Search Results (167)

Road geometry is a modifiable determinant of crash occurrence and severity; addressing it is critical for achieving sustainable transport systems. Yet, policy action requires clear prioritization across road types and years to ensure sustainable resource allocation. This study analyzes fatal and injury outcomes by roadway geometric context in Türkiye (2015–2024) and proposes a cell-level prioritization framework integrating crash burden, severity, and short-term deviations to support long-term sustainable road safety management. Annual data were structured as Year × Road type × Geometry × Category, with severity measured as deaths and injuries per 100 crashes (Kmin = 30). A Road Geometry Severity Index (RGSI; 0–100) combined standardized severity, log crash burden, and deviation from a three-year baseline. Isolation Forest and a MAD-based rule identified anomalies, while K-means clustering (K = 4) revealed burden–severity profiles. Results show deaths per 100 crashes declined from 7.91 (2015) to 3.29 (2022), then rose to 6.22 (2024). Severity was highest on provincial (8.82) and state roads (7.23), compared to motorways (4.66). High-severity cells were dominated by provincial-road contexts, especially dangerous curves and junction-related categories. The highest-priority cell was 2018–Provincial Road–Junction–No Junction (RGSI = 100). Under the predefined contamination specification (γ = 0.05), the Isolation Forest model flagged 35 anomalous cells, all of which also satisfied the MAD-based anomaly criterion. Findings highlight persistent high-priority roadway geometric contexts and demonstrate the potential of RGSI as a transparent infrastructure-prioritization tool. Full article

Accidents and vehicle damage caused by irregular road surfaces, such as potholes and cracks, remain a significant challenge in autonomous driving, particularly in terms of safety and trajectory reliability. Existing approaches often treat perception and control as separate processes, limiting their ability to respond effectively to road-surface anomalies in real time. In the proposed work, a unified framework for road-surface anomaly-aware control that integrates 3D point cloud perception with a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) is presented. The proposed approach utilizes onboard sensors to capture detailed geometric information of the road surface and detect anomalies relevant to vehicle motion. The detected anomalies are represented in a control-oriented form and incorporated into the LPV-MPC framework, enabling adaptive trajectory planning and speed regulation. This integration allows the controller to proactively adjust vehicle behavior in response to surface irregularities, improving both safety and tracking performance. Experimental results demonstrate that the proposed method enhances robustness against road disturbances and improves trajectory tracking compared to conventional control approaches without anomaly awareness. These results highlight the effectiveness of tightly coupling perception and control for reliable autonomous driving in real-world conditions. Full article

For intelligent transportation systems, monitoring road surface integrity is critical for enhancing vehicle safety and infrastructure longevity. Traditional detection relies on high-cost Light Detection and Ranging (LiDAR) and vehicle-mounted sensors that are often computationally expensive and difficult to deploy at scale. This study aims to address the challenge of deploying a high-accuracy CNN on a resource-constrained edge device (Raspberry Pi 4B) by optimizing the balance between inference latency and detection sensitivity. By utilizing a depthwise separable convolution architecture, the system shows a 10% increase in vehicle window area identification accuracy while operating within a low-power envelope of less than 15 W. Experimental results demonstrate that the integrated curvature-based mathematical model improves anomaly detection sensitivity by 15% compared to traditional threshold-based triggers. The developed system reduces hardware expenses to 30% of conventional LiDAR-centric systems, maintaining a real-time inference latency of 120 ms and a packet loss rate below 2% at speeds of 60 km/h. These results establish a cost-effective, edge-intelligent solution for vehicle-road collaborative monitoring, increasing overall driver comfort and safety by 15%. Full article

With the rapid development of society, the number of road vehicles has increased significantly, leading to a growing severity of traffic accident issues. Timely and accurate detection of road traffic anomalies or accidents is crucial for reducing fatalities and alleviating traffic congestion. Consequently, the detection of road traffic anomalies has become a focal point of research in recent years. With the assistance of computer technologies such as deep learning, researchers have developed more accurate and effective methods for detecting road traffic anomalies. However, the small proportion of anomaly-prone areas in surveillance video frames, combined with the complex and difficult-to-capture patterns of accidents, presents new challenges for the application of deep models to traffic anomaly detection from a surveillance perspective. In light of this, this paper annotates the TADS dataset we previously proposed, a popular text-assisted video representation learning method, to develop a more efficient detection method. Utilizing the well-known video-text model CLIP, we have constructed a detection model that leverages unique text and eye-gaze annotation data from the TADS dataset to learn anomaly representations more effectively, thereby improving the detection of road traffic anomalies from a surveillance perspective. Experimental results demonstrate the superiority of our model for detecting traffic anomalies from a surveillance perspective, as well as the utility of the text and eye-gaze data included in the dataset. Full article

This study presents the first integrated magnetic and electromagnetic (EMI) survey of the Pianabella Basilica (Ostia, Italy), combining high-resolution magnetic gradient measurements with EMI mapping. The site, characterized by late-antique Christian architecture and funerary structures, provides a complex environment for testing non-invasive geophysical techniques. Magnetic data were acquired using the MagEx system (v.1.2.2558), a new prototype based on Micro-Fabricated Atomic Magnetometer (MFAM) technology, marking its first field deployment in archaeological prospection. Simultaneously, EMI measurements using the CMD-Mini Explorer provided data on apparent conductivity and in-phase components across three depth levels (0.5–1.8 m). The magnetic gradient map successfully delineated the Basilica’s planimetric outline, revealing anomalies (~20 nT/m) corresponding to masonry and internal enclosures. A significant anomaly (50–60 nT/m) north of the Basilica suggests a basalt-paved Roman road leading toward Porta Laurentina. EMI results corroborated these findings, with low-conductivity zones outlining walls and in-phase responses highlighting reused Roman building materials. Despite significant urban noise from a nearby railway and fences, this integrated approach enhanced interpretability and reduced ambiguity. These findings demonstrate the efficacy of next-generation magnetic gradiometry and EMI for high-resolution archaeological investigations, providing a new methodological benchmark for cultural heritage prospection. Full article

Rapid urbanization and increasing traffic volumes necessitate effective road safety measures, particularly in metropolitan areas. Enhancing road safety is a fundamental pillar of social sustainability as it directly reduces the socio-economic burden of traffic accidents and promotes resilient urban environments. This article analyzes the impact of infrastructural traffic-calming devices on road safety parameters using a GIS-based method. This study provides a quantitative tool for monitoring and measuring the effectiveness of sustainable transport infrastructure. The study examines six different types of devices across 44 locations within the GZM Metropolis, Poland, utilizing official police data (Accident and Collision Records System—SEWIK) from a period of two years before and two years after implementation. The primary parameters analyzed include the frequency of incidents, the severity of injuries, and the structure of accident types. The results demonstrate a substantial positive association following the interventions, with an average 41.33% reduction in road incidents across all tested devices. Specifically, speed bumps proved most effective, reducing incidents by over 66%. However, the analysis revealed a critical anomaly: While pedestrian refuge islands decreased the overall number of minor injuries, they correlated with an increase in the number of severe injuries, suggesting a need for careful consideration. Furthermore, the study confirms a positive shift in the structure of incidents, notably a substantial decrease in rear-end and side-impact collisions. The findings offer practical evidence for evidence-based urban policies, contributing to the development of safe, inclusive, and sustainable transport systems in line with global sustainability goals. Full article

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management. Full article

This article investigates a unique iconographic anomaly in late medieval Dunhuang silk paintings: the conflation of the bodhisattvas Mañjuśrī and Samantabhadra. Focusing on two key artifacts from the 9th and 10th centuries and tracing their legacy to later folk prints, this study argues the phenomenon is not a scribal error but a creative Anomaly—a deliberate ritual synthesis. The analysis reveals this synthesis was driven by two forces: a phonetic re-semanticization in the local dialect and a theological logic born from the integration of Huayan School doctrines with Esoteric ritual practice. The paper demonstrates how Huayan metaphysics were operationalized through condensed Esoteric invocations, turning the inscription into a functional ritual shorthand. Crucially, this study demonstrates the genealogical survival of this Silk Road variant in Ming and Qing dynasty woodblock prints. It uncovers a parallel, non-canonical lineage of visual piety, sustained through workshop copybooks rather than elite textual discourse. This trajectory challenges the linear narrative of Buddhist art history, highlighting the generative power of localized adaptations existing outside the purview of the written canon. Full article

The vehicular energy consumption, primarily determined by the vehicle’s characteristics, exhibits significant variations influenced by driving behavior, traffic, and road attributes, with repercussions for emissions. This paper presents experimental results from real-traffic runs to characterize the relationship between fuel consumption and these factors. Data on consumption, performance, and kinematics of a light-duty vehicle were obtained using low-cost devices, including an On-Board Diagnostics (OBD) scanner, a unit integrating an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) receiver. The data allowed distinguishing consumption patterns between two distinct scenarios: a collector road stretch with deteriorated pavement and an express road stretch with lower surface roughness. Relevant association was identified between fuel consumption and factors such as discrete pavement anomalies and variables related to driving and traffic. Moderate correlations were observed with slope, and weaker ones with pavement roughness. Regarding the regression analysis, results identified acceleration and engine speed as the primary operational determinants of fuel consumption, with road grade emerging as the dominant geometric constraint across all scenarios. The results reveal relevant associations between fuel consumption and road, driving, and traffic-related factors while simultaneously demonstrating a robust and replicable experimental methodology based on commercially available sensing devices for real-traffic energy and emission assessments. Full article

Semi-automated urban rail transit systems still rely on human intervention during safety-critical events, yet emergency takeover performance has received far less attention than in SAE Level-3 road automation. This study focuses on the reaction phase of emergency takeover, defined as the interval from anomaly onset to the train operator’s first control action. We propose a conditional two-stage evaluation framework that jointly assesses event recognition and control execution quality. A simulation-based experiment was conducted to replicate GoA2 operating conditions under controlled emergency scenarios. Three indicators were extracted: (i) event recognition accuracy derived from eye-tracking and retrospective recall, (ii) takeover reaction time, and (iii) initial action accuracy reflecting compliance with operational speed or braking limits. An attention-enhanced multilayer perceptron (MLP) was developed to dynamically weight input features and improve interpretability. The proposed model achieved stable subject-wise performance, with an average accuracy of 0.86 and a macro F1-score of 0.857. These results support the feasibility of interpretable learning-based evaluation for human-in-the-loop safety assessment and provide practical implications for improving operator readiness monitoring and operational safety management in semi-automated metro systems. Full article

Despite widespread adoption, Electronic Medical Record (EMR) systems remain limited in providing intelligent clinical decision support, particularly for early detection of patient deterioration. We present MedROAD V2 (Medical Records Organization, Analysis, and Display), an open-source EMR that integrates AI-driven physiological analysis with comprehensive patient management. The system combines continuous vital sign monitoring and laboratory data using an ensemble of the following four complementary machine learning models: gradient boosting for supervised prediction, isolation forests for anomaly detection, autoencoders for pattern recognition, and Long Short-Term Memory networks for temporal modeling. A novel framework couples these predictions with a large language model (Claude AI) to generate explainable differential diagnoses grounded in medical literature. Validation on the MIMIC-IV database demonstrated excellent 12 h deterioration prediction. MedROAD demonstrates that combining quantitative prediction with natural language explanation can enhance clinical decision support while extending quality care to populations that would otherwise lack access. Full article

Gabal (G.) Suwayqat El-Arsha contains two distinct phases of granitoids: I-type granodiorite and A-type monzogranite. Both of them experienced intense fractional crystallization that affected plagioclase, alkali feldspar, quartz, and, to a lesser degree, ferromagnesian minerals. EnMAP hyperspectral data were used to discriminate between the different granitoid types through spectral analysis, using various techniques, including the Sequential Maximum Angle Convex Cone (SMACC) method. Granodiorite has high SiO₂ (68.21–71.44 wt%), Al₂O₃ (14.29–14.92 wt%), Fe₂O₃ (1.99–3.32 wt%), and CaO (2.34–3.87 wt%), whereas monzogranite has even higher SiO₂ (73.58–75.87 wt%) and K₂O (4.28–4.88 wt%). Both granodiorite and monzogranite exhibit calc-alkaline, peraluminous to metaluminous, and medium- to high-K characteristics, with attendant enrichment of light REE and LILE and depletion of heavy REE and HFSE. A negative Eu anomaly may indicate early plagioclase fractionation, especially in the monzogranite. The I-type granodiorite is likely derived from a high-K, mafic protolith that partially melted during lithospheric delamination, leading to severe fractional crystallization in the upper crust in a post-collisional environment. In contrast, the monzogranite exhibits A-type characteristics and was likely emplaced in an anorogenic setting. Both granites were affected by several episodes of hydrothermal alteration, resulting in silicification, kaolinitization, sericitization, and chloritization. The intrusions studied here exhibit key similarities with those in the Wadi El-Hima area, including tectonic setting, petrogenetic type, Neoproterozoic age (Stage I collisional: ca. 650–620 Ma; Stage II post-collisional: ca. 630–590 Ma), and mineralogical assemblages (notably two-mica granites). These correlations suggest that both suites form part of a regionally extensive batholith composed of I- and A-type granites, stretching from north of the Marsa Alam Road (Umm Salatit–Homrit Waggat) southward to at least Wadi El-Hima. Full article

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms. Full article

Effective anomaly detection in vehicle trajectories is crucial for developing sustainable and safe urban transportation systems. However, current research faces three main challenges including scarce anomaly data, inadequate spatial feature extraction in complex road networks, and limited capability in identifying complex behaviors. To address these issues, this paper proposes a Multi-scale Temporal and Road Network Interaction Anomaly Detection model (MTRI). Our framework leverages a Contrastive Learning-based Conditional Diffusion Model (CL-CD) to generate synthetic anomalous trajectories across diverse scenarios. It then employs an Urban road Network Interaction Modeling model (UNIM) to capture the profound interactions between trajectories and the road network. Finally, a Long-Short Temporal Anomaly Detection model (LSTAD) is designed to learn multi-scale temporal features for detecting sophisticated anomalies. Extensive experiments on real-world datasets from various urban scenarios demonstrate the superiority of our approach, which achieves high accuracy and adaptability (AUC-ROC > 0.85). This work contributes to sustainable urban mobility by providing a reliable solution for enhancing road safety through proactive anomaly detection. Full article

Traffic flow anomalies represent significant deviations from normal traffic behavior and disrupt the smooth operation of transportation systems. These may appear as unusually high or low traffic volumes compared to historical trends. Unexpectedly high volume can lead to congestion exceeding usual capacity, while unusually low volume might indicate incidents like road closures, or malfunctioning traffic signals. Identifying and understanding both types of anomalies is crucial for effective traffic management. This paper presents a clustering based approach for enhanced characterization of anamolies in traffic flows. Anomalies in traffic patterns are determined using three anomaly detection techniques: Elliptic Envelope, Isolation Forest, and Local Outlier Factor. These anomalies were newly detected in this work on the Montréal dataset after preprocessing, rather than directly reused from earlier studies. These methods were applied to a dataset that had been pre-processed using windowing techniques with different configuration settings to enhance the detection process. Then, to leverage the detected anomalies, we utilized clustering algorithms, specifically k-means and hierarchical clustering, to segment these anomalies. Each clustering algorithm was used to determine the optimal number of clusters. Subsequently, we characterized these clusters through detailed visualization and mapped them according to their unique characteristics. This approach not only identifies traffic anomalies effectively but also provides a comprehensive understanding of their spatial and temporal distributions, which is crucial for traffic management and urban planning. Full article