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.

Abstract: Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses North Dakota, USA as a representative case alongside insights from similar rural regions worldwide. The study conducts a systematic review of 82 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions.

Abstract: Speed prediction is fundamental to optimizing energy management strategies. Common evaluation metrics such as RMSE and MAE focus primarily on the numerical deviation between predicted and actual speeds. However, when applied to hybrid vehicle energy management strategy optimization, speed prediction models based on these metrics show a random deviation between energy consumption results and the theoretical optimal, indicating that these metrics are not effective in this application domain. To explore a more effective method for evaluating the practical application of speed prediction curves, this study uses multiple metrics to assess numerous speed prediction curves and analyses the correlation between each metric and the deviation from the optimal energy consumption during energy management strategy optimization. The results show that considering acceleration is more aligned with the needs of energy management strategy optimization than merely evaluating the proximity of speed values. Specifically, the standard deviation of the acceleration time ratio deviation performs better than traditional metrics like RMSE and MAE in distinguishing the effectiveness of speed prediction curves. The smaller the standard deviation of the acceleration time ratio deviation between the predicted and actual speed curves, the closer the energy consumption results of energy management based on the predicted speed curve are to the theoretical optimal.

Abstract: Enhancing public transport accessibility (PTA) is an effective strategy for creating sustainable and liveable urban environments. However, calculating the access index from predefined values for walking and waiting times requires further investigation to assess whether available public transport services sufficiently meet traveller needs. This study evaluates PTA to examine additional walking times from home, work, and other locations to various transport modes (bus, train, underground, and tram), and waiting times during different peak periods (morning, inter, evening, and night) as a case study in London. The findings reveal that walking times from home locations exceed PTAL thresholds, with median values surpassing 8 minutes at bus stops and 12 minutes at rail stations, while evening peaks result in higher waiting times than morning periods used in PTAL calculations. The data-driven access index from mobile app data, with reference data from TFL, is further examined for spatial patterns of over- and underestimation areas and to demonstrate how incorporating dynamic spatial and temporal attributes into PTAL offers valuable insights for improving public transport accessibility in urban environments.

Abstract: Shopping centers are significant traffic generators that influence traffic conditions on adjacent road networks. This study evaluates the impact of the Big Fashion shopping center in Novi Sad, Serbia, on two nearby signalized intersections and examines the effectiveness of reconstructing one of them into a turbo roundabout. Traffic counts conducted before (2015) and after (2020) the opening of the shopping center indicate notable increases in peak-hour traffic volumes: 8.38% at the Bulevar cara Lazara–Bulevar oslobođenja intersection and 6.96% at the Bulevar cara Lazara–Fruškogorska Street intersection. The increased demand contributed to higher delays and deteriorated levels of service. In 2023, the latter intersection was reconstructed into the region’s first turbo roundabout. Microsimulation (Synchro 11) using pre-reconstruction volumes revealed substantial operational improvements, including a reduction in the most critical left-turn delay from 165.5 s/veh to 11.9 s/veh. The results confirm that turbo roundabouts can effectively mitigate congestion and enhance intersection performance in urban environments with high directional turning movements.

Abstract: Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Firenze and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still limited number of studies on free-floating and electric bike-sharing systems, the objective of this work is to quantify the performance of electric bikes and E-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Results show that E-bikes are from 22 to 26\% faster on average with respect to muscular bikes, extending trip range in Bologna, but not in Firenze. Electric modes attract more users than traditional bikes, E-bikes have from 40 to 128\% higher daily turnover in Bologna and Firenze, and E-scooters from 33 to 62\% higher in Firenze with respect to traditional bikes. Overall, turnover is fairly low, with less than 2 trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode, trip purpose, and user type are investigated and compared. The results aim to support planners and operators in designing and managing more efficient and user-oriented services.

Abstract: As the population arises everyday, road accidents also increase. More than half of all road traffic deaths and injuries involve vulnerable road users, such as pedestrians, cyclists and motorcyclists and their passengers. One of the many reasons is the lack of road lighting. Without sufficient road lighting, it is necessary to provide some means to guide the drivers along dark roads. Road studs are among the most important devices that help in preventing cars from running off the road or their lanes and making our roads safer. They reflect the light from a car's headlights to allow the driver to observe the curves and corners of the road from a distance. Even in the dark, the driver is easily able to see the road alignments, ends, and corners of the road and judge where to turn, what lane of the road to adopt and in turn, drive safely. This makes studs extremely useful on poorly lit roads. They provide effective night guidance even under adverse weather conditions. The aim of this study is to provide better understanding and to get an overview of the developments of road studs through time. The data collection has been carried out through the search engines Google Scholar and Google Search. Therefore limitations, selections and exclusions have been made by the authors. This study focuses only on the variables stated above, the effectiveness of road studs to road safety and visibility . The method for answering the research questions was conducted by systematic literature search and review.

Abstract: In this study, a traffic flow analysis was conducted using a multi-agent simulation to evaluate the effect of reducing CO2 emissions through the penetration of connected autonomous vehicles (CAVs) equipped with vehicle-to-vehicle (V2V) communication functionality. By exchanging local information among CAVs, alleviating traffic congestion without the need for cooperative vehicle control, and reducing CO2 emissions by up to 20% is possible. In addition, we analysed the impact of CAV penetration rate and communication range on CO2 emissions, demonstrating that the reduction effect in CO2 emissions tends to appear more prominently once the penetration of CAVs reaches a certain threshold. In particular, when the communication range is narrow, a significantly high penetration rate is required before the benefits of CO2 reduction become evident. Furthermore, a wider communication range is not necessarily more desirable. These findings suggest that limiting the communication range may enable more efficient use of road traffic information. Although each CAV acts solely based on its own self-interest, route selection based on local information leads to the emergence of swarm intelligence, resulting in improved efficiency at the collective level.

Abstract: Advanced Air Mobility (AAM) has emerged as a transformative solution for time-critical healthcare logistics. It promises to overcome ground transport limitations in emergency response, organ transport, and medical supply distribution. Despite rapid technological progress in electric vertical takeoff and landing (eVTOL) systems, automation, and airspace management, scholarly work on AAM in healthcare remains fragmented across disciplines. Therefore, a systematic synthesis is needed to consolidate knowledge, evaluate methodological maturity, and guide future research and policy directions. This review examines how AAM has been conceptualized, modeled, and applied within healthcare contexts. It addresses three questions: (1) What bibliometric patterns characterize research on healthcare-focused AAM? (2) What technical, regulatory, and societal barriers constrain its integration? (3) How do current approaches optimize time-critical missions? The study conducts a systematic review of 121 peer-reviewed publications (2015–2025) using five major databases. The analysis combines bibliometric mapping, thematic synthesis, and semantic network visualization to identify conceptual patterns, research clusters, and interdisciplinary linkages. Six dominant themes emerge: design, logistics, airspace, acceptance, regulation, and economics. These themes reflect the multidimensional evolution of AAM research. Findings show rapid growth since 2020, driven by advances in automation and electrification. However, the study also reveals persistent fragmentation between engineering-driven feasibility studies and policy or healthcare-oriented research. The field is transitioning from technical prototyping toward integrated frameworks that address safety, governance, and healthcare system alignment. This review contributes a unified socio-technical perspective on AAM for healthcare. It offers conceptual clarity and identifies priority directions for empirical validation, equity-focused deployment, and regulatory harmonization. The study provides actionable insights for researchers, practitioners, and policymakers seeking to translate aerial mobility innovations into resilient, equitable healthcare delivery systems.

Abstract: This paper addresses the distributed control problem for high-speed trains subject to unknown actuator faults, input saturation, and parametric uncertainties—including structural variations among actuators, external resistance, and inter-carriage forces. A carriage-scale distributed adaptive fault-tolerant controller is designed based on a multi-agent dynamics model. The controller incorporates an adaptive law to estimate uncertain parameters and a second-order auxiliary system to mitigate the effects of input saturation on closed-loop stability. Simulation results demonstrate the controller’s effectiveness in achieving accurate dual-closed-loop tracking of both speed and position under actuator fault and input saturation conditions.

Abstract: The present article relates to the description of phenomenological relations of amorphous material behavior within the framework of viscoelasticity and elastic-viscoplasticity theory, as well as to the creation of its digital analogue. Ultra-high-molecular-weight polyethylene (UHMWPE) is considered in the study. The model is based on the results of a series of experimental studies. Free compression of cylindrical specimens in a wide range of temperatures [-40; +80] °C and strain rates [0.1; 4] mm/min was performed. Cylindrical specimens were also used to determine the thermal expansion coefficient of the material. Dynamic mechanical analysis (DMA) was performed on rectangular specimens using a three-point bending configuration. Maxwell and Anand models were used to describe the material behavior. In the framework of the study, a temperature dependence of a number of parameters was established. This influenced the mathematical formulation of the Anand model, which was adapted by introducing the temperature dependence of the activation energy, the initial deformation resistance, and the strain rate sensitivity coefficient. Testing of the material models was carried out in the process of analyzing the deformation of a spherical bridge bearing by a multi-cycle periodic load. The load corresponds to the movement of a train on a bridge structure, without taking into account vibrations. It is shown that the viscoelastic model does not describe the behavior of the material accurately enough for quantitative analysis of the stress-strain state of the structure. It is necessary to move on to more complex models of material behavior to minimize the discrepancy between the digital analogue and the real structure. It is established that taking into account plastic deformation while describing UHMWPE allows this to be done.

Abstract: To address the issues of inefficiency and high costs in obtaining data on the residential distribution of public transport passengers at present, this paper proposes an approach of "estimating the residential distribution of public transport passengers based on characteristics such as housing prices of residential Point of Interest (POI) and the convenience of public transport and its stops". First, from two aspects—public transport travel and the selection of public transport stops—eight influencing factors for the selection of public transport stops during travel are identified. Based on these factors, a regression model for the number of public transport passengers from residential POI to their corresponding stops is constructed, through which the number of passengers traveling from each residential POI to all accessible public transport stops is obtained. This number is then used as a weight to allocate the actual passenger flow of each public transport stop to respective residential POI, thereby realizing the estimation of the residential distribution of public transport passengers. Furthermore, this approach enables the estimation of the proportion of trips made from residential areas to specific public transport stops and the overall proportion of public transport trips among all travel modes from residential areas. The proposed estimation method is verified and evaluated using Shenzhen as a case study. The results show that the Mean Absolute Percentage Error (MAPE) of the proposed model is 72.024%, which outperforms the XGBoost model that uses the same set of characteristics.

Abstract: Aiming at the problems of identification and utilization efficiency evaluation of urban high-intensity development areas, and based on the general trend of urban spatial development from points to areas, this study proposes a method for identifying high-intensity development areas based on seed grid, which involves area growth, merging and segmentation, by drawing on the region growing method in image recognition. In this method, the Getis-Ord Gi* statistic of grid floor area ratio is used as the criterion to screen seed grids, and the region identification results are evaluated from the rationality of the geometric shape of the area and the independence of spatial relations. Furthermore, using gridded permanent population density, digital brightness of urban night-time lights, and point of interest (POI) density data, the utilization efficiency of high-intensity development areas is evaluated from the perspectives of population carrying capacity and industrial agglomeration. Finally, Shenzhen City is taken as an example to verify the proposed identification and utilization efficiency evaluation methods for high-intensity development areas. The results show that the proposed identification method has a good effect, and the identified high-intensity development areas have reasonable geometric shapes and independent spatial relations; in terms of utilization efficiency, the overall utilization efficiency of high-intensity development areas in Shenzhen is relatively low, especially the areas at the edge of the central district and non-central districts fail to attract population activities matching the intensity of construction.

Abstract: This comprehensive review examines the works of reinforcement learning (RL) in ship collision avoidance (SCA) from 2014 to the present, analyzing the methods designed for both single-agent and multi-agent collaborative paradigms. While prior research has demonstrated RL's advantages in environmental adaptability, autonomous decision-making, and online optimization over traditional control methods, this study systematically addresses the algorithmic improvements, implementation challenges, and functional roles of RL techniques in SCA, such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Multi-Agent Reinforcement Learning (MARL). It also highlights how these technologies address critical challenges in SCA, including dynamic obstacle avoidance, compliance with Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and coordination in dense traffic scenarios, while underscoring persistent limitations such as idealized assumptions, scalability issues, and robustness in uncertain environments. Contributions include a structured analysis of recent technological evolution, and a Large Language Model (LLM) based hierarchical architecture integrating perception, communication, decision-making, and execution layers for future SCA systems, which prioritizes the development of scalable, adaptive frameworks that ensure robust and compliant autonomous navigation in complex, real-world maritime environments.