Search Results (166)

This paper presents a novel data fusion framework that integrates Automatic Identification System (AIS) data with radar surveillance for real-time vessel monitoring in inland restricted waterways. The approach exploits the complementarity between heterogeneous sensors: AIS provides semantic information with temporal sparsity, while radar offers high-frequency observations without vessel identity. The proposed solution combines Kalman filtering and Newton interpolation (K-N) for high-resolution AIS resampling, followed by optimal data association using the Kuhn-Munkres (KM) algorithm. By formulating data association as a global optimization problem, the framework achieves globally optimal sensor fusion while effectively handling data imbalance through virtual point augmentation. Experimental validation using real-world data demonstrates a matching accuracy of 94.2% in low-density scenarios and 80.1% in high-traffic conditions, with computational efficiency suitable for real-time deployment. The system performs consistently across different waterway geometries, although performance varies slightly between curved and straight channels. By fusing the high temporal resolution of radar data with the rich identity information from AIS, this framework enables more accurate and reliable vessel tracking, providing waterway authorities with enhanced situational awareness for improved traffic management and scheduling in restricted waterways. Full article

Social media platforms have been established as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on semi-synthetic data streams constructed from historical geolocated seeds for controlled validation, utilizing real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous updating of the model in response to changes in traffic flow and concept drift. The classified events are then processed using DenStream, a clustering algorithm that incorporates a temporal decay mechanism designed to identify dynamic spatial patterns and discard older information. The evaluation is performed in a controlled streaming simulation environment that replicates the dynamics of cities such as Panama and Guayaquil. The proposed framework demonstrated robust quantitative performance, achieving a prequential accuracy of up to 86.4% and a weighted F1-score of 0.864 in the Panama scenario, maintaining high stability against semantic noise. The results suggest that this hybrid architecture is a highly viable approach for urban traffic monitoring, providing useful information for Intelligent Transportation Systems (ITS) by processing authentic social signals. Full article

Urban transport is a major contributor to particulate matter (PM) pollution, yet information on the spatial distribution of fine and ultrafine particle fractions remains limited in medium-sized tropical cities. This study examines the spatial variability of transport-related particulate matter across eleven urban districts in Padang, Indonesia, using Nano Sampler-based measurements. Size-segregated PM concentrations (PM₁₀, PM_2.5, PM₁, and PM_0.5) were obtained from 24 h sampling campaigns conducted between June and July 2025 at locations selected based on urban density, proximity to major roadways, and land-use characteristics. Descriptive statistics, correlation analysis, and principal component analysis were applied to evaluate spatial patterns and traffic-related influences. The results show pronounced spatial heterogeneity in PM concentrations. Traffic-intensive and mixed-use districts exhibited higher PM levels, particularly for coarse and ultrafine fractions, whereas coastal districts showed lower concentrations due to enhanced atmospheric ventilation. Strong correlations were observed between traffic volume and coarse PM fractions, with moderate associations for fine and ultrafine particles, indicating combined exhaust and non-exhaust emissions. These findings highlight the importance of district-specific mitigation strategies and size-resolved monitoring to support effective urban air-quality management. Full article

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how the Specific Operations Risk Assessment (SORA) methodology can be applied to conservation-oriented BVLOS missions under Kenyan airspace conditions, including coordination within military-controlled airspace. We evaluate three population-density estimation approaches (qualitative, bottom-up, and top-down) against available ground truth, and compare tabulated and analytical SORA methods for deriving the Ground Risk Class. The work illustrates how SORA 2.5 structures ground and air risk reasoning in a conservation context, while retrospective review identifies limitations in containment, Operational Safety Objectives, and tactical mitigation performance requirements. Field trials involved five concurrent teams and 30 personnel conducting over 260 flights and more than 60 h of UAS activity across the Ol Pejeta Conservancy, providing insights into multi-team coordination under field conditions. Field implementation revealed areas of misalignment between prescribed safety requirements and operational realities, prompting iterative adaptation of workflows and procedures. Observed outcomes included reductions in team size (25–50%) and procedural steps (18%), derived from retrospective comparison of field procedures. A lightweight Uncrewed Traffic Management prototype was also trialled, revealing practical limitations in conservancy environments. Finally, we present a ten-step framework for developing field-ready safety procedures to support risk-informed decision-making in non-standard operational contexts. The findings provide empirically grounded guidance on applying SORA principles to conservation UAS missions, without proposing a new risk framework or generalised operational model. Full article

Spatio-temporal features are crucial for maritime trajectory forecasting, especially in scenarios involving curved waterways or abrupt changes in ship motion patterns. Although Automatic Identification System (AIS) data, which are widely used for trajectory prediction, inherently include temporal and spatial information, effectively strengthening these features and integrating them into prediction models remains challenging. To address this challenge, we propose a Convolutional Neural Network (CNN)-Series-cOre Fused Time Series forecaster (SOFTS)-based framework that explicitly couples spatial and temporal features to achieve high-fidelity maritime trajectory forecasting, especially in scenarios with complex spatial patterns. We first employ a CNN-based spatial encoder to hierarchically abstract spatial density distributions through convolution and pooling operations, thereby learning global spatial structure patterns of ship movements. This encoder emphasizes overall spatial morphology rather than precise individual trajectory points. Second, we employ the SOFTS model to incorporate angular velocity, acceleration, and angular acceleration as input features to characterize ship motion states, which can capture the temporal dependencies of ship motion states from multivariate time series. Finally, the spatial embedding features extracted by the CNN are concatenated with the temporal feature representations learned by SOFTS along the feature dimension to form a joint spatiotemporal representation. This representation is then fed into a fusion regression module composed of fully connected layers to predict future ship trajectories. Experimental results on the validation dataset show that the proposed method achieves an MSE of 0.020 and an MAE of 0.060, outperforming several advanced time series forecasting models in prediction accuracy and computational efficiency. The introduction of angular velocity, acceleration, and angular acceleration features reduces the MSE and MAE by approximately 10.22% and 9.49%, respectively, validating the effectiveness of the introduced dynamic features in improving trajectory prediction performance. These results underscore the proposed method’s potential for intelligent navigation and traffic management systems by effectively enhancing inland river navigation safety and strengthening waterborne traffic monitoring capabilities. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Expressway traffic noise poses a critical threat to public health in developed high-density cities, causing chronic environmental stress in adjacent residential areas. While physical noise barriers are commonly used, the potential of audiovisual interactions in mitigating the adverse effects of traffic noise remains under-explored. Using immersive virtual reality (VR), this study examined the efficacy of visual greenery and auditory masking (birdsong) in promoting stress recovery, and tested whether audiovisual perception mediates the environment–restoration link. Following an acute stressor, 100 participants were randomly assigned to a 2 × 2 between-subjects experiment manipulating Green View Index (high vs. low) and soundscape composition (traffic noise vs. traffic noise plus birdsong), with 25 participants in each group. Restorative outcomes were assessed using self-reported measures and continuous physiological monitoring (heart rate variability [HRV] and electrodermal activity [EDA]). Results demonstrated that high-intensity visual greenery and natural sounds effectively enhance psychological restoration in noise-affected environments. Structural equation modeling revealed that audiovisual perception fully mediated the relationship between environmental features and restorative outcomes. The physiological outcome showed a distinct tiered restoration pattern, indicating that immediate psychological buffering can be achieved through natural sounds, while consistent visual reinforcement remained essential for deep physiological recovery. Consequently, soundscape planning in expressway-adjacent zones should integrate visual greening strategies to optimize the perceptual masking of traffic noise and enhance the environmental quality. Full article

With the increasing adoption of regenerative braking technology in electric vehicles (EVs), one-pedal driving (OPD) mode has become a prevalent feature. While OPD offers technical advantages in energy efficiency, its implications for driver behavior and traffic safety remain unclear. To address the lack of human factors research in this domain, this study utilized a driving simulator to systematically compare driving performance between OPD and two-pedal driving (TPD) modes. Twenty-six participants engaged in car-following tasks under varying traffic densities (uncongested vs. congested) and cognitive load levels (normal vs. 1-back). Driving performance and safety were quantified using the absolute speed difference, distance headway, braking frequency, and Time-to-Collision at brake onset (TTC_brake). The results revealed a significant trade-off: while OPD simplified operation, it led to compromised driving performance compared to TPD in specific contexts. Specifically, OPD resulted in larger speed variations and reduced safety margins during the approach stage. Conversely, under high cognitive load, OPD demonstrated a protective effect by mitigating performance degradation. These findings suggest that while OPD can benefit drivers under mental pressure, its deployment requires adaptive safety strategies, such as the integration of Headway Monitoring Warning (HMW) and Forward Collision Warning (FCW), to compensate for performance deficits in complex traffic environments. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

This study performs a comprehensive experimental analysis of the dynamic response of geocell-reinforced sandy subgrades exposed to traffic-induced loading. A series of laboratory tests were performed using a custom-manufactured loading apparatus capable of creating monitored dynamic waveforms representative of vehicular traffic. A steel strip footing was assigned on both unreinforced and geocell-reinforced sandy beds to evaluate the implementation of the reinforcement in attenuating transmitted vertical stresses and surface settlements. The influence of key parameters, among which were load amplitude (0.5 and 1.0 tons), loading frequency (0.5, 1.0, and 2.0 Hz), and relative density of sand (30% loose and 60% medium), was systematically examined. The applied dynamic loading was based on a force-controlled sinusoidal waveform with constant amplitudes and frequencies, which corresponded to low-frequency harmonic cyclic loading in the case of traffic-induced quasi-static effects. Therefore, the experimental results indicate that geocell reinforcement reduces the transmitted vertical dynamic stress by up to 45% and reduces surface settlement by about 60% compared to unreinforced sand. However, the heightening efficiency decreases with loading frequency, the amplitude of the load, and the relative sand density. Thus, the findings are important in highlighting the capacity of geocell systems to enhance the longevity and efficiency of sand substrates when the systems are subjected to low-frequency harmonic cyclical loading conditions pertaining to traffic-induced quasi-static influences. Full article

Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function $\beta silu$, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article

Automatic Identification System (AIS) signals are critical for maritime traffic monitoring and collision avoidance. In satellite reception environments, signal collisions occur frequently due to large coverage areas and high ship density, severely degrading decoding performance. We propose a dual-branch deep learning architecture that combines precise boundary detection with segment-level classification to address this collision problem. The network employs a multi-scale convolutional backbone that feeds two specialized branches: one detects collision boundaries with sample-level precision, while the other provides semantic context through segment classification. We developed a satellite AIS dataset generation framework that simulates realistic collision scenarios including multiple ships, Doppler effects, and channel impairments. The trained model achieves 96% collision detection accuracy on simulated data. Validation on real satellite recordings demonstrates that our method retains 99.4% of valid position reports compared to direct decoding of the original signal. Controlled experiments show that intelligent collision removal outperforms random segment exclusion by 6.4 percentage points, confirming the effectiveness of our approach. Full article

Traffic signs exposed to long-term outdoor conditions frequently exhibit deformation, inclination, or other forms of physical damage, highlighting the need for timely and reliable anomaly assessment to support road safety management. While point-cloud data provide accurate three-dimensional geometric information, their sparse distribution and lack of appearance cues make traffic sign extraction challenging in complex environments. High-resolution sequential video-log images captured from multiple viewpoints offer complementary advantages by providing rich color and texture information. In this study, we propose an integrated traffic sign detection and assessment framework that combines video-log images and mobile-mapping point clouds to enhance both accuracy and robustness. A dedicated YOLO-SIGN network is developed to perform precise detection and multi-view association of traffic signs across sequential images. Guided by these detections, a frustum-based point-cloud extraction strategy with seed-point density growing is introduced to efficiently isolate traffic sign panels and supporting poles. The extracted structures are then used for geometric parameterization and damage assessment, including inclination, deformation, and rotation. Experiments on 35 simulated scenes and nine real-world road scenarios demonstrate that the proposed method can reliably extract and evaluate traffic sign conditions in diverse environments. Furthermore, the YOLO-SIGN network achieves a localization precision of 91.16% and a classification mAP of 84.64%, outperforming YOLOv10s by 1.7% and 8.7%, respectively, while maintaining a reduced number of parameters. These results confirm the effectiveness and practical value of the proposed framework for large-scale traffic sign monitoring. Full article

Urban mobility systems increasingly depend on remote sensing and artificial intelligence to enhance traffic monitoring and safety management. This study presents a LiDAR-based framework for urban road condition analysis and risk evaluation using vehicle-mounted sensors as dynamic remote sensing platforms. The framework integrates deep learning based object detection with mathematically defined surrogate safety indicators to quantify collision risk and evaluate evasive maneuverability in real traffic environments. Two indicators, Hazardous Modified Time to Collision (HMTTC) and Searching for Safety Space (SSS), are introduced to assess lane-level safety and spatial availability of avoidance zones. LiDAR point cloud data are processed using a Voxel RCNN architecture and converted into parameters such as density, speed, and spacing. Field experiments conducted on highways and urban corridors in South Korea reveal strong correlations between HMTTC occurrences, congestion, and geometric road features. The results demonstrate that AI-driven analysis of LiDAR data enables continuous, infrastructure-independent urban traffic safety monitoring, thereby supporting data-driven, resilient transportation systems. Full article

The Strait of Gibraltar is a major biogeographic bottleneck connecting the Atlantic Ocean and the Mediterranean Sea, where migratory cetaceans coexist with an intense maritime traffic. To evaluate the feasibility of broadband passive acoustic monitoring (PAM) for both soundscape characterisation and cetacean detection, a short drifting-buoy experiment was conducted near Barbate, Spain, in May 2025. The system, equipped with a calibrated SoundTrap 400 recorder, continuously sampled the underwater acoustic environment for 2.5 h. Analysis of the recordings revealed vocalisations of Orcinus orca, representing the first preliminary and incomplete description of the Iberian killer whale acoustic repertoire, and numerous transient tonal events with energy peaks between 40 and 50 Hz, consistent with baleen whale sounds previously attributed to foraging fin whales (Balaenoptera physalus). Sperm whale clicks and delphinid whistles were also occasionally detected. The power spectral density analysis further showed a persistent anthropogenic component dominated by vessel noise below 200 Hz and narrow-band echosounder signals at 30 and 50 kHz. These findings confirm the potential of PAM to detect multiple cetacean species and to resolve the complex interplay between biophony and anthropophony in one of the world’s busiest marine corridors. Establishing a permanent PAM observatory in the Strait would enable continuous, non-intrusive monitoring of species presence, behaviour, and habitat use, thereby contributing to conservation efforts for endangered populations such as the Iberian killer whale. Full article