Search Results (119)

Bike-sharing systems (BSS) are increasingly adopted as sustainable urban mobility solutions; however, the station location problem has been tackled through isolated approaches—mathematical optimization, GIS-based topological inspection, or multi-criteria analysis—without a unified framework for data-scarce cities. This study presents a three-stage integrated methodology for optimal BSS network design, applied to the Municipality of Athens, a city with no pre-existing BSS. Stage 1 generates candidate bike stations through GIS analysis, with demand estimated via a transit-population composite proxy combining fixed-track transit ridership and population density, and road safety quantified via kernel density estimation of traffic accidents. Stage 2 formulates a Maximal Coverage Location Problem (MCLP) as a mixed-integer linear program (MILP) solved via the ε-constraint method, generating Pareto fronts across twelve scenarios defined by four temporal periods and three walking thresholds. Stage 3 applies the AHP-TOPSIS methodology to rank candidate Pareto-optimal stations across six criteria. The selected optimal configuration of 124 stations at a 300 m walking threshold achieves 94% weighted demand coverage with a stable year-round network. Road safety is the dominant AHP criterion (weight = 43.4%), reflecting Athens’ critical infrastructure gap. These results validate the framework’s applicability in data-scarce contexts and offer a transferable methodology for BSS planning. Full article

Low-altitude unmanned aerial vehicle (UAV)-based traffic object detection is challenged by substantial scale variations from aerial perspectives, the extremely small pixel proportions of distant traffic participants, complex road background interference, unstable illumination, and severe occlusion in dense traffic scenes. To address these problems, this paper proposes ACP2-YOLO, an improved YOLO11-based detection framework for low-altitude UAV traffic scenarios, with the goal of enhancing the detection of vehicles, pedestrians, and non-motorized traffic participants. The proposed framework introduces two key improvements. First, a lightweight hybrid ACmix module that integrates convolution and self-attention is embedded into the network, enabling the model to jointly capture local detailed features and global contextual dependencies and thereby strengthen feature representation under complex backgrounds. Second, a P2 small-object detection layer is added to the original three-scale detection structure of YOLO11 to construct a four-scale P2–P5 feature pyramid. By allowing shallow high-resolution features to directly participate in object prediction, this design effectively reduces spatial information loss caused by deep downsampling and improves small-object perception. Experiments on the VisDrone2019 dataset show that the improved model achieves 53.1% Precision, 41.1% Recall, 42.9% mAP@50, and 26.3% mAP@50–95, outperforming the baseline YOLO11 by 4.2, 4.2, 5.0, and 3.6 percentage points, respectively. Comparisons with mainstream YOLO-series detectors further demonstrate its superior overall accuracy, small-object detection capability, and adaptability to complex scenes, indicating its potential for UAV-based traffic monitoring, road safety inspection, and intelligent transportation perception. Full article

Under-bridge inspection involves complex structural geometries, confined working spaces, and substantial safety risks for manual operation. To address these challenges, this study proposes a hierarchical obstacle-avoidance motion-planning framework for a large road-rail dual-use bridge inspection manipulator. First, a consistent kinematic model is established for an 11-DOF physical actuation system composed of six revolute joints and five prismatic telescopic joints. For inverse kinematics and template matching, the five physical telescopic joints are mapped to two equivalent prismatic variables, whereas collision checking and execution remain in the full physical joint space. Second, an improved bidirectional RRT-Connect planner is developed by integrating goal-biased sampling, multi-candidate expansion, soft low-lift constraints, and combined state and edge validity checking. Third, a pose-library-guided segmented planning strategy is introduced to reuse successful deployment sequences for known targets and to automatically generate intermediate poses for unseen targets. All post-processed trajectories are revalidated for collision and clearance before acceptance. Comparative simulations demonstrate that the proposed framework improves collision-free planning success and suppresses unreasonable high-lift configurations. The framework provides a reproducible planning solution for automated bridge inspection in confined under-bridge environments. Full article

Strengthening existing structures and bridges allows us to continue using them, increase their reliability, resistance, durability and extend their service life instead of demolishing them and replacing them with new ones. This helps to reduce CO₂ (decarbonization). The use of prestressed CFRP strips represents the use of new modern materials and new technology for strengthening existing bridges. The paper is focused on the use of prestressed CFRP strips for strengthening a concrete bridge made of precast prestressed box girders as the most suitable strengthening alternative in a given case. This is a technology that is more commonly used for strengthening structures, but it is not common to use this technology for strengthening bridges. There are relatively few examples of using this technology for strengthening bridges, also because these are dynamically loaded structures. The paper firstly presents the diagnostics and calculation of the load-carrying capacity of the railway bridge on a narrow-gauge railway line in Štrbské Pleso, Slovakia, and then the strengthening of the given bridge. The bridge is located in the mountains of the High Tatras in the northern part of Slovakia and bypasses two local roads. The bridge was made from the precast prestressed post-tensioned box girders of six single spans. The visual inspection, diagnostics, and verification of real dimensions and material characteristics were requested. The non-destructive and semi-destructive methods of testing were used to determine the geometrical and materials’ properties. After that, the calculation of the load-carrying capacity was done. For this purpose, a numerical 3D FEM model was created. For determining the load-carrying capacity, the standard approach, given in Eurocodes, was used according to provisions, which take into account the modified (lower) reliability levels and their adequate partial safety factors. From the calculation, it follows that the bridge should be strengthened. The strengthening of the superstructure was done using prestressed CFRP strips in the lower part of the box girders. This is one of the first applications of this modern method of strengthening, not only in Slovakia but in Central Europe as well. Full article

To address the issue of traffic accidents caused by livestock crossing roads on grassland highways, this paper proposes an adaptive cruise control method for unmanned aerial vehicles (UAVs) based on an ensemble Q-network and a Soft Actor-Critic (SAC) with delayed policy updates, namely the ED-SAC algorithm. Building upon the standard SAC framework, this method introduces multiple independent Critic networks to form an ensemble Q-network, and employs a random subset minimization strategy during the calculation of target Q-values to mitigate policy bias resulting from overestimated values; simultaneously, a delayed policy update mechanism decouples the optimization processes of the Actor and Critic networks, thereby enhancing training stability and control robustness. Using the PyBullet simulation platform, this paper constructs a UAV inspection scenario on grassland roads and designs three typical test tasks: infinite loop, grid scan and spiral trajectories, to conduct comparative validation of the PPO, TD3, SAC and ED-SAC algorithms. Experimental results demonstrate that, under disturbance-free conditions, ED-SAC achieves the highest mission success rate and the lowest tracking error across all three trajectory scenarios, with an average tracking error as low as 0.27 m and a mission success rate as high as 98.7%. Under continuous random external disturbances, ED-SAC still maintains high trajectory tracking accuracy and attitude control stability, with a mission success rate reaching up to 96.2%. The results demonstrate that the proposed ED-SAC algorithm can effectively enhance the trajectory tracking accuracy, training stability and anti-disturbance capability of UAVs in complex grassland road inspection scenarios, providing a reliable intelligent control method for active grassland road inspection and traffic safety early warning. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment). This article introduces the Random Generated Reference (RGR) method, a novel automated solution for quantifying marking degradation using a standard on-board camera. The proposed pipeline is a complete protocol from video acquisition to road marking inspection and validation of the inspection that combines deep learning with computer vision: YOLOv8 is employed for robust detection, while a unique algorithm generates a “perfect virtual reference” that dynamically replicates the real scene’s geometry and illumination conditions, including shadows. By computing pixel-level deviations between the observed marking and this ideal reference, the system assigns a continuous degradation score aligned with the UK CS126 standard. Experimental validation was conducted on a real-world circuit yielding over 20,000 detections. Verification via Cochran sampling demonstrates that 68% of the automated assessments fall within one class of human inspection. This proof-of-concept confirms the viability of an approach based on generating the ground truth and scene conditions—such as illumination, shadows, rain, traffic, etc.—for road marking inspection. Full article

Road construction, inspection and maintenance are activities that often require workers near heavy equipment, traffic, and dangerous materials. This proximity to potential hazards along with the characteristics of highway and street work zones—transient and in restricted areas—increases the possibility of accidents and near-misses. Recent developments in remote-controlled technology can provide workers and inspectors with the ability to conduct activities from a safer distance. This paper aims to scan and evaluate several promising remote-controlled technologies that could be used to improve safety in highway and streets work zones. The technology scanning highlighted over twenty technologies in several levels of development that met this goal. Each technology was briefly evaluated not only based on safety features but also on productivity, data processing, and requirements for implementation. Finally, recommendations for implementation of selected technologies were provided. This consolidated review provides a unique and timely resource for researchers and practitioners. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations. Full article

Road safety depends heavily on the timely identification and repair of potholes; however, detecting potholes is challenging due to various lighting and weather conditions. This work presents an attention-enhanced object detection framework for aerial pothole detection design that relies on a pre-trained backbone, YOLOv8, and a custom feature-extraction network, the Feature Pyramid Network (FPN). An enhanced detection head is used to make the model aware of discriminative areas in space to get accurate localization of a pothole to overcome the major limitations of the standard YOLOv8 used in aerial road inspection, irrespective of the road surface. The underlying architecture incorporates a purpose-built data layer and a preprocessing engine that can accommodate scenarios such as seasonal changes and bad weather. To further enhance learning dynamics, a customized loss function and a new optimizer framework are incorporated to improve convergence towards overall detection reliability. Specifically, a custom differential optimizer that uses layer-wise adaptive learning rates and momentum-based gradient updates to help suppress false positives and accelerate convergence. Conversely, the IoU-based personal loss function, combined with real-time validation, stabilizes training across a range of road conditions. A major feature of the proposed system is its ability to process aerial imagery from unmanned drone platforms. Empirical analysis proves a good result: an average precision of 0.980 with the IoU of 0.5 and an F1-score of 0.97 with a confidence threshold of 0.30. Precision is high (0.97 at the 90-percent confidence level). These metrics show how well the model will be able to balance false positives and false negatives—a critical need in a safety-critical deployment. The results make the framework a potential, scalable, and reliable candidate for integrating smart transportation systems and autonomous vehicle navigation. Full article

With the continuous advancement of autonomous driving technology, lane marking-based environment perception has become a critical component of autonomous vehicle systems. However, long-term vehicle loads cause road markings to deteriorate and fade, significantly compromising driving safety. Traditional road marking quality inspection methods are inefficient and struggle to achieve high-performance, convenient detection. To address these challenges, this paper proposes an integrated framework for road marking detection and evaluation using Unmanned Aerial Vehicle (UAV) imagery. The framework comprises three core modules: lightweight data acquisition, efficient marking extraction, and accurate distress assessment. First, optimized UAV flight parameters enable low-cost, highly flexible, and safe data collection. Second, the YOLOv8-MEB model, combined with instance segmentation screening and local image optimization, achieves lane segmentation precision and recall above 90% with FPS exceeding 60. Furthermore, a standard marking template library is constructed, and a RANSAC-based template matching method with affine transformation is employed to restore intact marking shapes. A contour correction strategy is introduced to mitigate errors induced by construction inaccuracies. The proposed framework supports nine common types of road markings and yields approximately 10% error in distress ratio calculation under non-severe damage conditions, providing a practical technical reference for intelligent road maintenance. Full article

The identification and monitoring of pavement pathologies are critical for maintaining road infrastructure and ensuring transportation safety. As traditional inspection methods are often time-consuming, labor-intensive, and prone to human error, in recent years, Unmanned Aerial Vehicles (UAVs) have emerged as a promising tool for pavement condition assessment due to their mobility, efficiency, and ability to capture high-resolution imagery and multi-sensor data. This Systematic Literature Review aims to synthesize and evaluate existing research on the use of UAV for identifying pavement pathologies, such as cracks, potholes, rutting, and surface degradation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology, publications were screened and selected across major academic databases such as Scopus and Web of Science. A total of 361 relevant articles published from 2020 to July 2025 were identified and analyzed using bibliometric overview. And a full-text synthesis and qualitative analysis was performed on a subset of 108 studies, which met the quality assessment criteria. The review categorizes the UAV systems, computer vision approaches, pathology types, and pavement materials examined in the studies. The findings indicate a growing trend in the use of UAV and computer vision techniques for pavement pathology detection, along with evolving preferences for UAV platforms, analytical approaches, and targeted pathology categories over time. This review highlights current gaps and outlines future research directions to advance UAV-based pavement pathology identification as a viable and reliable alternative to conventional inspection methods. Full article

This article presents the results of simulation-based research aimed at assessing the impact of tyre damage on vehicle travel safety. The analysis takes into account various influencing factors, including vehicle speed, load conditions, and road surface condition (dry or wet asphalt). Particular emphasis was placed on the dynamic analysis of the vehicle during collision scenarios, including post-impact vehicle positioning, changes in kinetic energy, and the magnitude of the generated impact force. Simulation results indicate that tyre damage significantly compromises vehicle trajectory stability and, in certain cases, makes vehicle control impossible. The conclusions highlight the critical importance of maintaining proper tyre condition in mitigating the consequences of road collisions and emphasise the need for regular tyre inspections as part of routine vehicle maintenance. Full article