petri Nets (TCPN) to simulate and manage the charge demand of the EV fleet. At first, the proposed tool must take into account the charging requests from different EV drivers with different charging need located in different geographical areas. This is possible by knowing input data such as EV current location, battery data, charge points (CPs) availability, and compatibility. In particular, EV drivers are simulated when finding and booking the preferred charge option according to the available infrastructure in the area of interest and the CPs tariff and power rate. The proposed TCPN is designed to model the multi-user charging demand in specific geographic areas, and it is evaluated in several scenarios of a case study to measure its performance in serving multiple EV users. Full article

This article investigates the safety potential of a freight transportation company, considering tire set selection as one of the most important aspects to ensure safe driving and a reliable transportation service. The revision of tire sets selection in large vehicle fleets is attributed to a new regulation from the United Nations to maintain non-deteriorating tire wet braking performance up to a minimum allowable wear limit, encouraging both safety and sector sustainability, as a significant part of tires are currently replaced before reaching a tread depth of 3 mm. In this research, an experimental test was conducted that involved four maneuvers with a truck using ten different sets of tires (including new and retreaded) to determine which set performs better in critical driving conditions. The results are then analyzed using the TOPSIS method where the most efficient set of tires and the best alternatives are selected. Finally, the safety of trucks on the road using the appropriate set of tires is evaluated by the estimated accident reduction potential. It should be mentioned that the optimal selection of the truck tire set is also important for sustainable transportation, as the pollution of worn tires remains a relevant environmental issue. Full article

Renewable energy sources and energy independence are becoming increasingly important worldwide, and reducing emissions and optimizing energy use are high on the EU’s agenda. In this context, electric and hybrid vehicles could not only be a means of transport but also an active part of the grid. This paper analyzes one year of empirical data of a hybrid vehicle using a linear programing method that allows the optimization of energy return under different settings. The aim of the study is to determine the contribution that vehicles can make to the stability of the grid and the functioning of energy communities. It also compares the distribution of energy sources used in the EU and presents the current range of V2G-capable vehicle models. The results show that hybrid vehicles can also be effective energy storage devices, especially at fleet level. V2G technology could influence the development of battery production and contribute to the expansion of secondary markets by enabling the recycling of degraded batteries for buildings or renewable energy systems. The article also summarizes the development opportunities and challenges for V2G technology, in particular its role in energy grids and sustainable transport. Full article

In the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such as search and rescue operations, environmental monitoring, and surveillance. However, achieving global situational awareness, although essential, represents a significant challenge, due to computational and communication constraints. This paper proposes a Distributed Moving Horizon Estimation (DMHE) technique that integrates consensus theory and Moving Horizon Estimation to optimize computational efficiency, minimize communication requirements, and enhance system robustness. The proposed DMHE framework is applied to a formation of UAVs performing target detection and tracking in challenging environments. It provides a fully distributed architecture that enables UAVs to estimate the position and velocity of other fleet members while simultaneously detecting static and dynamic targets. The effectiveness of the technique is proved by several numerical simulation, including an in-depth sensitivity analysis of key algorithm parameters, such as fleet network topology and consensus iterations and the evaluation of the robustness against node faults and information losses. Full article

The FuelEU Maritime Regulation, part of the European Union’s (EU’s) Fit for 55 initiative, aims to achieve significant reductions in greenhouse gas (GHG) emissions within the maritime sector. This study assesses the feasibility of alternative fuels for the Estonian pilot fleet using a Well-to-Wake (WtW) life cycle assessment (LCA) methodology. Operational data from 18 vessels, sourced from the Estonian State Fleet’s records, were analyzed, including technical specifications, fuel consumption patterns, and operational scenarios. The study focused on marine diesel oil (MDO), biomethane, hydrogen, biodiesel, ammonia, and hydrotreated vegetable oil (HVO), each presenting distinct trade-offs. Biomethane achieved a 59% GHG emissions reduction but required a volumetric storage capacity up to 353% higher compared to MDO. Biodiesel reduced GHG emissions by 41.2%, offering moderate compatibility with existing systems while requiring up to 23% larger storage volumes. HVO demonstrated a 43.6% emissions reduction with seamless integration into existing marine engines. Ammonia showed strong potential for long-term decarbonization, but its adoption is hindered by low energy density and complex storage requirements. This research underscores the importance of a holistic evaluation of alternative fuels, taking into account technical, economic, and environmental factors specific to regional and operational contexts. The findings offer a quantitative basis for policymakers and maritime stakeholders to develop effective decarbonization strategies for the Baltic Sea region. Full article

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 10⁵ 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NO_x) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NO_x emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies. Full article

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements. Full article

This paper addresses the challenge of coordinating task allocation and generating collision-free trajectories for a fleet of mobile robots in dynamic environments. Our approach introduces an integrated framework comprising a centralized task allocation system and a distributed trajectory planner. The centralized task allocation system, employing a heuristic approach, aims to minimize the maximum spatial cost among the slowest robots. Tasks and trajectories are continuously refined using a distributed version of CHOMP (Covariant Hamiltonian Optimization for Motion Planning), tailored for multiple-wheeled mobile robots where the spatial costs are derived from a high-level global path planner. By employing this combined methodology, we are able to achieve near-optimal solutions and collision-free trajectories with computational performance for up to 50 robots within seconds. Full article

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices. Full article

The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

We evaluated the energy consumption of a 3T MRI using a central monitoring system, focusing on hospital energy costs during peak winter months from 2021 to 2023. We analyzed consumption during non-productive phases like end-of-day standby and assessed their impact. For active use, we compared standard and AI-enhanced protocols on phantoms, scheduling high-demand protocols during off-peak hours to benefit from lower energy prices. Standard protocols consumed 3.4 to 15 kWh, while optimized protocols used 2.3 to 10.6 kWh, reducing consumption by 32% on average. Savings per scan ranged from EUR 0.03 to EUR 3.7. The electrical consumption of a brain MRI protocol is equivalent to that of 3–4 knee protocols or 2–3 lumbar spine protocols. Using AI-optimized protocols and management, 41 protocols can be completed in 12 h, up from 30, reducing daily costs by EUR 2.38 to EUR 29.18. Annually, AI-optimized protocols could save 7900 to 8800 kWh per MRI unit, totaling 10,500 to 11,600 MWh across France’s MRI fleet, equivalent to the yearly consumption of about 4700 to 5300 people. Optimizing MRI resource use can expand patient access while significantly reducing the associated energy footprint. These findings support the implementation of more sustainable practices in medical imaging without compromising care quality. Full article

This study deals with the assessment of the progress of the electromobility programme with regard to the technical availability of public transport buses. The aim of this study was to conduct a comparative assessment of the availability of electric buses in relation to diesel buses (of analogous capacity). The study was carried out in real operating conditions using the example of Lublin city. Over a period of 33 months, the availability of electric buses and diesel buses was tested in a sample of 18 vehicles for each type of bus. Availability indices were compared using the method of variance analysis. It was found that electric buses had a higher level of availability and no trend during the study period. The applied research method can be used in operational practice to monitor the risks associated with vehicle failure rates and the continuity of fleet operations. Full article

The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native principles, where distributed computing nodes often have unfixed and changing networking configurations. Unlike traditional approaches, this architecture also focuses on managing services across massively distributed mobile nodes, as demonstrated in the automotive use case presented. Apart from working as MANO framework, the proposed solution efficiently handles service lifecycle management in large fleets of vehicles without relying on public or static IP addresses for connectivity. Its modular, microservices-based approach ensures adaptability to emerging trends like Edge Native, WebAssembly and RISC-V, positioning it as a forward-looking innovation for IoT ecosystems. Full article

The introduction of a significant industrial carbon price in Alberta, Canada, has precipitated major changes in its electricity market, both for fossil fuel generators, which has resulted in a rapid transition from coal to natural gas, as well as for renewable energy projects, which can monetize emission offset credits. Coal, which generated close to half of the electricity in the province in 2016 before the major changes were introduced, had fallen to less than 8 percent by the end of 2023 and was completely phased out by June 2024. Conversely, wind energy grew from 6 to 12 percent of the annual supply, in part due to the increasing value of the carbon credits whose value is connected to the deemed greenhouse emissions they are displacing. As wind energy increased in penetration, it lowered its own market price, which was discounted from the average market price by 10–43 percent, but in turn increased the relative importance of its offset. This paper examines the evolution of emissions displaced by wind energy in Alberta by considering 10 years of historical merit order data and creating a counterfactual scenario where historical wind generation is replaced by next-in-merit units. On average, coal made up 84 percent of the marginal energy and 93 percent of the marginal emissions in 2018. As the coal capacity declined, natural gas units replaced coal on the margins, jumping from 21 percent of next-in-merit generation in 2020 to 84 percent in 2023. Alberta uses a deemed emissions displacement factor, which is a combination of historical build and operating margins that declined from 0.65 tCO₂e/MWh in 2010 to 0.52 tCO₂e/MWh in 2023. Using the counterfactual scenario, an alternative offset value is considered, which had a maximum difference of 57 percent (9 CAD/MWh) of increased value over the actual historical offset. However, the counterfactual rate of emission offsets fell to near parity with the deemed grid displacement factor by 2022 as natural gas became increasingly dominant in the market. As the carbon price is scheduled to increase from 65 CAD/tCO₂e in 2023 to 170 CAD/tCO₂e by 2030, the provincial offset could reach a maximum value of 53 CAD/MWh in 2030 but begin to decline thereafter as the carbon price drives decarbonization, thereby lowering displaced emissions in either method of calculation. The introduction of significant carbon pricing into a thermally dominated electricity market resulted in more emissions being displaced by renewable energy than they were credited for in the short term, but the resultant decarbonization of the grid decreases the long-term value of emission offsets. Full article