Search Results (849)

With the rise of computation-intensive and latency-sensitive applications in the Internet of Vehicles (IoV), vehicles face increasing computational pressure. Computation offloading has become a key strategy for enhancing processing capabilities. Meanwhile, growing IoV data traffic raises security and reliability concerns. Existing blockchain-based solutions secure data transmission but overlook added delay and energy costs, increasing overall system cost. To address this issue, a blockchain-assisted computation offloading strategy based on Twin Delayed Deterministic Policy Gradient (TD3) (B-COTD) is proposed. Specifically, the offloading strategy selection is formulated as a multi-objective optimization problem considering latency, energy consumption, and blockchain costs, with the Delegated Byzantine Fault Tolerance (DBFT) algorithm ensuring the security of the offloading process. The TD3 algorithm solves this optimization problem, achieving efficient task offloading. Extensive experiments show that B-COTD improves overall performance, with the total system cost reduced by approximately 23.89% on average and the offloading success rate increased by about 11.02%. Full article

Addressing the data scarcity and complex consumption characteristics in mid-to-long-term electricity load forecasting for new canals, this study proposes a novel model based on navigation traffic volume cascade mapping. A multidimensional feature matrix integrating economic indicators, meteorological factors, and facility constraints is established, with canal similarity quantified via integrated constraint optimization weighting to derive multisource fusion weights. These enable freight volume prediction through feature migration using comprehensive transportation sharing. The “freight volume–lockage volume–electricity consumption” cascade then applies tonnage-based mapping to capture vessel evolution trends, generating lockage volume forecasts. Core consumption components are predicted through a mechanistic-data hybrid model for ship lock operations and a three-layer “Node–Behavior–Energy” framework for shore power system characterization, integrated with auxiliary consumption to produce the operational mid-to-long-term load forecast. Case analysis of the Pinglu Canal (2027–2050) reveals an overall “rapid-growth-then-stabilization” electricity consumption trend, where shore power’s proportion surges from 24.1% (2027) to 67.8% (2050)—confirming its decarbonization centrality—while lock system consumption declines from 28.6% to 17.2% reflecting efficiency gains from vessel upsizing and strict adherence to navigation intensity constraints.The model provides foundations for green canal energy deployment, proving essential for establishing eco-friendly waterborne logistics. Full article

This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article

Rapid urbanization and the widespread use of impervious materials have intensified the urban heat island (UHI) effect, raising surface temperatures and energy demands. Conventional concrete pavements contribute significantly due to their high thermal conductivity and low reflectivity. This study systematically investigates the development of thermally efficient concrete paver blocks using sustainable alternative fine aggregates to mitigate heat accumulation while retaining a minimum compressive strength of 35–45 MPa (recommended for medium traffic). Unlike prior isolated studies, this research offers a comprehensive comparative analysis of three sand replacements—Vermiculite powder (12.5–50%), Perlite powder (20–80%), and Crushed Glass (7.5–30%)—in M30-grade concrete. Fresh and hardened properties were evaluated through slump, density, and compressive strength tests at 7, 14, and 28 days, while infrared thermography quantified surface temperature variations under controlled heat exposure. Results showed significant thermal improvements, with optimal mixes Vermiculite 25% (VC-25), Perlite 40% (PR-40), and Crushed Glass 15% (CG-15) reducing surface temperatures by 25.1 °C, 22.2 °C, and 18.2 °C, respectively, while maintaining compressive strengths of 47.8 MPa, 38.8 MPa, and ~58 MPa. VC-25 proved superior, achieving the lowest surface temperature (26.3 °C) and 48.8% lower heat absorption than conventional concrete. The study establishes optimal replacement thresholds balancing insulation and strength, supporting SDGs 11, 12, and 13 through climate-responsive, resource-efficient construction materials. Full article

Road traffic significantly contributes to fuel consumption and emissions. Fortunately, the advent of cooperative adaptive cruise control (CACC), facilitated by vehicle-to-vehicle (V2V) communication, reduces energy consumption and improves efficiency in transportation systems. Nevertheless, V2V communication performance (V2VCP) is highly vulnerable to degradation due to various factors. Limited comprehension exists regarding the generalized modeling of subpar V2V communication performance (SV2VCP), coupled with limited exploration of its resulting impacts on environmental sustainability. To bridge these gaps, this study presents the first attempt to assess the impact of SV2VCP on fuel consumption and exhaust emissions within the CACC framework. More specifically, we adopt the multi-predecessor following (MPF) topology and model SV2VCP scenarios, along with proposing five vehicle state update methods (VSUMs). Subsequently, by simulating various SV2VCP and driving scenarios, we comprehensively understand the effects of different VSUMs, SV2VCP, and abnormal vehicle positions on the safety, emissions, and energy consumption of the platoon. The results reveal that SV2VCP substantially impacts the fuel efficiency and emission performance of the CACC platoon, with fuel consumption during deceleration exceeding that of acceleration by approximately 14% when all vehicles are subject to SV2VCP. Furthermore, our study provides critical recommendations for optimal strategy selection, aiming to foster energy conservation and emission reductions, thereby promoting sustainable transport systems. Full article

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

Depth estimation is a critical enabling technology for sustainable intelligent transportation systems (ITSs), as it supports essential functions such as obstacle detection, navigation, and traffic management. However, existing Neural Radiance Field (NeRF)-based monocular depth estimation methods often suffer from high computational costs and poor performance in occluded regions, limiting their applicability in real-world, resource-constrained environments. To address these challenges, this paper proposes a lightweight monocular depth estimation framework that integrates a novel capacity redistribution strategy and an adaptive occlusion-aware training mechanism. By shifting computational load from resource-intensive multi-layer perceptrons (MLPs) to efficient separable convolutional encoder–decoder networks, our method significantly reduces memory usage to 234 MB while maintaining competitive accuracy. Furthermore, a divide-and-conquer training strategy explicitly handles occluded regions, improving reconstruction quality in complex urban scenarios. Experimental evaluations on the KITTI and V2X-Sim datasets demonstrate that our approach not only achieves superior depth estimation performance but also supports real-time operation on edge devices. This work contributes to the sustainable development of ITS by offering a practical, efficient, and scalable solution for environmental perception, with potential benefits for energy efficiency, system affordability, and large-scale deployment. Full article

Long-span steel box girder suspension bridges are prone to vortex-induced vibrations (VIVs) due to their light weight, flexible characteristics, and low structural damping. Traditional temporary aerodynamic measures, although effective in vibration suppression, involve prolonged construction periods and high costs, leading to traffic disruptions and considerable socio-economic losses. To address these limitations, this study implemented rapid vibration suppression by prescribing designated lanes and traveling speeds for vehicles with varying aerodynamic configurations, dynamically arranged on the bridge deck for efficient vibration control. Through CFD numerical simulations, the influence of vehicle placement on vibration suppression efficiency was systematically investigated. The results indicated that the strategic arrangement of vehicles could reduce the root-mean-square (RMS) amplitude of VIV of the main girder by more than 75%, with suppression efficiency significantly correlated with the spatial distribution of the vehicles. Moreover, the suppression mechanism was analyzed, revealing that resonance occurs when the vortex-shedding frequency matches the natural frequency of the main girder in the absence of suppression measures. Vehicle deployment alters the vortex-shedding frequency from the bridge surface, shifting it away from the structural natural frequency, while simultaneously weakening the periodic energy input from vortex shedding, thus effectively mitigating the vibration response. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

In commercial aviation, accurate estimation of the remaining track miles (RTM) during descent is essential for energy-efficient trajectory management. Currently, pilots often rely on heuristics and experience due to the lack of consistent RTM information, which can result in suboptimal decisions. This study investigates the accuracy of RTM estimations made by commercial pilots through a structured survey involving scenario-based assessments across seven European airports. Results show a consistent underestimation bias, with a root mean square error (RMSE) of 9.69 NM. To quantify the potential of data-driven alternatives, a machine learning model based on gradient boosting was developed using ADS-B surveillance and weather data. The model achieved significantly lower prediction errors, with an RMSE of 5.43 NM, particularly outperforming pilots in early descent segments. Feature importance analysis revealed that spatial and trajectory-related variables were key to accurate predictions. The findings suggest that integrating predictive models into flight management systems or pilot decision support tools could improve descent planning and operational efficiency. This study provides an empirical comparison between human and AI-based RTM estimations, highlighting the potential for machine learning to complement pilot expertise in future air traffic operations. Full article

Microscopic Particulate Matter (PM) below 10 µm can enter the respiratory system and affect human health in the short and long term. Railway enclosures are sites with high concentrations of fine PM and technical solutions like mechanical filtration exist to increase the air quality. However, several crucial factors must be evaluated and optimized like energy consumption, maintenance cost/interval, design and control. A fast and adaptable evaluation of decontamination solutions is required to find the optimal solution. To answer this, a 1D multizone model based on station discretization aligned with the track direction is proposed to precisely place decontamination systems along the station. In each zone, a set of ordinary differential equations is used to forecast the daily progression of PM concentrations, based on physical parameters (air and train velocities, and train traffic) used to describe the different physical phenomena (resuspension, deposition, ventilation and generation). Three-dimensional CFD (Computational Fluid Dynamics) simulations are used to characterize the efficiency and range of decontamination products and reproduce their effect in the 1D model. This approach allows for flexible optimization of local and global decontamination efficiencies with multiple parameter changes. PM₁₀ and PM_2.5 (below 10 and 2.5 µm) are studied here as they are often monitored. Full article

Urban intersections are critical nodes where traffic congestion and energy inefficiency converge. Traditional signal control systems often optimize either mobility or sustainability, creating an asymmetry between flow efficiency and environmental impact. This study introduces a symmetry-aware generative optimization framework that leverages Generative Artificial Intelligence (GAI) to balance both dimensions. Using the microscopic simulator SUMO, we modeled a signalized intersection in Colima, Mexico, under five control strategies: Fixed Time (baseline), GPT-4o, GPT-5 Thinking, Gemini 2.5 Pro, and DeepSeek V3. Each Large Language Model (LLM) received structured simulation data and generated new phase-duration configurations to minimize queue length, travel time, and CO₂ emissions while improving average speed. Step-level performance was evaluated using descriptive statistics, and Wilcoxon signed-rank tests paired with Holm–Bonferroni correction. Results show that all LLM-based controllers significantly outperformed the Fixed Time baseline (adjusted p ≤ 4.8 × 10⁻⁶), with large effect sizes (|dz| ≈ 1.5–2.6). GPT-5 achieved the strongest performance, reducing queue size by ≈ 44%, CO₂ emissions by ≈ 17%, and increasing average speed by ≈ 58%. The results validate the feasibility of symmetry-aware generative reasoning for sustainable traffic optimization and establish a reproducible methodological framework applicable to future AI-driven urban mobility systems. Full article

Cooperative and Autonomous Vehicle (CAV) platoons offer significant potential for improving road safety, traffic efficiency, and energy consumption, but maintaining precise inter-vehicle spacing and synchronized velocity under disturbances while ensuring string stability remains challenging. This paper presents a fully decentralized two-layer architecture for homogeneous platoons whose identical vehicle dynamics and information flow produce an inherent symmetrical system structure. Operating under a predecessor-following topology with a constant time headway policy, the upper layer generates a smooth velocity reference based on local spacing and relative-velocity errors, while the lower layer employs a two-degree-of-freedom (2-DOF) digital RST controller designed through discrete-time pole placement and sensitivity-function shaping. The 2-DOF structure enables independent tuning of tracking and disturbance-rejection dynamics and provides a computationally lightweight solution suitable for embedded automotive platforms. The paper develops a stability analysis demonstrating internal stability and $L_2$ string stability within this symmetrical closed-loop architecture. Simulations confirm string-stable behavior with attenuated spacing and velocity errors across the platoon during aggressive leader maneuvers and under input disturbances. The proposed method yields smooth control effort, fast transient recovery, and accurate spacing regulation, offering a robust and scalable control strategy for real-time longitudinal motion control in connected and automated vehicle platoons. Full article

In the analysis of electric vehicle (EV) energy consumption, three main approaches are commonly used: physics-based models, artificial intelligence (AI) models, and hybrid frameworks that combine both. This combination enables more accurate estimations of EV energy consumption under diverse operating conditions, while also supporting applications in eco-driving, route planning, and urban energy management. Accurate analysis and prediction of EV energy consumption are critical for vehicle design, route planning, grid integration, and range anxiety. Recent advances in AI, notably machine learning (ML) and deep learning (DL), enable data-driven models that capture complex interactions among driving behavior, vehicle characteristics, road topology, traffic, and environmental conditions. This paper reviews the state of the art and presents a structured methodology for building, validating, and deploying AI models for EV energy consumption and efficiency analysis. Features, model architectures, performance metrics, explainability techniques, and system-level applications are discussed. Full article

This article addresses selected aspects of designing a Traffic Management System (TMS) for the railway component of Poland’s Central Communication Port (CPK) project. The primary objective was to determine train headway times while considering automated traffic conflict resolution and speed profile optimization in relation to traction energy consumption. The study utilized simulations in the MATLAB/Simulink (Version number: R2024a Update 3) environment, modeling the movement of an ETR610 (ED250) train on a line equipped with the European Train Control System (ETCS). The simulation results provided insights into the impact of the adopted assumptions on TMS operational efficiency under failure conditions and its capability to optimize train movements. The conclusions underscore the critical importance of time reserves in effective conflict resolution, the interplay between buffer allocation and speed restrictions, and the impact of minimizing train stops on energy consumption. They also highlight the necessity of adapting operational strategies to infrastructure characteristics and the influence of simulation time on the effectiveness of conflict resolution methods. Furthermore, the study emphasizes the need to broaden operational scenarios to include failures of traction vehicles and train control systems, along with appropriate planning for time reserves. Full article