Search Results (93)

Current Automated Vehicle (AV) technologies still face challenges in operating safely across diverse road environments, as existing infrastructure is not yet fully adapted to AV-specific requirements. While many previous studies have relied on simulations, real-world data is crucial for accurately assessing AV safety and understanding the impact of road characteristics. To address this gap, this study analyzes human-driven vehicle (HDV) crashes and AV failures using machine learning and explainable AI (XAI), providing insights into how road design can be improved to facilitate AV integration into existing infrastructure. Using XGBoost-based frequency modeling, the study achieved accuracy ranging from 0.6389 to 0.9770, depending on the specific model. The findings indicate that road geometry and traffic characteristics play a significant role in road safety, while the impact of road infrastructure varies across different road classifications. In particular, traffic characteristics were identified as key contributors to HDV crashes, whereas road geometry was the most critical factor in AV failures. By leveraging real-world AV failure data, this study overcomes the limitations of simulation-based research, improving the reliability of safety assessments. It provides a comprehensive evaluation of road safety across different road types and traffic flow conditions while simultaneously analyzing HDV crashes and AV failures. The findings offer critical insights into the challenges of mixed-traffic environments, where AVs and HDVs must coexist, highlighting the need for adaptive road design and infrastructure strategies to enhance safety for all road users. Full article

In recent decades, road transport has become one of the dominant factors shaping environmental conditions, with both beneficial and adverse consequences. While transport infrastructure facilitates access to essential services and supports societal well-being, vehicular emissions remain a major source of air quality degradation. Among the pollutants released, nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) are of particular concern due to their adverse health effects, especially in densely trafficked urban areas. Pollutant levels are determined not only by traffic intensity but also by external influences such as meteorological conditions and roadway design. This study examines how different intersection configurations affect ambient concentrations of PM₁₀, PM_2.5, and NO₂. Field monitoring and dispersion modeling were carried out for a three-arm intersection and a roundabout. NO₂ concentrations were quantified using a reference chemiluminescence method, while PM₁₀ and PM_2.5 were measured with an optical aerosol spectrometer. Traffic flow characteristics associated with each intersection geometry were simulated in PTV Vissim, and pollutant dispersion patterns were subsequently analyzed using the CadnaA modeling environment. Field measurements revealed lower PM concentrations (reduction in PM₁₀, PM_2.5–10 and PM_2.5 concentration—30.1%, 45.1% and 22.8%) and higher NO₂ concentrations (increase in NO₂ concentration—143.3%) at the roundabout. Full article

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development. Full article

To improve the mechanical stability and service durability of solar road structures, this study systematically investigates the mechanical response characteristics of photovoltaic panels with different geometric shapes—including triangles, rectangles, squares, regular pentagons, and regular hexagons—under consistent boundary and loading conditions using the discrete element method (DEM). All panels have a uniform thickness of 10 cm and equivalent surface areas to ensure shape comparability. Side lengths vary among the shapes: square panels with sides of 0.707 m, 1.0 m, and 1.5 m; triangle 1.155 m; rectangle (aspect ratio 1:2) 0.707 m; pentagon 1.175 m; and hexagon 0.577 m. Results show that panel geometry significantly influences stress distribution and deformation behavior. Although triangular panels exhibit higher ultimate bearing capacity and failure energy, they suffer from severe stress concentration and low stiffness. Regular hexagonal panels, due to their geometric symmetry, enable more uniform stress and displacement distributions, offering better stability and crack resistance. Size effect analysis reveals that larger panels improve load-bearing and energy dissipation capacity but exacerbate edge stress concentration and reduce overall stiffness, leading to more pronounced “thinning” deformation and premature failure. Failure mode analysis further indicates that shape governs crack initiation and path, while size determines crack propagation rate and failure extent—revealing a coupled shape–size mechanical mechanism. Regarding assembly, honeycomb arrangements demonstrate superior mechanical performance due to higher compactness and better load-sharing characteristics. The study ultimately recommends the use of small-sized regular hexagonal units and optimized splicing structures to balance strength, stiffness, and durability. These findings provide theoretical guidance and parameter references for the structural design of solar roads. Full article

This paper presents a review of explosion mitigation techniques for road tunnels, with a focus on scenarios involving high-pressure hydrogen tank rupture under fire conditions. Both passive and active strategies are considered—including structural configurations (e.g., tunnel branching, vent openings, right-angle bends) and protective systems (e.g., drop-down perforated plates, high-performance fibre-reinforced cementitious composite (HPFRCC) panels)—to reduce blast impact on tunnel occupants and structures. The review highlights that while measures such as blast walls or energy-absorbing barriers can significantly attenuate blast pressures, an integrated approach addressing both blast load reduction and structural resilience is essential. This paper outlines how coupled computational fluid dynamics–finite element method (CFD–FEM) simulations can evaluate these mitigation methods, and we discuss design considerations (e.g., optimising barrier placement and tunnel geometry) for enhanced safety. The findings provide guidance for designing safer hydrogen vehicle tunnels, and they identify gaps for future research, including the need for experimental validation of combined CFD–FEM models in hydrogen fire–explosion scenarios. Full article

Robust visual perception and geometric alignment are crucial for intelligent automation in various domains, such as industrial processes and infrastructure monitoring. Accurately aligning structured visual elements, such as floor markings or road-marking templates, is essential for tasks like automated guidance, verification, and condition assessment. However, traditional feature-based methods struggle with templates that feature simple geometries and lack rich textures, making reliable feature matching and alignment difficult, even under controlled conditions. To address this, we propose GeoTemplateKPNet, a novel self-supervised deep-learning framework, built upon Convolutional Neural Networks (CNNs), designed to learn robust, geometrically consistent keypoints specifically in synthetic template images. The model is trained exclusively in a synthetic template dataset by enforcing equivariance to geometric transformations and utilizing self-supervised losses, including inside mask loss, peakiness loss, repulsion loss, and keypoint-driven image reprojection loss, thereby eliminating the need for manual keypoint annotations. We evaluate the method in a synthetic template test set, using metrics such as a keypoint-matching comparison, the Inside Mask Rate (IMR), and the Alignment Reconstruction Error (ARE). The results demonstrate that GeoTemplateKPNet successfully learns to predict meaningful keypoints on template structures, enabling accurate alignment between templates and their transformed counterparts. Ablation studies reveal that the number of keypoints (K) impacts the performance, with K = 3 providing the most suitable balance for the overall alignment accuracy, although the performance varies across different template geometries. GeoTemplateKPNet offers a foundational self-supervised solution for the robust geometric analysis of templates, which is crucial for downstream alignment tasks and applications. Full article

Landslide prevention is crucial, particularly for protecting roads and infrastructure in rock landslide-prone areas. This global issue has garnered significant attention from researchers worldwide. This study addresses landslide prevention by modeling the factor of safety (FoS) for slope stability through the Geological Strength Index (GSI), limit equilibrium method (LEM), and finite element method (FEM). A GSI analysis was conducted using RocLab software version 1.0, and slope modeling was performed using RocScience SLIDE version 6.0 and RS2 version 11. The results revealed various cohesion and friction angles across six slopes, with Slope 5 exhibiting the highest FoS values (up to 3.27 with the FEM) and Slope 1 exhibiting the lowest (1.59 with the FEM). All slopes, designed with a uniform geometry, remained stable, exhibiting FoS values greater than 1.1. This study further provides an optimal slope design for the open pit in the andesite mining plan at Anugerah Berkah Sejahtera. These findings highlight the important role of accurate modeling in the assessment of slope stability. With a suggested safe slope height of 10 m and an angle of 80° (FoS = 1.62), slope stability analysis based on the factor of safety (FoS) showed that single slopes made of andesite maintain stability at steep angles. Claystone slopes, however, have a maximum slope height of 30 m at 20° (FoS = 1.27) and 27 m at 50° (FoS = 1.34), requiring more conservative geometries to maintain their stability. For an overall slope that comprises both rock types, a height of 30 m with a slope angle of 60° is recommended (FoS = 1.23) to ensure stability. The critical design condition for a claystone slope occurs at a height of 30 m with a slope angle of 50°, yielding a factor of safety (FoS) of 0.92, which indicates instability (FoS < 1.1). Similarly, a 35 m-high slope with a slope angle of 20° produced an FoS of 1.04, and a 35 m-high slope with a slope angle of 50° produced an FoS of 0.89, further confirming instability. For the overall slope configuration, instability occurs at a height of 30 m with a slope angle of 65° that produces an FoS of 1.09. Full article

This study presents an integrated surveying methodology for efficient and accurate cadastral documentation, combining UAV photogrammetry, SLAM-based terrestrial and aerial scanning, and conventional geodetic measurements. Designed to be scalable across various cadastral and planning contexts, the workflow was tested in Charlottenburg, Romania’s only circular heritage village. The approach addresses challenges in built environments where traditional total station or GNSS techniques face limitations due to obstructed visibility and complex architectural geometries. The SLAM system was initially deployed in mobile scanning mode using a backpack configuration for ground-level data acquisition, and was later mounted on a UAV to capture building sides and areas inaccessible from the main road. The results demonstrate that the integration of aerial and terrestrial data acquisition enables precise building footprint extraction, with a reported RMSE of 0.109 m between the extracted contours and ground-truth total station measurements. The final cadastral outputs are fully compatible with GIS and CAD systems, supporting efficient land registration, urban planning, and historical site documentation. The findings highlight the method’s applicability for modernizing cadastral workflows, particularly in dense or irregularly structured areas, offering a practical, accurate, and time-saving solution adaptable to both national and international land administration needs. Beyond the combination of known technologies, the innovation lies in the practical integration of terrestrial and aerial SLAM (dual SLAM) with RTK UAV workflows under real-world constraints, offering a field-validated solution for complex cadastral scenarios where traditional methods are limited. Full article

Soft mobility is gaining popularity in urban spaces due to its various benefits in terms of carbon footprint, air quality, congestion mitigation, and public health. Soft mobility infrastructure mainly includes urban road adjustments to accommodate pedestrian and bicycle flows. Relevant design codes are being developed worldwide, and important investments are being made in soft mobility. This paper provides a review and comparative analysis of 17 design codes and regulations from different countries and regions across the world. Furthermore, the German road design code for motorized traffic is used as a reference to assess the level of detail and eventual gaps in the soft mobility infrastructure design codes. Results indicate that, in contrast to road codes, soft mobility infrastructure codes vary significantly from country to country. Most importantly, the limit and recommended values of geometric parameters are fewer in number and less documented compared to road design parameters. Evidence-based recommendations are needed to enhance the design, construction, operation, maintenance, and safe management of soft mobility infrastructure. Full article

Tire shape prediction presents significant engineering challenges due to the complex behavior of cord-rubber composites during manufacturing processes. Fabric cord components undergo thermal shrinkage and permanent deformation that substantially influence final tire dimensions, creating discrepancies between mold geometry and cured tire shape. While Post-Cure Inflation (PCI) helps control these dimensional changes, accurate prediction methods remain essential for reliable performance forecasting. This study addresses this challenge through a systematic experimental characterization of fabric cord behavior under manufacturing conditions. Thermal shrinkage and permanent set were quantified under various combinations of in-mold strain and PCI force, with distinct patterns identified for different cord materials (PET and nylon). Based on these experimental findings, a comprehensive finite element analysis methodology was developed to predict cured tire shape. Validation against 65 tire profiles demonstrated remarkable improvements over conventional approaches, with dimensional error reductions of 54.2% for the outer diameter and 49.5% for the section width. Profile and footprint predictions also showed significantly enhanced accuracy, particularly in capturing geometric features critical for tire–road contact characteristics. The proposed methodology enables more precise tire design optimization, improved performance prediction, and reduced prototype iterations, ultimately enhancing both product development efficiency and final tire performance. Full article

This paper presents a microsimulation-driven framework to analyze the performance of connected and automated vehicles (CAVs) alongside vehicles with human drivers (VHDs), channeled towards assessing project alternatives in road intersection design. The transition to fully automated mobility is driving the development of new intersection geometries and traffic configurations, influenced by increasing market entry rates (MERs) for CAVs (CAV-MERs), which were analyzed in a microsimulation environment. A suburban signalized intersection from the Polish road network was selected as a representative case study. Two alternative design hypotheses regarding the intersection’s geometric configurations were proposed. The Aimsun micro-simulator was used to hone the driving model parameters by calibrating the simulated data with reference capacity functions (RCFs) based on CAV factors derived from the Highway Capacity Manual 2022. Cross-referencing the conceptualized geometric design solutions, including a two-lane roundabout and an innovative knee-turbo roundabout, allowed the experimental results to demonstrate that CAV operation is influenced by the intersection layout and CAV-MERs. The research provides an overview of potential future traffic settings featuring CAVs and VHDs operating within various intersection designs. Additionally, the findings can support project proposals for the geometric and functional design of intersections by highlighting the potential benefits expected from smart driving. Full article

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article

Road safety systems are critical engineering solutions designed to minimize the effects of traffic accidents and increase the safety of transportation infrastructures. Traditional road safety structures are generally manufactured using steel, concrete and polymer materials. However, manufacturing processes with these materials are high-cost, limited in terms of design flexibility and can lead to material waste. In recent years, rapidly developing additive manufacturing (AM) technologies stand out as an important alternative in the production of road safety systems. AM enables the production of complex geometries and enables the development of lightweight and high-strength structures that can absorb impact energy more effectively. This study focuses on the use of AM methods in road safety systems, examining the performance and applicability of polymer, metal and composite materials. The advantages of AM-produced road safety barriers, traffic signs, speed bumps and shock absorbing structures, depending on the material type, are evaluated. In addition, the advantages offered by AM, such as design flexibility, sustainable production processes and material efficiency, are discussed, and technical challenges and applicability limitations are also discussed. This review evaluates the current and potential applications of AM for road safety systems, providing insights into how this technology can be used more effectively in the future. The findings of the study provide significant contributions towards improving the integration of AM technologies into road safety systems from both academic and industrial perspectives. The findings of the study provide important contributions to the development of the integration of AM technologies into road safety systems from both academic and industrial perspectives. Future research can further enhance the innovative potential of AM in road safety systems, with a particular focus on sustainable material use, design optimization and energy efficiency in manufacturing processes. However, overcoming technical challenges in large-scale applications and compliance with regulatory standards are critical research areas for the widespread adoption of this technology. Full article

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues. Full article