Search Results (4,820)

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

Highway vehicle–road collaborative systems rely on the effective deployment of roadside equipment (RSE) to support both traffic sensing and communication. In mixed connected and automated vehicle (CAV) and human-driven vehicle (HDV) traffic environments, existing studies on hybrid RSE deployment have mainly focused on unilateral deployment or scenarios with a high CAV penetration rate, whereas bilateral deployment under a low-to-medium CAV penetration rate has received limited attention. To address this gap, this study proposes a bilateral hybrid deployment framework for highways, in which sensing and communication RSE (scRSE) and communication RSE (cRSE) are jointly allocated based on data sensing accuracy and communication connection probability. The proposed method is validated through a case study on the Qinglan Expressway in Shandong Province, China. The results show that the bilateral hybrid deployment method outperforms the benchmark deployment methods in both sensing and communication performance. In a representative scenario, the mean symmetric mean absolute percentage error (SMAPE) decreases from 2.36% under bilateral uniform deployment to 0.94% under bilateral hybrid deployment, while the mean communication connection probability (MCCP) increases from 82.20% to 86.29%. Moreover, the proposed method performs better than unilateral deployment strategies under the same deployment conditions. These findings indicate that the proposed bilateral hybrid deployment framework offers a practical and cost-effective solution for highway RSE allocation in mixed traffic environments, particularly under low-CAV-penetration conditions. Full article

Urban transportation systems are facing increasingly severe threats from extreme weather events such as rainstorms, which can trigger cascading failures and lead to regional traffic paralysis. The strategic location of emergency facilities to enhance system resilience has emerged as a critical proactive prevention strategy. This study proposes a multi-objective hierarchical coverage location model that integrates point and flow demands to improve the resilience of urban road traffic systems under rainstorm conditions. First, the resilience risk levels of road nodes were quantified using an entropy-weighted TOPSIS method that combines topological attributes, traffic flow performance, and indirect propagation intensity. Second, a flow-capturing mechanism was introduced to address the dynamic rescue demands of stranded vehicles in motion, enabling the pre-positioning of “safe havens” along critical travel routes. The model balances two objectives: maximizing the resilience risk value of the covered demands and minimizing facility construction costs. A case study was conducted in Jianghan District, Wuhan, a flood-prone area, and the NSGA-II algorithm was employed to solve the multi-objective optimization problem. The results demonstrate that the proposed model significantly outperforms traditional single-demand location models in terms of coverage effectiveness and cost efficiency, achieving improvements in resilience risk coverage of up to 311.6% and cost reductions of up to 63.6%. This study provides a systems science perspective for pre-disaster emergency resource allocation, shifting the paradigm from infrastructure-centric protection to human-centered rescue. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Shrinking and aging societies undermine the economic viability of network-based infrastructure once supported by economies of scale and network externalities. This paper develops a conceptual framing of “Denetworking” as a possible reconfiguration strategy in the contraction phase: reducing dependence on highly asset-specific dedicated networks (e.g., pipes and rail tracks) and shifting service functions to distributed systems or generic shared networks (e.g., roads) while maintaining minimum service standards. Rather than presenting a calibrated optimization model or full life-cycle cost (LCC) estimation, the paper proposes a heuristic decision condition for comparing a “keep” scenario (renew and maintain the dedicated network) with a “shift” scenario (Denetworking) and uses quantitative anchors from public sources to illustrate the associated fiscal and institutional trade-offs. Two Japanese cases are used as contrasting illustrations: physical Denetworking, referring to the reduction in or substitution of dedicated physical network assets, in wastewater services (centralized sewerage to decentralized treatment); and functional Denetworking, referring to the transfer of service functions from dedicated networks to more generic shared networks, in regional mobility (local rail to bus/BRT on the road network). The cross-case discussion suggests that Denetworking may become a rational policy option under certain conditions, particularly when demand density declines near renewal-investment peaks and asset specificity increases lock-in. The paper contributes a conceptual vocabulary and comparative policy framing for discussing infrastructure reconfiguration in shrinking societies and highlights practical issues of timing, cost sharing, phased implementation, and stakeholder engagement. Full article

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

The fast drainage of surface water from road pavements is essential to ensure both driving safety and adequate infrastructure service life. For close-graded asphalt mixtures, surface runoff relies on sufficient longitudinal and transverse slopes that convey water toward hydraulic drainage devices. However, construction defects, surface distress, or inadequate placement of drainage systems may compromise this process and reduce pavement durability. When water infiltrates beneath the wearing course and saturates the underlying layers, heavy traffic loads can accelerate deterioration through erosion, pumping, interlayer delamination, and subgrade overstress. This work investigates the joint use of Ground Penetrating Radar (GPR) and Mobile Laser Scanning (MLS) to evaluate drainage deficiencies and detect signs of layer delamination in bituminous pavements. A highway section in Salerno (Italy) was selected as a case study due to known hydraulic-related issues. MLS data were used to reconstruct pavement geometry and model surface runoff patterns, while GPR surveys assessed the condition of the bonding between asphalt and base layers. The results revealed ineffective runoff management and identified multiple areas affected by delamination, confirming a relationship between surface drainage behaviour and subsurface damage. These findings highlight the broader potential of the integrated GPR–MLS framework as a scalable and transferable approach for proactive drainage assessment and structural monitoring in pavement management practices. Full article

Horse chestnut (Aesculus hippocastanum L.) is a widely planted ornamental and urban tree valued for its aesthetic and ecological functions. In recent years, declining health of horse chestnut in urban environments has been increasingly reported, often associated with a complex of biotic and abiotic stressors. During a health survey of A. hippocastanum trees growing along an urban road corridor in Warsaw, Poland, extensive bark necrosis and branch dieback were observed. The aim of this study was to identify the causal agent of these symptoms using morphological, cultural, molecular (ITS rDNA), and pathogenicity tests under controlled conditions. Fungal isolates were obtained from necrotic tissues and were consistently identified as Kalmusia variispora based on ITS sequence analysis (99.0–99.6% similarity to GenBank references) and characteristic morphology. Pathogenicity tests fulfilled Koch’s postulates, reproducing necrotic lesions and cambial damage similar to those observed in the field. To our knowledge, this is the first documented report worldwide of K. variispora infecting A. hippocastanum. The findings expand the known host range of this opportunistic Didymosphaeriaceae species and highlight its potential role in bark and wood disease complexes of urban trees. Further research is needed to assess its distribution, genetic diversity, and epidemiological significance in urban forest ecosystems. Full article

Fleet electrification is increasingly recognized as a cornerstone of urban decarbonization in high-density megacities. This study introduces a multi-scenario simulation framework integrating high-resolution mobile signaling data with traffic modeling to quantify the systemic environmental and energy impacts of road-based battery electric vehicle (BEV) integration in Shanghai. By evaluating both a fixed-fleet baseline and dynamic-fleet growth scenarios focused on the urban road network, we find that aggressive fleet electrification leads to a profound reduction in aggregate carbon emissions and criteria pollutants, effectively decoupling transit-related environmental burdens from urban growth. However, results also highlight a significant energy trade-off: while fossil fuel displacement accelerates, grid-based electricity demand increases under fleet growth conditions. Within this context, the expanded vehicle population exacerbates urban congestion, which disproportionately inflates the fuel consumption of remaining internal combustion vehicles. Their operational efficiency is severely compromised by frequent stop-and-go cycles, leading to an intensification of idling losses. Ultimately, this research highlights the capability of the proposed simulation framework to provide granular insights into urban emission dynamics, offering a quantitative foundation for policymakers to harmonize electrification targets with proactive traffic management and grid infrastructure strengthening to evaluate the systemic trade-offs toward achieving long-term urban sustainability. Full article

Existing research primarily focuses on ordinary straight roads or curves; however, there is a notable lack of recent research on continuous curves. This research employs Computational Fluid Dynamics (CFD) dynamic mesh technology to numerically simulate the external flow field during vehicle overtaking on a continuous curve resembling a sine wave. This study conducts a numerical simulation to analyze the external flow field of vehicles during overtaking on a continuous curve, similar to a sine curve, using CFD. Using different initial velocities, the study analyzes lateral force on the vehicle body during overtaking. It investigates how dynamic changes in the external flow field affect vehicle dynamics by employing tetrahedral meshes, the SST k-ω turbulence model, and UDF programming. To address emergency overtaking scenarios during medical vehicle rescues, a four-factor orthogonal experimental design was employed to identify the safest overtaking condition: overtaking a small vehicle (5 m × 1.8 m) at 22 m per second with 1.5 times the vehicle width and no crosswind. Regression lines were fitted to the data, yielding a nonlinear regression equation that can predict road conditions, thereby providing theoretical support for intelligent driving systems. Full article

Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design. Full article

Generative AI has advanced rapidly in architectural design; however, existing building footprint generation models tend to emphasize stylistic exploration while insufficiently integrating site context as a fundamental physical constraint that facilitates alignment with the surrounding urban fabric. To address this limitation, this study proposes a context-responsive methodology for generating building footprints using a multi-layered four-channel representation of site conditions—including roads, sidewalks, adjacent buildings, and site boundaries—within a Latent Diffusion Model framework. The proposed approach encodes these physical conditions into a structured tensor and concatenates them directly to the U-Net input, enabling site context to function as an explicit spatial control variable during generation. An ablation study evaluated the effectiveness of the proposed contextual configuration. Compared with a single-channel model, the four-channel model achieved an 18.08% reduction in average pixel-wise information entropy, indicating a measurable decrease in generative uncertainty. Qualitative analyses further demonstrated that the enriched contextual input promotes geometrically coherent footprint configurations, such as context-responsive setbacks and spatial alignment with surrounding built forms. These findings suggest that structured multi-channel site information enhances contextual grounding in generative design processes and may contribute to more environmentally integrated and spatially coherent architectural outcomes. Full article

The study presents an engineering solution for maintaining traffic flow during road and utility operations, such as trench excavation. The analysis of existing organizational and technical approaches, along with global experience in temporary bridge use, showed that most foreign analogs were developed for military purposes and are not fully suitable for urban conditions in Kazakhstan and CIS countries. As an alternative solution, the development of a mobile overpass adapted for operation in dense urban environments is proposed. The present study continues earlier research focused on optimizing the placement of mobile overpass supports while accounting for the nonlinear deformation behavior of the soil foundation. At the previous stage, a rational distance between the supports and the trench edge was substantiated, and horizontal soil deformations were reduced. In the current study, the primary focus is on the design of the undercarriage, which determines the mobility, stability, and operational feasibility of the structure. A morphological analysis and synthesis method is applied to select a rational configuration of the undercarriage. A 3D model and a 1:4 scale test bench were developed, followed by load tests of 50–200 kg. The maximum deflection of −1.19 mm at 200 kg demonstrated an almost linear deformation pattern. The constructed regression model ($R^2=0.97$) confirmed the accuracy and reliability of the design. The developed mobile overpass is versatile, cost-effective, and practical, improving the resilience of urban transport infrastructure, reducing traffic congestion during roadworks, and creating a foundation for serial production in Kazakhstan and CIS countries. Full article