An exploration of active safety technologies for commercial vehicles: Research on on-road driving risk identification based on alarm and other multi-source data.

Causal analysis of ship inspection data and maritime accidents through causal neural networks

Comprehensive safety analysis of anticipated transient without scram in an integrated marine reactor

A forensic investigation and simulation-based analysis of a chemical plant explosion accident.

Workplace accidents represent a significant health problem for workers. In 2022, the EU recorded over 3,000 workplace deaths and nearly 3 million non-fatal accidents. The aim of this paper is to provide an in-depth analysis of multiple fatal accidents in Italy in recent years, analyzing their occurrence patterns and causal factors to increase useful information for risk management. The statistics come from the INAIL Database and the Infor.MO National Surveillance System, which collects detailed information on accident dynamics and uses a multifactorial model to analyze the causes of injuries. In this system, the in-depth analysis of multiple fatal accidents was conducted on a cluster of 181 events that occurred between 2008 and 2022. Data sources show that road accidents are a significant cause of multiple injuries. Other recurring accidents include collapses, fires, explosions, and asphyxiation in confined spaces. The construction and manufacturing sectors are the most affected. The main causes, highlighted particularly in the Infor.MO system, include poor worker training, inadequate workplace organization, and a lack of safety devices. To reduce workplace accidents, especially those with dramatic consequences such as multiple accidents, the importance of a stronger organizational safety culture is reiterated, in which training enables workers to understand risk analysis and gain greater awareness of hazards.

This paper presents a comprehensive safety analysis of the NuScale Small Modular Reactor, a next-generation nuclear technology. The NuScale design emphasizes enhanced safety, flexibility, and efficiency, achieved through a natural circulation cooling system and robust passive safety functions. The analysis is conducted using the RELAP5/SCDAPSIM3.4 system code for high-fidelity thermal-hydraulic simulations. The first part details the steady-state qualification of the reactor model, a crucial step to establish a reliable baseline for normal operation and subsequent accident analyses. The study confirms the model's accuracy against key design parameters including temperature distributions, coolant flow rates, and system pressure validating its predictive capability. Building upon this qualified model, the second part investigates a severe beyond-design-basis accident scenario. The initiating event is the inadvertent opening of one Reactor Vent Valve, leading to an inside containment Small Break Loss of Coolant Accident. This breach is compounded by the postulated total failure of both the Emergency Core Cooling System and the Decay Heat Removal System, creating a fully unmitigated scenario. The progression of this severe accident, simulated for a duration of 500 s, critically assesses the reactor's passive safety features and inherent resilience. The system's response is analyzed in detail, with key findings showing a consistent behavior comparable to the results of similar scenarios documented in the literature.

A driving visibility index for evaluating urban road driving safety based on point cloud data.

Large language model-driven knowledge graph and intelligent analysis for coal mine accidents

Leading government Safety Investigation Authorities (SIAs) that investigate major aviation accidents are members of the International Transportation Safety Association (ITSA). Investigation analysis methodology details are rarely included on SIA websites, in final accident reports, shared with peers, or specified by the International Civil Aviation Organization (ICAO). Some 'New View' safety researchers assume SIAs don't use systemic methodologies and that accident investigation has little value. Because of the sensitivity of SIA work and need to prioritize safety value from available investigation funding, there has been little academic research on choice and use of investigation analysis methodologies to address these issues supported by SIAs or involving multiple SIAs. Investigation analysis methodologies were defined broadly to include SIA-used academic models and 'bespoke' investigation processes. Over several years we gained the trust of 12 ITSA SIAs to provide written and interview data to address four research questions. NVivo12 software supported interview thematic analysis. Ten SIAs reported using Rasmussen-based methodologies, 7 Reason-based, 6 own bespoke methodologies, 5 BowTie, 5 more recent systemic methodologies such as CAST and FRAM, and 10 SIAs used other methodologies such as SHELL, 6M, and bespoke methodologies from other ITSA SIAs. Multiple investigation analysis methodologies were used by all SIAs, sometimes in the same investigation. In contrast with aging ICAO publications and negative New View claims, the SIA research data illustrates choice and usage of complex systemic and less complex analysis methodologies in varying investigative contexts. The research is being used by the 12 SIAs to better understand, and draw from, each other. It has broad relevance for updating ICAO documentation that is now underway, for other SIAs and the wider aviation community, and for other high-risk industries that seek to understand professionally operationalized methodologies used for investigation, analysis, and safety action with associated learning.

Analysis of heavy vehicle accidents is critical for traffic safety, as such crashes often result in serious injuries, fatalities, material damage, traffic disruptions, and economic losses. This study proposes an integrated approach combining geographic information systems (GIS), multi-criteria decision-making (MCDM), and statistical analysis to examine heavy vehicle accidents in Türkiye between 2015 and 2019. The methodology includes: (i) examining accident records and identifying 21 independent variables and 93 sub-variables, (ii) calculating their priority values using the fuzzy Logarithm Methodology of Additive Weights, (iii) conducting GIS-based spatial analyses, and (iv) applying binary logistic regression to evaluate accident outcomes as injury or fatality. The results indicate that road, environmental, accident-related, and driver-related factors significantly affect crash severity. Fuzzy LMAW results show that driver violations have the greatest negative impact. Spatial analyses reveal that severe crashes are mainly concentrated in western, north-western, south-western, and central Türkiye.

Mountain activities have become increasingly popular in recent years, leading to a growing number of mountain rescue missions. Despite this trend, detailed epidemiological analyses of mountain accidents in the Polish Tatra Mountains are lacking. The aim of this study was to describe the epidemiology of mountain rescue missions in the Polish Tatra Mountains, with particular emphasis on the severity of the case mix of injuries and illnesses and their implications for rescue response. A retrospective analysis of prospectively collected data from the Tatra Mountain Rescue Service (TOPR) covering the period of 2015-2024 was conducted. Demographic, epidemiological, and operational data were extracted from the electronic rescue registry. The severity of the patient's condition was classified using the National Advisory Committee for Aeronautics (NACA) score. The population at risk was estimated on the basis of the number of ticket entries to Tatra National Park during the study period. Seasonal differences in rescue incidence and severity were analysed, and absolute risk increase (ARI) and number needed to harm (NNH) were calculated. A total of 9,477 patients were included in the final analysis. The median age of the rescued individuals was 36 years (IQR 25-48); 48% (n = 4,539) were women, and 10% (n = 998) were minors. The median NACA score was 2 (IQR, 0-3), while 38% of the patients were classified as having a NACA score ≥ 3. Deaths occurred in 2% of the rescue missions. The median duration of rescue missions was 1.75h (1.25-2.75). For every 100,000 visitors to the Tatra National Park, approximately 24 rescue missions were performed, including one involving severe injury or illness (NACA ≥ 4). Severe cases were significantly more frequent during the winter season (6.3% vs. 4.7%; p = 0.001). Helicopters were used in approximately one-third of the missions, and their use was correlated with higher NACA scores. Mountain rescue missions in the Polish Tatra Mountains were associated with seasonal patterns of mountain activities and the number of visitors to Tatra National Park. Systematic assessment of a patient's severity using the NACA score may support decision-making and resource allocation in mountain rescue. Epidemiological analyses based on comprehensive rescue databases may contribute to improved planning, training, and organization of medical search and rescue systems in mountain environments.

This study presents a comprehensive analysis of road accidents in India by using official government data sets from 53 major cities across 28 states. This research is conducted under a Type-2 picture fuzzy environment with advanced aggregation operators such as the arithmetic mean, geometric mean, hamy mean, Einstein weighted operator, and Aczel-Alsina operator. To enhance decision-making under uncertainty, a novel distance measure is proposed and theoretically validated. Five prominent distance-based multi-criteria decision-making (MCDM) methods such as TOPSIS, VIKOR, WASPAS, CODAS, and COPRAS were applied to rank the cities based on 35 critical road accident-related criteria. The results of the model revealed that Chennai consistently secured the top ranking in TOPSIS, COPRAS, and FIR. Amritsar also performed exceptionally, ranking 1st in VIKOR and achieving 3rd in TOPSIS. In contrast, cities such as Kanpur, Lucknow, and Ahmedabad consistently ranked in the bottom quartile, with Kanpur receiving the lowest position (53rd) in TOPSIS and FIR, indicating they are in critical risk zones. Mid-ranked cities such as Hyderabad, Pune, and Rajkot showed variations across methods but maintained stable FIR positions. To validate the robustness of the rankings, a comprehensive sensitivity analysis was conducted by various decision parameter q, confirming the stability of rankings under fluctuating conditions. The proposed framework improved the precision of accident severity and demonstrated adaptability to broader decision-making scenarios involving uncertainty. This study contributes a novel, integrated, and mathematically rigorous methodology for assessing urban safety for policymakers to reduce the road accident risks across Indian cities.

MARVEL is an 85-kW thermal microreactor with a 36-pin three-layer hexagonal patterned core being developed and deployed at the Idaho National Laboratory. Its reactivity control system (RCS) consists of two separate systems of different design, functionality, and technology. The first is the primary means of control and safety-related shutdown, called the control drum (CD) system, which employs four rotating CDs that are evenly distributed around the peripheral of the reactor core. The second is the defense-in-depth central insurance absorber (CIA), which consists of a translating central control rod operating in a port located in the core’s center. As is conventional, the systems adjust the position of their reflecting and absorbing materials relative to the core to control reactivity. MARVEL’s RCS system is designed around specific operating requirements, which were established by thorough hazard evaluation and accident analysis. This drives the requirements for relative positioning and the consequential operating limits, such as maximum speed and sufficient position resolution, as well as scram demands such as maximum duration compliance and minimized impact. MARVEL’s drum and rod subsystem components and characteristics have been designed to satisfy the enveloping form of these demands as verified through finite element analysis and the mathematical computation of each system’s driving nonlinear second-order differential equation form. Iterative prototyping and testing were also performed to give intermediate assurance that the designs and their supporting analyses matched physical behaviors. A regimented qualification plan under continual maturation during the prototyping process pitted the evolving prototypes’ performance against the requirements to drive the design to an acceptable state. The resulting final designs’ critical characteristics are propagated into rigorous procurement and assembly requirements, satisfied via the execution of vendor specifications, in-house commercial-grade dedication plans, and detailed supplemental work instructions. These systems will be employed as a tool during assembly to intermittently verify that the RCS has retained its functionality after each subsystem’s inclusion, thus providing additional assurance that the new subsystem has been sufficiently incorporated. These efforts have culminated in system designs that are robust and have a unique rapid tunability to meet the system demands. Tunability is a critical capability, as the final system’s physical demands are nonconfirmed during initial startup and can shift during operation and throughout the system’s lifetime. Though developed for the MARVEL reactor, the presented CD and CIA system designs and methods can be leveraged in whole or in part for the benefit of the reactor development community.

This study develops a probabilistic dynamic risk assessment framework for grounding and collision/contact accidents in narrow waterways by integrating the Human Factors Analysis and Classification System for Passenger Vessels (HFACS-PV) with Bayesian Networks (BN). Marine accident reports from the Dover Strait (2004–2020) were systematically analysed to identify human, organisational, technical, and environmental risk factors, which were subsequently structured into a Bayesian Network to model their interdependencies and dynamic influence on accident occurrence. Conditional probability tables were derived from accident data and supplemented through structured expert elicitation. The resulting model enables real-time inference and predictive risk estimation under evolving operational conditions. Model performance was evaluated using detailed grounding and collision case studies, demonstrating its capability to replicate accident evolution and quantify the contribution of key causal factors. The results indicate that unsafe acts, particularly decision-based and perceptual errors, combined with deficiencies in voyage planning, supervision, and situational awareness, dominate accident causation in the Dover Strait. The proposed framework provides a quantitative decision-support tool for vessel traffic services and maritime operators, supporting proactive risk mitigation and safety optimisation in high-density and constrained navigational environments. • Analysis of Dover Strait grounding and collision accidents using HFACS to identify human and organisational causes. • A novel dynamic risk model enables real-time assessment of collision, contact, and grounding risks for operators. • The model supports risk control measures and optimises best practices to improve safety in narrow waterways.

High-fidelity transient neutronics/thermal-hydraulics coupling analyses of control rod ejection accident in a prismatic gas-cooled reactor core

Accident analysis of CO₂ pipeline failures: Patterns, detection, and consequences

Abstract— This project presents the design and implementation of an IoT-enabled smart vehicle black box system aimed at enhancing road safety, accident analysis, and real-time monitoring. The system integrates sensors, a microcontroller, and wireless communication modules to continuously capture critical parameters such as vehicle speed, location, acceleration, and impact force. In the event of an accident, the system automatically records and transmits essential data to a cloud platform, enabling quick access for emergency response and post-incident investigation. Additionally, features such as GPS tracking, driver behaviour monitoring, and remote data access improve vehicle security and accountability. The proposed solution offers a cost-effective, reliable, and scalable approach to modern vehicular data logging, contributing to smarter transportation systems and improved safety outcomes. Keywords— IoT, Smart Vehicle System, Black Box, Accident Detection, GPS Tracking, Data Logging, Real-Time Monitoring, Vehicle Safety, Cloud Storage, Sensor Integration, Emergency Alert System, Telematics

The article presents an analysis of the current safety level in the operation of the linear section of main pipelines. The strategic importance of the pipeline system for the country's energy security is considered, with its total length in Russia exceeding 257,4 thousand km. The composition and design features of the linear section are examined, including underground laying, shut-off valves, crossings under obstacles, and electrochemical protection systems, which are a mandatory condition for pipeline durability. Special attention is paid to the current regulatory framework, in particular, the new Safety Rules for Hazardous Production Facilities of Main Pipelines, which came into force in 2021 and tightened requirements for the placement of facilities, taking into account the risk of spread not only of liquid hydrocarbons but also of toxic clouds. Based on statistical data, the main causes of accidents are analyzed, with corrosion (26 %), defects in construction and installation works (25,8 %), and mechanical damage (21 %) being the leading factors. The large-scale consequences of ruptures, including volumes of gas losses, are also considered. In conclusion, modern approaches and promising directions for improving safety are presented, such as the implementation of automatic monitoring systems, in-line diagnostics using «intelligent» flaw detectors, and the use of new materials. The necessity of a comprehensive approach, integration of efforts of all participants, and adequate funding of modernization programs to achieve an acceptable level of safety for this critical infrastructure is concluded. An accident analysis revealed that corrosion, construction and installation defects, and mechanical damage remain the key causes of failures, accounting for the majority of incidents. This highlights the need for enhanced quality control during construction and repair, improved electrochemical protection and insulation systems, and stricter safety zones. The age of a significant portion of the pipeline network also significantly impacts reliability, necessitating the priority modernization of the most deteriorated sections. A review of the regulatory framework reveals that the Safety Rules for Main Pipelines, effective since 2021, reflect a shift to a risk-based approach. Significantly expanding requirements for facility design and placement, consideration of toxic cloud propagation scenarios, and the introduction of special provisions for ammonia pipelines enhance industrial safety. Modern safety improvements are linked to the digitalization of the industry: the implementation of automated monitoring systems, in-line inspection using intelligent flaw detectors, the development of GIS and digital twins, as well as the use of new materials, welding technologies, and corrosion protection. The transition from a reactive model to preventive risk management enables the timely identification of hazardous areas and the rational allocation of resources. Thus, achieving an acceptable level of safety in the operation of trunk pipelines is only possible through a comprehensive approach that combines regulatory improvements, technological modernization, the development of diagnostic methods, and sustainable funding for infrastructure renewal programs. A key condition remains the integration of the efforts of design, operating, supervisory, and scientific organizations aimed at reducing accidents and ensuring the long-term reliability of the critical pipeline system.

This study investigates the contributing factors to traffic crash severity in Korea’s metropolitan areas, focusing on Seoul and Gyeonggi, which account for approximately 41% of national crash occurrences. Using 10 years of crash data (2011–2020) from the Traffic Accident Analysis System (TAAS), a random parameter ordered probit model with interaction effects was applied to capture unobserved heterogeneity and the complex interplay among behavioral and environmental factors. Crash severity was categorized into three levels. The analysis identified that crash severity was significantly associated with speeding, right-curved road segments, rainy weather, and van-involved crashes. Moreover, interaction effects revealed that combinations of these factors further amplified crash severity under specific conditions, particularly at night or on downhill sections. The model incorporating interaction terms demonstrated improved goodness-of-fit over conventional specifications. The findings underscore the need for context-sensitive safety strategies in densely populated urban regions. Beyond their traffic safety implications, these results also contribute to sustainable transportation by supporting interventions that improve the safety, resilience, and inclusiveness of metropolitan mobility systems, particularly through more efficient safety resource allocation and enhanced protection of vulnerable road users.

ABSTRACT This study applies text mining to South Korean news data to identify weak signals related to elderly drivers and analyze their policy implications. A total of 2264 news articles published between January 2024 and March 2025 were collected, and keyword-based weak signals were identified. Word network analysis interpreted relationships between weak signals and co-occurring terms. The results revealed five signal categories—acceleration/brake/activation, free, black box, sidewalk, and system—corresponding to driving errors, incentive-based policies, accident documentation, pedestrian safety, and administrative structures. The findings highlight the need for technical measures to prevent pedal misapplication, mobility-related support for those ceasing to drive, recording devices for accident analysis, protection infrastructure against sidewalk intrusion, and conditional licensing and integrated management systems for elderly drivers. Recognizing elderly driver-related accidents as a structural risk, this study provides insights for traffic safety and criminal justice policy using a weak signal detection approach grounded in public discourse.