Search Results (158)

Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recurrent bottlenecks. This study proposes an NRC detection framework based on a Physics-Informed Neural Network (PINN) that embeds the Lighthill–Whitham–Richards (LWR) conservation law into the learning process to construct a physically consistent baseline of normal traffic states. The traffic flow physics is represented by a two-regime fundamental diagram combining the Greenshields model for free-flow conditions and the Underwood model for congested conditions, and the network is trained by minimizing a composite loss that adaptively balances the data fitting error against the LWR residual. NRC is then detected when the observed density exceeds the PINN-estimated baseline density beyond a tolerance threshold of 150%. The framework was evaluated on a 12 km segment of the Seoul Ring Expressway in Korea using six months of 15 min data collected from seventeen sensor stations. The results show that the proposed model reliably isolates NRC events from recurrent peak-period congestion. From the perspective of symmetry, the framework interprets recurrent traffic as a temporally symmetric background state governed by a conservation law, and non-recurrent congestion as a local breaking of this symmetry, which the physics-constrained residual is designed to expose. The key contribution of this study is a theoretically grounded, label-free anomaly detection approach that couples machine learning with traffic flow theory, offering traffic management centers an automated and interpretable tool for incident detection and response. Full article

The regulatory disparity in nautical chart carriage requirements between SOLAS and non-SOLAS vessels poses a systemic risk for maritime safety. This study analyzes maritime traffic and grounding accidents in the East Adriatic Sea to examine the empirical patterns associated with this risk and to quantify this risk. Analysis of 2,699,930 vessel arrivals in Croatian ports (2012–2020) reveals that non-SOLAS vessels dominate traffic, outnumbering SOLAS vessels by factors of 3.44 (cargo) and 13.92 (passenger). Correspondingly, over 92% of 170 recorded groundings (2017–2019) and 90.6% of 202 groundings (2020–2025) involved non-SOLAS ships, predominantly during favourable weather conditions. The core problem is identified as the desynchronization in disseminating Maritime Safety Information (MSI) across official and unofficial chart systems, leading to inconsistent situational awareness. This risk is formalized through a conceptual objective function modelling the time delay in MSI updates across different chart production chains. These quantitative metrics demonstrate that the current regulatory gap exposes the majority of maritime users to a reduced safety standard, directly affecting risk management at the operational and regulatory levels. To minimize this delay and enhance navigational safety, the paper proposes a dual-pathway amendment: extending the ECDIS/ENC mandate to all SOLAS ships and establishing a new framework mandating the use of official ENC data within Electronic Chart Systems (ECS) for non-SOLAS vessels. This study is intended as a system-level analysis of regulatory and informational asymmetry rather than as a full accident-causation investigation. The findings should therefore be interpreted as exploratory and policy-relevant, with caution regarding direct causal inference for individual incidents. The proposed measures are essential to synchronize critical maritime safety information across all maritime users. Full article

Traditional wet markets are critical components of Indonesia’s urban food system, yet remain largely unexamined from an occupational health and safety (OHS) perspective. This study identified and prioritized OHS risks among vendors and visitors at Nanggalo Market, a high-density traditional market in Padang, West Sumatra. This was an exclusively qualitative, descriptive, non-probability (purposive) study; no actual occupational accidents, injuries, or exposure incidents were evaluated, and risk levels reflect participants’ perceptions together with researchers’ triangulated observations rather than objective incident records. An observational qualitative design combined elicitation surveys with 45 participants (20 vendors, 20 visitors, 5 market managers), direct observation, and in-depth interviews with 7 key informants. Risk assessment followed the AS/NZS 4360:2004 matrix, classifying risks by likelihood and consequence severity based on participant- and researcher-perceived evidence rather than recorded incident data. Ten potential hazards were identified across six activity zones: traffic and parking; buying and selling; culinary activities; building structures and floor conditions; security and stray animals; and emergency access. Seven risks were classified as high-level, including slipping on wet floors, lack of evacuation routes, lack of fire extinguishers, narrow circulation paths, poor toilet conditions, crowding, and traffic injuries, while three were moderate. All high-level risks lacked adequate controls. Recommended interventions span the full hierarchy of controls, prioritizing engineering and administrative measures, and propose community-based occupational health posts as a sustainable mechanism. These findings provide an evidence base for strengthening OHS governance in traditional wet markets across Indonesia and comparable low- and middle-income country settings. Full article

Background: Postoperative haemorrhage (POH) is a rare but potentially life-threatening complication of thyroid surgery, with an incidence of 0.6–4%. Early identification of patients at increased risk is critical to guide perioperative management, especially in the context of evolving surgical practices and increasing demand for outpatient procedures. Methods: We conducted an explorative, retrospective, single-centre case–control study using a prospectively documented quality assurance dataset including 9158 thyroidectomies (2012–2019). POH requiring revision (n = 104) were compared to matched controls (n = 416; 1:4 ratio), matched by age, sex, type of procedure (uni- vs. bilateral), and year of surgery. Univariate analysis (Chi-square and t-test) was used to identify possible associations between candidate risk factors and POH. To supplement classical univariate statistics, we applied a Random Forest machine learning model to assess the relative importance of 25 potential variables derived from the clinical dataset based on previous literature. It was also planned to use the results to develop a proposal for a quantitative risk score, system, assigning weights to each factor (3, 1, or 0 points) depending on their relative importance for predicting POH. Patients were subsequently categorized into risk classes (low, intermediate, high) based on total point scores and reclassified. Results: High-impact risk factors confirmed in univariate analysis and Random Forest modelling included reoperative thyroidectomy, smoking, relevant comorbidities, medical treatment for hyperthyroidism and advanced age and were weighted with 3 points. Moderately associated variables such as regular alcohol consumption, Graves’ disease, hyperthyroid state at surgery, duration of the procedure and thyroid weight were weighted 1 point. Factors with negligible predictive value (e.g., BMI, gender, ASA classification) were assigned 0 points. The average score among patients without haemorrhage was 5.21, whereas the average score among patients with haemorrhage was 7.61. Within the matched study cohort, patients with POH accumulated higher risk scores than controls, suggesting potential discriminatory capacity. These findings formed the basis for the development of an exploratory three-stage ‘traffic light’ risk stratification model that requires external validation. Conclusions: A simple, interpretable point-based scoring system derived from a large matched case–control cohort identified key predictors of postoperative hemorrhage (POH) after thyroid surgery and enabled risk stratification within the study population. By combining conventional statistical methods with machine-learning approaches, the score may support individualized perioperative monitoring, surgical planning, and institutional resource allocation. However, because the model was developed using a 1:4 matched case–control design, it should be considered an exploratory risk stratification tool rather than a fully validated prediction model, and it does not directly estimate absolute POH risk or population incidence. External and prospective validation in large, representative multicentre cohorts (e.g., StuDoQ, HEDOS) is ongoing and will be required to establish calibration, generalizability, and clinical utility. Full article

Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22.1% reduction in average vehicle delay ($p<0.001$), while microscopic simulations confirmed up to 28.0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53.5–72 kg of CO₂ avoided per day, alongside annual fuel savings of 8430 L. Assembled within a $1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies. Full article

Background/Objectives: Primary care clinics increasingly receive urgent and semi-urgent requests from patients who may otherwise attend emergency departments or urgent care centres when timely appointments are unavailable. This article describes and evaluates the Cranston Ridge Medical Clinic Registered Nurse Prescriber-led Triage–Treatment–Continuity model in Calgary, Alberta, Canada. Methods: The manuscript is reported as a single-clinic practice innovation and service evaluation using aggregate, non-identifying operational data from 1 April 2025 to 31 March 2026. The model combines medical office assistant emergency recognition, RN prescriber-led stability assessment, traffic-light urgency classification, a booking-contingency algorithm, clinical support tools, diagnostic test ordering and prescribing within authorized scope, safety-netting, and communication with the patient’s primary care provider through the electronic medical record. Results: During the evaluation period, 5032 pathway contacts were managed. Of 5030 stable contacts assigned traffic-light categories, 4950 (98.4%) were Code Red same-day contacts, 55 (1.1%) were Code Yellow 24–48-h contacts, and 25 (0.5%) were Code Green non-urgent contacts. Two contacts triggered EMS/911 activation before traffic-light classification. Following RN prescriber assessment, 9 emergency department referrals, 2 urgent care referrals, 85 primary care provider follow-up appointments, and 5 patient refusals were recorded; no safety incidents or complaints were recorded in the aggregate monitoring dataset. A CIHI-informed 15% reference scenario corresponds to approximately 755 potentially avoided ED/UCC visits, but no confirmed diversion or monetary savings are claimed. Conclusions: The model reframes triage as an integrated primary care intervention that combines assessment, treatment, escalation, and continuity. Further ethics-approved research is required to evaluate patient-level outcomes, safety, confirmed health-system utilization effects, stakeholder experience, and cost-effectiveness. Full article

Road traffic crashes remain a major global public health and economic challenge, with heavy vehicle drivers disproportionately involved in severe incidents, particularly in low- and middle-income countries. In Ethiopia, limited access to continuous professional training constrains efforts to improve drivers’ safety-related knowledge and awareness. This study explored the impact potential and user acceptance of gamified e-learning modules designed to enhance heavy vehicle drivers’ knowledge and awareness of fatigue management, speed-related behavior, and eco-driving practices. A randomized pretest–post-test control-group design was employed, in which professional drivers were assigned to either an intervention group that completed three gamified e-learning modules or a control group that received no training. Data were analyzed using mixed repeated-measures analysis of variance. The results revealed significant time × group interaction effects across all domains (p < 0.001), with substantially greater improvements in the intervention group and large effect sizes. Participants also reported high perceived usefulness, behavioral intention, and trust in the system. These findings provide preliminary evidence that gamified e-learning may be a feasible and promising approach for improving short-term safety-related knowledge among professional heavy vehicle drivers. Further research is needed to determine whether these improvements are sustained over time and translate into behavioral change and measurable road safety outcomes before broader implementation can be recommended. Full article

Urban traffic networks under high-demand and incident-like perturbations can evolve from stable operation to cascading congestion, increasing delay, stop-and-go traffic, fuel or energy consumption, and traffic-related emissions. These effects make congestion regulation an important component of sustainable urban traffic management. Existing signal control methods still focus mainly on local delay reduction or short-horizon response, limiting their ability to regulate congestion propagation and stress-induced network degradation. This paper proposes Mamba-PTC, a coordinated reinforcement learning framework for urban signal control in heterogeneous traffic networks. The framework combines centralized multi-intersection control with a simplified Mamba-style sequence encoder and a transition-aware objective optimized by PPO. To connect control with network-level traffic dynamics, we introduce a transition risk indicator for online regulation and macroscopic observables for evaluation, including a composite congestion measure and an instability-amplification proxy. Experiments on stressed heterogeneous urban networks show that Mamba-PTC improves the throughput–duration profile while reducing congestion degradation indicators under heavy load and perturbation. Matched control comparisons, ablation analysis, and cross-network validation further show that these gains arise from the joint effect of temporal representation, transition-aware objective design, and coordinated control. The results suggest that coordinated reinforcement learning can support sustainable network operation by regulating congestion growth in stressed urban traffic networks. The findings provide a basis for designing congestion-aware signal control strategies, robustness evaluation protocols, and future intelligent traffic management systems for stressed urban networks. Full article

The expected rise in space operations challenges the European Air Traffic Management (ATM), as traditional static airspace segregation causes operational inefficiencies. To mitigate this, a new function within the European Network Manager Operations Centre (NMOC), supported by the novel Network Real-time Mission Monitoring (N-RMM) tool, and complemented by ad hoc Debris Response Areas (DRAs), are being developed. This paper introduces the safety assessment of this approach using the Expanded Safety Reference Material (E-SRM) methodology. By developing specialised Accident Incident Models (AIMs) for mid-air collisions with space debris, we quantify safety barrier efficiencies and define a Risk Classification Scheme (RCS). The results indicate that by developing dedicated AIMs for the proposed dynamic airspace-management concept, the derived safety criteria, under the stated assumptions, are compatible with the targeted safety thresholds. The potential reduction in segregated airspace volume and duration remains an expected operational benefit to be quantified in subsequent validation work. Full article

The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveillance cameras at intersections enable continuous traffic monitoring and rapid incident detection, optimizing signal timing to improve road safety and reduce traffic congestion and travel delay. These cities present new challenges for the police force, forcing them to blend into the environment. The paper proposes novel hierarchical Police Internet of Things (PIoT) concepts that should enable and secure timely, high-priority policing forecasting and decision-making processes in smart cities. Hierarchical edge, fog, and cloud computing were presented according to the police decision-making process. This concept is carefully developed to improve the timeliness of predictive policing, planning, management, and decision-making using artificial intelligence and fuzzy logic. The proposed vertical PIoT concept is supported by vertical data processing. In hierarchical computing, machine learning models for time series prediction and fuzzy-logic-based decision-making are applied to enable comprehensive analysis in a smart environment. Two case studies dealing with crime and traffic issues are presented in detail. Full article

Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article

Urban infrastructure works conducted under live traffic conditions often face a persistent gap between approved traffic management plans and their actual field implementation. This gap remains underexplored in longitudinal studies, particularly in utility projects from low- and middle-income urban contexts. This study evaluates a risk-based supervisory approach that integrates daily monitoring of the Traffic Management Plan (TMP) with a corporate risk management framework aligned with ISO 31000. The dataset includes 288 supervised workdays over 16 months (November 2023–February 2025), 99 non-conformity tickets, 96 signal-theft events (137 units), and seven traffic incidents. The analysis combines descriptive statistics, hypothesis testing, logistic regression, segmented longitudinal analysis, count models, response-time evaluation, and a composite risk index. TMP non-compliance decreased from 18.8% to 6.9% between the first and second halves of the study period ($p=0.0028$). The odds of non-compliance were significantly higher during the staff transition period in April–May 2024 (OR = 3.50; 95% CI: 1.24–9.82), while day and night shifts showed comparable rates. Monthly patterns indicate that staff instability and signal theft contributed to non-compliance levels, and ticket resolution remained slow (mean response time: 69.9 days). These findings highlight the importance of supervisory continuity, contractor stability, and timely corrective actions in improving work zone safety. Full article

Effective incident management is a cornerstone of transportation system performance, influencing roadway clearance times (RCTs) and the risk of secondary crashes. This study investigated how towing regulations involving large commercial vehicle crashes and jurisdictional variations affect the management of large-vehicle crashes, focusing on the relationship between regulatory frameworks, incident duration, and secondary crash occurrence with the state of Tennessee as a case study. The objective was to determine whether differences in towing policies, operational mandates, and rural/urban contexts lead to measurable changes in clearance efficiency. A multi-year dataset of more than 770,000 traffic incidents and 4400 towing-involved large-vehicle crashes from 2017 to 2022 was analyzed. Statistical methods, including two-sample testing and hazard-based survival modeling, were applied to evaluate the impact of towing regulations and operational protocols on roadway clearance and secondary crash patterns. The results consistently showed that strong performance-based towing regulations, such as mandated maximum response times and standardized training and equipment requirements, were associated with significantly lower average RCTs. Jurisdictions with enforced rapid-response mandates achieved average clearance durations of approximately 120–130 min, even under high incident volumes, compared to over 150 min in areas without performance benchmarks or with more complex procedural requirements. A pronounced rural–urban divide was observed, with incidents outside urbanized areas averaging 30–40% longer clearance times, largely due to limited towing resources, longer dispatch distances, and less stringent regulatory enforcement. Secondary crash analysis identified that more than 90% of secondary collisions were linked to crashes requiring towing, with the majority occurring within 20 min and 0.5 miles of the primary incident, underscoring the direct connection between delayed clearance and safety risk. These results carry direct implications for transportation policy and incident management practice by providing empirical evidence that standardized, performance-based towing regulations can meaningfully reduce RCTs and secondary crash risk, particularly when paired with investments in rural towing infrastructure Full article

Traffic incident impact prediction remains challenging for intelligent transportation systems due to complex spatiotemporal dependencies. This study analyzes 38,430 real-world traffic incidents from Istanbul (2022–2024) to predict normalized traffic deviation $\Delta Traffic(\%)$ using machine learning with rigorous temporal validation. Three models—Random Forest (RF), XGBoost, and LightGBM—were evaluated using rolling-origin cross-validation (2022 training, 2023 testing; 2022–2023 training, 2024 testing) to prevent temporal leakage, employing a strictly operational 13-feature set that excludes information unavailable at incident onset (t₀). LightGBM achieved MAE = 26.81 ± 1.94% and R² = 0.506 ± 0.042 (mean ± std across folds) with 95% bootstrap confidence intervals of [27.54%, 28.81%] for MAE on the 2024 test set, significantly outperforming historical baselines (R² = 0.100 ± 0.054, p < 0.001, Bonferroni-corrected). Feature ablation studies revealed that temporal features contribute 65.2% of predictive power, while incident type contributes only 1.3%. Distributional robustness analysis confirms conclusions are stable across distributional treatments (log, winsorised, quantile), with feature importance rank correlations ρ = 1.000 between all treatment pairs. This work provides empirical evidence for context-aware traffic management systems and demonstrates the importance of proper temporal validation in transportation forecasting. Full article