Search Results (660)

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article

Pavement cracks are a critical indicator for assessing structural health and forecasting deterioration trends. Accurate and automated crack classification is of paramount importance for the intelligent maintenance of road structures. Inspired by the principles of symmetry—which often lead to robust and efficient structures in both nature and engineering—this paper proposes ASPCCNet, a lightweight network that embeds these principles into its core design. The network centers on a novel building block, AugShuffleBlock, which embodies a symmetry-informed design through the integration of Partial Convolution (PConv), a tunable channel splitting mechanism (AugShuffle), and the Channel Prior Convolutional Attention (CPCA). This design achieves efficient feature extraction and fusion with minimal computational overhead. Experimental results on the public RCCD dataset demonstrate that ASPCCNet significantly outperforms mainstream lightweight models, achieving an F1-score of 0.816, which is 6.4% to 10.9% higher than other mainstream models, with only 0.294 M parameters and 48.68 MFLOPs. This work showcases how a symmetry-guided design philosophy can be leveraged to achieve a superior balance between accuracy and efficiency for real-time edge deployment. Full article

Traffic accidents pose unpredictable and severe social and economic challenges. Rapid and accurate accident detection, along with reliable severity classification, is essential for timely emergency response and improved road safety. This study proposes DSGF-YOLO, an enhanced deep learning framework based on the YOLOv13 architecture, developed for automated road accident detection and severity classification. The proposed methodology integrates two novel components: the DS-C3K2-FasterNet-Block module, which enhances local feature extraction and computational efficiency, and the Grouped Channel-Wise Self-Attention (G-CSA) module, which strengthens global context modeling and small-object perception. Comprehensive experiments on a diverse traffic accident dataset validate the effectiveness of the proposed framework. The results show that DSGF-YOLO achieves higher precision, recall, and mean average precision than state-of-the-art models such as Faster R-CNN, DETR, and other YOLO variants, while maintaining real-time performance. These findings highlight its potential for intelligent transportation systems and real-world accident monitoring applications. Full article

As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of small passenger cars generally ranges between 405 mm and 485 mm. However, the lower edge height of the current Chinese Class A W-beam guardrail is 444 mm above the ground, which leads to a high risk of “underride” during collisions, resulting in elevated occupant injury risks. To address this issue, this paper proposes an optimized guardrail structure composed of a double W-beam and a C-type beam, aiming to reduce the underride risk for small passenger cars while accommodating multi-vehicle protection needs. In this design, the double W-beam is installed at a height of 560 mm and the C-type beam at 850 mm, connected to circular posts using a regular hexagonal anti-obstruction block. The beam thickness is uniformly 3 mm, while the thickness of other components is 4 mm. To systematically evaluate the impact of material strength on both safety performance and cost, two material configurations are proposed: Scheme 1 uses Q235 carbon steel for all components; Scheme 2 reduces the thickness of the C-type beam to 2.5 mm and employs Q355 high-strength low-alloy steel, with the thickness of the connected anti-obstruction block reduced to 3.5 mm, while the other components retain Q235 steel and unchanged structural dimensions. Using finite element simulation, collisions involving small passenger cars, medium trucks, and buses are simulated, and performance comparisons are conducted based on vehicle trajectory and guardrail deformation. For the small passenger car scenario, risk quantification indicators—Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV), and Post-impact Head Deceleration (PHD)—are introduced to assess occupant injury. The results demonstrate that Scheme 2 not only meets the required protection level but also significantly reduces occupant risk for small passenger cars, lowering the injury rating from Class C to Class B. Moreover, the overall structural mass is reduced by approximately 1407 kg per kilometer, with material costs decreased by about RMB 10,129, demonstrating favorable economic efficiency. The proposed structural optimization not only effectively mitigates small car underride and improves multi-vehicle protection performance but also provides the industry with a novel guardrail geometric design directly applicable to engineering practice. The technical approach of enhancing material strength and reducing component thickness also offers a feasible reference for lightweight design, material savings, and cost optimization of guardrail systems, contributing significantly to improving the safety and sustainability of road transportation infrastructure. Full article

Electric vehicles, or EVs, have taken the spotlight in recent years in attempts to minimize the negative environmental effects associated with conventional modes of transportation. The location of charging infrastructure is an significant roadblock to promoting EV adoption; it demands careful planning in order to ensure the sustainability of EV use. Models like the Flow Capturing Location Model (FCLM) and Flow Refueling Location Model (FRLM) address this by considering operational constraints, system features, and uncertainties to provide effective solutions. In this study, Using MATLAB, R2020a the FCLM and FRLM were applied in Al-Karkh, Baghdad. When combined, the results revealed three key outcomes: identification of the nodes most frequently connected by traffic flows, with the shortest path method used to exclude paths that could not be utilized due to vehicle range limitations, and determination of the best nodes located along the shortest feasible routes dependent on the number of stations that the models chose. Full article

In the context of global climate change and rapid urbanization, the Urban Heat Island (UHI) effect has become a pressing environmental and public health concern, particularly in semiarid regions. This study evaluates the microclimatic performance of various urban design strategies aimed at enhancing thermal comfort along a densely built-up street in Van, a medium-sized city located in Turkey’s semiarid climate zone. Using ENVI-met 5.7.2, nine alternative scenarios were simulated, incorporating different configurations of vegetation cover (0%, 25%, 50%, 75%), ground surface materials, and green roof applications (0%, 25%, 50%, 75%). Physiological Equivalent Temperature (PET) and other thermal comfort indicators were assessed at multiple time intervals on the hottest summer day. Results indicate that increasing vegetation cover substantially reduces PET values, with a maximum reduction of 3.0 °C observed in the 75% vegetation scenario. While the scenario with no vegetation but light-colored pavements achieved a 1.8 °C reduction in air temperature at 2:00 p.m., the maximum PET value remained unchanged. Conversely, using dark-colored asphalt decreased the average air temperature by 1 °C and improved the thermal comfort level by reducing the PET by 0.4 °C compared to a non-vegetated scenario. The scenario with the highest overall greenery led to a 2.9 °C drop in air temperature and a 12.8 °C reduction in average PET at 2:00 p.m. compared to other scenarios. The study provides evidence-based recommendations for human-centered urban planning and advocates for the integration of microclimate simulation tools in the early stages of urban development. Full article

Recent urbanization and climate change have altered the hydrologic characteristics of road surfaces, intensifying urban flooding and associated damage. This study focuses on permeable block pavements, a key LID technology for sustainable urban development, particularly with respect to their application for sidewalks. To quantitatively evaluate the permeability performance of the pavement system and clarify the infiltration mechanisms associated with different combinations of upper and lower aggregates, an integrated permeability testing apparatus was developed. Based on small-scale testing, the coefficient of permeability was quantitatively evaluated according to the gradation characteristics of the base aggregates. The results indicated that as the fine content increased and the coefficient of uniformity (Cu) decreased, the permeability coefficient also decreased. Furthermore, when blocks were added above the base layer, the permeability coefficient showed a decreasing trend, suggesting that even if the upper layers have higher intrinsic permeability, the hydraulic conductivity of the lower layers predominantly governs the overall permeability of the system. Using large-scale rainfall simulation, the permeability was evaluated under a rainfall intensity of 88.2 mm/h. The base-only configuration exhibited the highest storage capacity (approximately 36%), while adding the bedding layer and block pavement reduced the initial outflow time by up to 33 s. Full article

As urban disasters intensify, the relationship between urban spatial form and disaster risk is increasingly important. Different spatial configurations reflect varying levels of resilience to disasters. However existing research offers limited quantitative evidence linking spatial form indicators and disaster risk indices. This study addresses this gap by developing a quantifiable, city-scale framework to analyze the form–risk relationship across 32 Chinese cities. Urban spatial form is quantified using fractal dimension to measure boundary complexity and compactness to assess internal structure, supplemented by a diagrammatic classification of urban patterns. A comprehensive disaster risk index is developed based on four dimensions: hazards, exposure, vulnerability, and resilience. Regression analysis is then applied to quantify the direction and magnitude of correlations between spatial-form indicators and the comprehensive risk index. The results reveal three major findings: (1) Disaster risk increases with fractal dimension, indicating that cities with more complex and irregular boundaries tend to be more vulnerable. In contrast, compactness has no statistically significant effect on disaster risk. (2) Spatial patterns are strongly associated with risk levels: cluster-type and block-type cities generally experience lower risks than radial-type and constellation-type cities. (3) City size and geography also influence risk, as larger cities typically exhibit lower risks, whereas southern cities face higher risks than those in northern regions. These results highlight the critical role of urban spatial structure in shaping disaster resilience. Managing boundary complexity, fostering polycentric and block-based spatial layouts, and improving road-network redundancy can effectively enhance urban adaptive capacity. These insights provide theoretical foundations and practical guidance for resilience-oriented spatial optimization and disaster-risk reduction in vulnerable cities. Full article

The three-leg 50-Years Roundabout at King Abdulaziz University (KAU) is known for its vibrance and important location as it is located at the center of several major buildings and hospitals. In recent years, the roundabout is witnessing a huge demand that influences the university road networks’ level of service, “LOS”, which in return, has negative impacts on students and faculties in terms of delay and travel time. Several treatments can be implemented along the roundabout. One of those treatments is applying restrictions during morning peak hours such as blocking and restricting specific lanes. This treatment has the advantage of reducing conflict points that cause sudden and frequent stops at the roundabout; as a result, delay and congestion occur. By reducing conflict points, traffic flow can be improved, in addition to enhancing safety and promoting sustainability. This paper examines the base condition of the 50-Years Roundabout in terms of traffic flow, LOS, delay, capacity, and toxic emissions, and proposes traffic system management (TSM) strategies through applying restricted and designated lanes to improve traffic condition. The study employs PTV Vissim, SIDRA Intersection, and Surrogate Safety Assessment Model “SSAM” to examine the base and proposed conditions. The results show a significant improvement through the reduction in conflict points, so that reflects the positive impacts on sustainability, congestion, delay, travel time, LOS, and overall toxic emissions. Full article

The global spread of carbapenem-resistant Acinetobacter baumannii (CRAB) poses a severe public health threat, driving growing interest in phage-based precision antibacterial strategies. This systematic review synthesizes recent advances in the field of A. baumannii phage. Modern taxonomy, based on whole-genome phylogeny, has reclassified the majority of A. baumannii phages into the class Caudoviricetes, revealing distinct evolutionary clades that correlate with host tropism and biological properties, superseding the traditional morphological families (Myoviridae, Siphoviridae, Podoviridae). To overcome limitations of natural phage therapy, such as narrow host range, cocktail therapies (ex vivo resistance mutation rates < 5%) and phage-antibiotic synergism (enabling antibiotic efficacy at 1/4 minimum inhibitory concentration) have significantly enhanced antibacterial efficacy. Preclinical models demonstrate that phage therapy efficiently clears pathogens in pneumonia models and promotes the healing of burn wounds and diabetic ulcers via immunomodulatory mechanisms. Technical optimizations include nebulized inhalation delivery achieving 42% alveolar deposition, and thermosensitive hydrogels enabling sustained release over 72 h. Genetic engineering approaches, such as host range expansion through tail fiber recombination and CRISPR/Cas-mediated elimination of lysogeny, show promise. However, the genetic stability of engineered phages requires further validation. Current challenges remain, including limited host spectrum, the absence of clinical translation standards, and lagging regulatory frameworks. Future efforts must integrate metagenomic mining and synthetic biology strategies to establish a precision medicine framework encompassing resistance monitoring and personalized phage formulation, offering innovative solutions against CRAB infections. Full article

This study, based on the maintenance engineering of regular national and provincial highways in Shanxi Province, aims to achieve refined maintenance of aging asphalt pavements under heavy-duty traffic conditions. Lightweight inspection equipment was used to perform frequent distress collection on the study sections, and for the first time, the EPCI (Economic Pavement Surface Condition Index, which can quickly improve the overall condition level of the pavement by identifying simple two-dimensional diseases such as transverse and longitudinal joints and tortoise net cracks, and low-cost maintenance measures can be carried out through the detection data, which does not include diseases such as subsidence, which are more complex and costly.) is proposed to assess pavement distress conditions. The study conducted six high-frequency data collections over one year on the designated road sections. EPCI evaluations were carried out on each lane in different driving directions, summarizing eight types of pavement distress, including alligator cracking, block cracking, longitudinal and transverse cracking, potholes, longitudinal and transverse crack repairs, and block repairs. The development trends of EPCI and the distribution of pavement distress were analyzed. By comparing EPCI data, it was found that EPCI values in the driving lane fluctuated more stably than those in the overtaking and slow lanes, which was attributed to differences in maintenance intensity. The overall PCI data of the pavement during the COVID-19 pandemic showed that reduced maintenance activities are more conducive to analyzing the pavement’s deterioration patterns. By examining the distressed area in each lane over time, it was observed that the slow lane had the highest distribution of alligator and block cracking, while longitudinal and transverse cracking were most prevalent in the overtaking and driving lanes. Further analysis of the relationship between distressed area and EPCI suggests that controlling the distressed area to around 500 square meters per kilometer per lane can maintain the EPCI score at approximately 80. This level of maintenance is considered the most economical while ensuring satisfactory pavement performance. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

Rockfalls represent a widespread natural hazard that threatens infrastructures and settlements in mountainous and coastal areas. In Baunei (Sardinia, Italy), steep carbonate cliffs above the SS125 road frequently generate block detachments that endanger traffic and nearby urban areas. The present work adopts a quantitative risk assessment framework, consistent with the Swiss PLANAT guidelines, to evaluate the protective effectiveness of direct-protection forests in combination with engineered barriers. The framework integrates the key components of hazard, exposure, and vulnerability to quantify direct-impact risk and associated economic loss. Using Rockyfor3D simulations, three scenarios were analysed: bare slope, forest only, and forest plus protective works. The results demonstrate that vegetation markedly reduces both runout distance and kinetic energy of falling blocks, halving the direct-impact risk compared to bare-slope conditions. The addition of barriers further decreases residual exposure, with most trajectories intercepted and remaining impacts limited to low-energy classes. Monetised risk estimates confirm an 84% reduction with forest cover alone and near-complete mitigation when complemented by fences, except in short discontinuous segments. The proposed approach offers a replicable and cost-effective tool for rockfall risk management and sustainable protection forest planning in Mediterranean settings. Full article

Ports often act as bottlenecks in import cycles. Yet feasibility assessments of port–dry port systems still privilege distance/location over the process dynamics that generate time and cost. This paper proposes a process-centred feasibility method that links process analysis and reengineering, operational cycle design, and economics. We map AS-IS/TO-BE activities, design shuttle-train operations between port and dry port, and estimate costs via a time-driven approach aligned with TDABC. The method is applied to the Port of Genoa—Rivalta Scrivia dry port. Consolidating containers to a single inland destination and relocating customs clearance to the dry port via Fast Corridor stabilises yard operations, removes re-handling in the port-to-dry-port cycle, reduces dwell, and improves train formation and reliability. Under these conditions, the process-based configuration becomes time- and cost-competitive vis-à-vis all-road and conventional block-train baselines for relevant inland destinations. This paper reframes feasibility from a distance-based view to a process, cycle, and economics logic, offering managerial guidance and policy implications on regulatory enablers that unlock dry-port benefits. Full article

Urban recreational spaces (URSs) are pivotal for enhancing resident well-being, making the accurate assessment of public perceptions crucial for quality optimization. Compared to traditional surveys, social media data provide a scalable means for multi-dimensional perception assessment. However, existing studies predominantly rely on single-modal data, which limits the comprehensive capturing of complex perceptions and lacks interpretability. To address these gaps, this study employs cutting-edge large vision–language models (LVLMs) and develops an interpretable model, Qwen2.5-VL-7B-SFT, through supervised fine-tuning on a manually annotated dataset. The model integrates visual-linguistic features to assess four perceptual dimensions of URSs: esthetics, attractiveness, cultural significance, and restorativeness. Crucially, we generate textual evidence for our judgments by identifying the key spatial elements and emotional characteristics associated with specific perceptions. By integrating multi-source built environment data with Optuna-optimized machine learning and SHAP analysis, we further decipher the nonlinear relationships between built environment variables and perceptual outcomes. The results are as follows: (1) Interpretable LVLMs are highly effective for urban spatial perception research. (2) URSs within Beijing’s Third Ring Road fall into four typologies, historical heritage, commercial entertainment, ecological-natural, and cultural spaces, with significant correlations observed between physical elements and emotional responses. (3) Historical heritage accessibility and POI density are identified as key predictors of public perception. Positive perception significantly improves when a block’s POI functional density exceeds 4000 units/km² or when its 500 m radius encompasses more than four historical heritage sites. Our methodology enables precise quantification of multidimensional URS perceptions, links built environment elements to perceptual mechanisms, and provides actionable insights for urban planning. Full article