Abstract: Improving the efficiency of energy consumption, conversion, and transmission on merchant ships has become a critical challenge due to rising fuel costs, increasingly stringent environmental regulations, and the introduction of operational efficiency requirements such as the IMO CII. Existing energy-efficiency metrics are predominantly based on abso-lute or design-oriented indicators and do not adequately capture the latent reserves of energy savings embedded in ship energy systems. This study addresses this gap by proposing a methodological framework for quantifying energy efficiency through the concept of relative energy-saving potential. The proposed approach integrates ship energy balance analysis with a hierarchical assessment of relative theoretical, technical, and economical-ly feasible energy-saving potentials. The methodology is demonstrated through an illustrative case study of a medium-size product tanker, focusing on the main engine, auxiliary generators, pumping systems, and HVAC loads. The results indicate that a significant share of energy losses can be systematically identified and progressively constrained by technical and economic feasibility considerations, providing a transparent basis for prior-itizing energy efficiency measures. The study concludes that relative energy-saving potentials offer an effective and scalable foundation for ship energy management, supporting SEEMP implementation, CII compliance strategies, and integration into digital twin and AI-based energy management systems.

Abstract: To mitigate structural vibrations caused by liquid sloshing inside the suspended water tank of high-speed trains and to prevent issues such as baffle fatigue failure and water leakage from tank cracking, this study designed an acoustic black hole (ABH)-type baffle that comprehensively considers both vibration and wave suppression performance. Based on acoustic black hole (ABH) theory, numerical simulations were conducted using the CFD software Fluent to analyze the vibration and wave suppression characteristics of the ABH-type baffle under lateral and longitudinal impact conditions. The influence of the position and number of ABH structures on the baffle’s performance was systematically examined. Finally, the structural strength and the vibration/wave suppression capability of the baffle were validated.The results demonstrate that the structural strength of the ABH-type baffle meets the design requirements. Compared to a conventional baffle, the ABH-type baffle reduces the liquid sloshing force inside the tank, lowers the peak sloshing pressure under various operating conditions, and decreases the surface vibration velocity of the baffle within its dominant vibration frequency range of 0–100 Hz. The optimal positions for the ABHs are at the 80% and 20% water-level lines on the baffle, and the best suppression performance is achieved when the center of the ABH is aligned horizontally with the liquid surface. Furthermore, the vibration and wave suppression capability deteriorates when the number of ABHs is either greater or fewer than three.

Abstract: Urban curbside loading and unloading zones are increasingly affected by competing non logistics uses, such as outdoor terraces or resident parking, leading to reductions in effective curbside length. These design decisions can significantly alter service capacity and generate environmental externalities in urban freight operations that are rarely quantified. This study introduces the Factor of Occupancy (Fo) as a space–time design indicator for curbside unloading zones, defined as the product of effective curbside length and the maximum authorised dwell time. Using direct observational data from an urban block in Zaragoza (Spain), the analysis focuses on a loading and unloading zone whose effective length was reduced by approximately 6 m due to the installation of a restaurant terrace. Two curbside configurations are compared: a reduced configuration (8 m) and a restored configuration (14 m), keeping demand and temporal constraints constant. Fo is integrated into a loss based queueing model (M/M/1/1) to estimate blocking probabilities and the number of served and rejected freight operations. To capture the environmental implications of curbside capacity loss, the paper proposes the Hidden Carbon Emissions (HCE) indicator, which quantifies the additional CO₂ emissions generated by rejected vehicles through block recirculation and idling during illegal occupancy, based on observed behaviour and publicly available emission factors. Results show that restoring curbside length substantially increases effective service capacity and reduces rejected vehicles, leading to a marked decrease in hidden CO₂ emissions per operation. The findings highlight that minor curbside design decisions can produce measurable impacts on both urban freight efficiency and environmental performance.

Abstract: Traffic congestion not only severely impacts the quality of residents' daily travel and increases travel costs, but also triggers traffic accidents, causes environmental pollution, and leads to resource wastage. There is a practical need for implementing engineering measures simultaneously at multiple intersections during urban road traffic congestion mitigation, necessitating in-depth research on the selection of critical intersection clusters. Based on existing research, the relationship between vehicle emissions and saturation degree was derived. The network efficiency evaluation metric was refined using saturation degree, and a model linking vehicle emissions to network efficiency was established. A validation experiment was designed using the core road network of Xining City, Qinghai Province as an example. Results indicate that vehicular exhaust emissions per kilometer are proportional to the saturation degree metric value. The network efficiency metric value is inversely proportional to the overall (or average) saturation degree of the network. Vehicular exhaust emissions exhibit an inverse relationship with network efficiency. As the road traffic operational state shifts from congestion to smooth flow, for every 1-unit increase in network efficiency value, the average exhaust emissions per vehicle per kilometer decrease by 3.976 kg. Different congestion mitigation node selection schemes correspond to varying total emission reductions during the morning peak. If ranked by the magnitude of change in network efficiency (from largest increase to smallest), the corresponding total morning peak emission reductions gradually decrease in a stepwise manner. According to the 2 out of 60 and 3 out of 60 experimental results, compared to the worst node cluster selection scheme, the optimal node cluster selection scheme can reduce vehicular exhaust emissions by 4441 kg and 6616 kg, respectively.

Abstract: The main objective of this study is to automatically detect real-time snow-related road-surface conditions utilizing existing webcams along interstate freeways. Blowing snow is considered one of the most critical road surface conditions, causing vertigo and adversely affecting vehicle performance. A comprehensive image reduction process was performed to extract two distinct reference datasets. The first dataset comprised two image categories: blowing snow and no blowing snow, while the second dataset consisted of five categories: blowing snow, dry, slushy, snow-patched, and snow-covered. Six pre-trained convolutional neural networks (CNN) were utilized for road-surface condition classification: AlexNet, SqueezeNet, ShuffleNet, ResNet18, GoogleNet, and ResNet50. In Dataset 1, it was concluded that AlexNet is a superior model with respect to training time and 97.56% overall detection accuracy. Regardless of differences in training time, ResNet50 achieved the highest overall accuracy of 97.88%, as well as the highest recall and F1-score. In Dataset 2, the ResNet18 model achieved an optimal overall detection accuracy of 96.10%, while the AlexNet model demonstrated the shortest training time and an overall detection accuracy of 95.88%. In addition, a comprehensive comparison was conducted between pre-trained CNNs and traditional machine learning models, with the former displaying significantly superior detection performance. Analysis of the confusion matrices revealed that AlexNet performed the best in detecting blowing snow events. The proposed models could automatically provide real-time accurate and consistent surface condition information.

Abstract: This paper introduces a hybrid framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with a focus on the Pods4Rail project. The methodology combines qualitative and quantitative approaches to address the inherent uncertainty of early design phases. The qualitative component evaluates Technology Readiness Levels (TRLs) for individual subsystems using expert judgment and visual heat maps, identifying critical challenges in automation, digitalization, and sustainability. The quantitative approach distinguishes between the probabilistic model—representing the uncertainties in TRL and IRL—the problem of propagating these uncertainties to estimate the System Readiness Level (SRL), and the algorithm used to solve this problem, which in this case is Monte Carlo simulation. This framework enables SRL estimation under uncertainty, where explicit quantification of uncertainties is essential for sound decision-making. Results indicate that Pods4Rail project currently falls between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While subsystems such as the Transport Unit and Rail Carrier Unit exhibit higher maturity, planning and logistics remain less developed. By combining interpretative insights with statistical rigor, this framework provides a comprehensive readiness assessment and supports informed decision-making for future integration and risk management. The proposed approach is transferable to other innovative mobility systems facing similar challenges in early development stages.

Abstract: Drivers with color vision deficiency (CVD) often face difficulty recognizing traffic light colors at intersections, putting at risk their safety and independence while driving in city environments.  This study presents the development of an assistive prototype designed with Python and a PyQt5 graphical user interface. The system applies a YOLOv12 model, a Convolutional Neural Network-based object identification method that uses the OpenCV Python library that has been trained and evaluated on a comprehensive dataset consisting of various conditions, such as daytime and nighttime circumstances, clear and rainy weather, and traffic density, to recognize traffic light signals as red, yellow, and green.  The detection result of traffic light color from a car webcam is delivered to users with offline audio feedback available in Indonesian, Mandarin, and English.  During testing, we found a mean average precision of 0.74 across eight challenging scenarios and a maximum confidence of 0.95. The system aims to improve driving safety for individuals with color vision deficiency, offering an additional assistive device rather than replacing standard driving regulations.

Abstract: Road freight transportation remains the dominant mode for goods distribution worldwide, with articulated vehicles playing a critical role in this sector. However, these vehicles are prone to severe instability phenomena such as jackknifing, trailer sway, and rollover, particularly under high-speed or emergency maneuvers. This paper presents an advanced steering stability control strategy for articulated vehicles based on Model Predictive Control (MPC) and differential braking, aiming to enhance lateral and yaw stability during autonomous driving operations. The proposed controller integrates trajectory tracking and yaw stability objectives within a unified optimization framework, systematically handling multi-variable constraints. A dynamic model of a tractor–semitrailer combination has been developed, enabling accurate representation of vehicle kinematics and tire forces. Simulation results demonstrate that the inclusion of differential braking significantly reduces articulation angle and yaw rate deviations, preventing instability even at speeds exceeding the critical threshold of 31.04 m/s. Comparative analysis reveals that coordinated braking applied to both tractor and trailer units achieves superior performance over single-unit application, particularly under high-speed conditions. While the findings confirm the effectiveness of MPC-based differential braking for articulated vehicle stability, the study also highlights the current limitation of simulation-based validation and the need for experimental testing to ensure real-world applicability. Future research should explore multi-actuator coordination, including active front steering integration, to further enhance stability and reduce longitudinal speed loss.

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The introduction of drones as part of a future logistics systems could enhance the efficiency of some goods movements but brings with it the prospect of a change to the environment and society. This paper reports on a study which seeks to identify how varied the concerns are amongst both practitioners and citizens and also how different the concerns of the public are from those of practice. The research uses Q-Sort methods to understand the critical variables and clusters of opinions which underlie policy controversies. A Q-Sort was first conducted with 53 professional stakeholders before a common, but reduced size Q-Sort was undertaken with a representative sample across three different local geographies (N = 610) in the UK. The findings suggest many common clusters of viewpoints across the expert and citizen samples, with the key interactions being between the degree of in principle support for drones for delivery and the degree of practical control over their introduction. However, the citizen group was dominated by drone sceptics worried about privacy, terrorism and environmental impacts in a way which was not manifested in the experts. Few differences occurred between places suggesting that simple urban-rural dichotomies do not define groups of opinions.

Abstract: The importance of the infrastructure is associated with the value of the infrastructure, the greater the importance of infrastructure, the greater its value. The concept of the importance of road infrastructure can take on a different value instead of different points of view. For example, roads can be evaluated from an economic, social, political, and military, among others. In 2021, the Lazio Regional Road Authority (ASTRAL) requested assistance from the Research Center for Transport and Logistics (CTL) to develop a composite scoring index (Regional Index, Ri) that would rank the relative importance of ASTRAL–maintained roadway network. The Ri index is expressed numerically between values from 1 to 5 (with 5 representing the highest importance). It includes the following variables: Population density, AADT, road traffic crashes, accessibility to point of interest, maintenance cost, air emissions, and noise pollution. The methodology includes the following steps. First, the variables were selected on the basis of their reliability, measurability, coverage and relevance to the phenomenon to be measured. Then, the data collection and normalization of the variables on a scale of 1 to 5 were carried out. Subsequently, through a multicriteria analysis, the variables were weighted and added. Finally, a sensitivity analysis was performed to evaluate which variables had the most influence on the final output of the formula. The methodology proposed has been implemented on the Region Lazio roadway network in order to obtain the Ri of the road segments.

Abstract: Urban Air Mobility (UAM) and low-altitude drone operations are emerging as a critical component of next-generation urban transportation and logistics systems. As mission volumes increase, operators face growing challenges in coordinating large-scale low-altitude missions, managing heterogeneous operational states, and closing the loop between mission execution and platform-level resource utilization. Existing UAM platforms primarily focus on flight scheduling and monitoring, while lacking systematic mechanisms to model mission lifecycles and their operational value within an integrated platform architecture. This paper presents SkyNetUAM, a low-altitude UAM operations platform that introduces a structured mission lifecycle model to bridge mission planning, execution, and post-mission settlement within a unified system. We propose a hierarchical operational asset model covering individual missions, service packages, and air-corridor time slots, enabling fine-grained tracking of mission states and operational performance. The platform architecture integrates real-time mission scheduling, operational monitoring, and lifecycle state management, allowing mission-level events to drive platform-wide coordination and resource allocation. As an operational extension, the system incorporates a lightweight on-chain persistence mechanism to record mission states and support automated settlement workflows without altering the core operational logic. A prototype implementation demonstrates the end-to-end workflow from mission creation to completion across simulated low-altitude scenarios, and a reproducible 100k-missions/day experiment quantifies approval rate, delay behavior, and latency distributions under congestion and regulatory constraints.

Abstract: This paper proposes a comprehensive framework for integrating Digital Twins (DT) with real-time traffic optimization systems to enhance urban mobility management in Smart Cities. Using the Pobitno Roundabout in Rzeszów as a case study, we established a cali-brated microsimulation model (validated via the GEH statistic) that serves as the core of the proposed Digital Twin. The study goes beyond static scenario analysis by introducing an Adaptive Inflow Metering (AIM) logic designed to interact with IoT sensor data. While traditional geometrical upgrades (e.g., turbo-roundabouts) were analyzed, simulation re-sults revealed that geometrical changes alone—without dynamic control—may fail under peak load conditions (resulting in LOS F). Consequently, the research demonstrates how the DT framework allows for the testing of "Software-in-the-Loop" (SiL) solutions where Python-based algorithms dynamically adjust inflow parameters to prevent gridlock. The findings confirm that combining physical infrastructure changes with digital, real-time optimization algorithms is essential for achieving sustainable "green transport" goals and reducing emissions in congested urban nodes.

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In urban corridors, roundabouts often operate in close proximity to signalized intersections, yet the safety implications of their mutual interaction remain insufficiently explored. This study combines field measurements and VISSIM microsimulation with the Surrogate Safety Assessment Model (SSAM) to analyze roundabout–signalized intersection pair under varying outer radii (12–22 m), spacings (40–160 m), signal red times (17–27 s), and traffic distributions. A multiple linear regression model for predicting the total number of conflicts is developed and partially validated using calibrated real-site models for corridors in Osijek and Poreč, Croatia. Small spacings (40 m) increase the total number of conflicts by 40–60% for small roundabouts (R = 12 m) and 20–40% for larger radii compared with isolated operation. Increasing the outer radius from 12 to 17 m reduces conflicts by up to about 90%, while longer red times further lower conflicts, especially for small roundabouts. The final regression model, based on spacing, red time, and outer radius, explains about 80% of the variance in conflicts and shows good agreement with SSAM estimates within its applicability range, providing a practical tool for safety-oriented design of urban roundabout–signalized intersection corridors thereby contributing to the goals of developing a sustainable transport system in complex urban environment.

Abstract: Growing concerns over greenhouse gas (GHG) emissions have positioned hydrogen fuel cell buses (HFCBs) as a promising alternative for sustainable urban mobility. By elimi-nating tailpipe emissions and enabling significant reductions in well-to-wheel GHG in-tensities when hydrogen is sourced from renewables, HFCBs can contribute to im-proved urban air quality, energy diversification, and alignment with climate goals. De-spite these benefits, large-scale adoption faces challenges related to production costs, hy-drogen infra-structure, and efficiency improvements across the supply chain. Life Cycle Assessment (LCA) provides a valuable framework to assess these trade-offs holistically, capturing en-vironmental, economic, and social dimensions of HFCB deployment. How-ever, incon-sistencies in system boundaries, functional units, and impact categories high-light the need for more standardized and comprehensive methodologies. This paper ex-amines the potential of hydrogen buses by synthesizing evidence from peer-reviewed studies and identifying opportunities for integration into urban fleets. Findings suggest that when combined with robust LCA approaches, hydrogen buses offer a pathway to-ward decar-bonized, cleaner, and more resilient public transport systems. Strategic adop-tion could not only enhance environmental performance but also foster innovation, infra-structure de-velopment, and long-term economic viability, positioning HFCBs as a corner-stone of sus-tainable urban transportation transitions.

Abstract: Connected and autonomous vehicle (CAV) platoons face the dual challenge of maintaining longitudinal formation stability while ensuring lateral safety in dynamic traffic environments, yet existing control approaches often address these objectives in isolation. This paper proposes a hierarchical cooperative control framework that integrates a differential game-based longitudinal controller with a risk potential field-driven model predictive controller (MPC) for lateral motion. At the coordination control layer, a differential game formulation models inter-vehicle interactions, with analytical solutions derived for both open-loop Nash equilibrium under predecessor-following (PF) topology and an estimated Nash equilibrium under two-predecessor-following (TPF) topology. The motion control layer employs a risk potential field model that quantifies collision threats from surrounding obstacles and road boundaries, guiding the MPC to perform real-time trajectory optimization. A comprehensive co-simulation platform integrating MATLAB/Simulink, Prescan, and CarSim validates the proposed framework across three representative scenarios: ramp merging with aggressive cut-in maneuvers, emergency braking by a preceding obstacle vehicle, and multi-lane cooperative obstacle avoidance involving multiple dynamic obstacles. Across all scenarios, the CAV platoon achieves safe obstacle avoidance through autonomous decision-making, with spacing errors converging to zero and smooth velocity adjustments that ensure both formation stability and ride comfort. The results demonstrate that the proposed framework effectively adapts to diverse and complex traffic conditions.

Abstract: Cost-sensitive advanced driver-assistance systems (ADAS) increasingly rely on embedded platforms without discrete GPUs, where power-intensive deep neural networks are often impractical to deploy and difficult to certify for safety-critical functions. At the same time, classical geometry-based lane detection pipelines still struggle under strong backlighting, low-contrast night scenes, and heavy rain. This work revisits geometry-driven lane detection from a sensor-layer perspective and proposes a Binary Line Segment Filter (BLSF) that exploits the structural regularities of lane markings in bird’s-eye-view (BEV) images. The filter is integrated into a three-stage pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and simplified Hough-based sliding-window fitting with RANSAC. On a self-collected dataset of 297 challenging frames (strong backlighting, low-contrast night, heavy rain, and high curvature), the full pipeline improves lane detection robustness over the same implementation without BLSF while maintaining real-time performance on a 2 GHz ARM CPU-only platform. To assess generality, we further evaluate BLSF on the Dazzling Light and Night subsets of the large-scale CULane and LLAMAS benchmarks, where it achieves a consistent 6–7% improvement in F1-score over a line-segment baseline under a fixed pre-processing configuration, along with corresponding gains in IoU. These results demonstrate that explainable, geometry-driven lane feature extraction can deliver competitive robustness under adverse illumination on low-cost, CPU-only embedded hardware, and can serve as a complementary design point to lightweight deep-learning models in cost- and safety-constrained ADAS deployments.

Abstract: In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + illumination” module is constructed. Specifically, a learning-based dehazing method, Learning Hazing to Dehazing, is employed to restore long-range details affected by scattering artifacts. Additionally, HVI-CIDNet is introduced to decouple luminance and chrominance in the Horizontal/Vertical Intensity (HVI) color space, thereby simultaneously enhancing structural fidelity in low-light regions and achieving global brightness consistency. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Furthermore, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment, respectively. Evaluated on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5–2.7 percentage points in core metrics such as mAP@50–90 compared to similar lightweight models (e.g., the DEIM baseline and YOLOv12s), while maintaining low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions.

Abstract: Accurately determining actual energy consumption is essential for guiding technological developments in the transport sector, assessing vehicle development outcomes, and designing effective energy and climate policies. Although laboratory driving cycles such as the WLTP provide standardized benchmarks, they do not reflect the complex interactions between human behavior, environmental conditions, and vehicle dynamics under real-world operating conditions. This article presents an integrated framework for assessing long-term, actual energy carriers consumption in four main vehicle categories: internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), hydrogen fuel cell electric vehicles (H2EVs), and battery electric vehicles (BEVs). The entire discussion here is based on the results of data analysis from natural operation using the so-called vehicle energy footprint. This framework provides a method for determining the average energy carriers consumption for each group of vehicles with the specified drivetrains. This information formed the basis for assessing the total energy demand for the operation of the analyzed vehicle types in normal operation. The simulations show that among mid-range passenger vehicles, ICEVs are the most energy-intensive in normal operation, followed by H2EVs, HEVs, and BEVs the least. The study highlights the methodological challenges and implications of accurately quantifying energy consumption. The presented method for assessing energy demand in vehicle operation can be useful for manufacturers, consumers, fleet operators, and policymakers, particularly in terms of energy efficiency, emission reduction, and public health protection.

Abstract: Against the backdrop of global energy transition and carbon emission reduction, the scientific siting of electric vehicle (EV) charging stations has become a key issue constraining the sustainable development of the industry. To address the common shortcomings of existing research, such as single-objective bias and the tendency of traditional optimization algorithms to fall into local optima, this paper proposes a multi-objective siting optimization method that couples an improved NSGA-II algorithm with an improved TOPSIS model. First, a charging station location model is established with the dual objectives of minimizing total operator costs and maximizing user satisfaction, where user satisfaction comprehensively incorporates factors such as charging distance and queuing time. Second, at the algorithmic level, chaotic mapping, opposition-based learning, and adaptive crossover–mutation operators are introduced to enhance global search capability and solution diversity. Then, an improved entropy-weighted TOPSIS model is used to select the optimal compromise solution from the Pareto set, achieving objective weight determination and stabilized ranking outcomes. Finally, simulation experiments show that the proposed method outperforms the standard NSGA-II algorithm in both operating cost reduction and user satisfaction improvement, while also exhibiting superior performance in hypervolume (HV), inverted generational distance (IGD), and diversity metrics. The results verify that the integrated improved NSGA-II–TOPSIS framework provides an efficient, scientific, and interpretable decision-support tool for the planning of EV charging infrastructure.

Abstract: Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings.