Fatal Accidents Research Articles

METHODOLOGIES FOR MANAGING THE TREEFALL RISK OF URBAN TREES

Recently, concern about the risk of falling trees, triggered by several fatal accidents involving falls in urban environments, has increased the interest of managers around the world in developing effective assessment techniques for accident prevention. The objective of this study was to compare different methods of visual assessment of the risk of falling urban trees in Spathodea campanulata individuals. To do so, 43 individuals were analyzed using five visual risk analysis methodologies. Data collection was conducted between December/2020 and January/2021 using a form created in Google Forms. The risk level classification was standardized into high, medium and low for all methodologies, and statistical tests were performed to verify the similarity between them. The time taken for analysis was timed and tree DBH dimensions were measured to verify the correlation with the degree of risk. It was observed that 41.8% of the trees were included in the high-risk class by all methodologies, with methodology II showing the lowest variation between individuals. Statistically, methodology I differed from all methodologies, while II was only the same as IV, and the others were similar. AV is the fastest and I takes the longest. No high correlation was found between time spent and degree of risk of falling trees or DBH.

Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation

Missing persons and infrastructures of search and identification

People go missing all over the world, but the reasons for disappearances are enormously diverse. Some people are intentionally disappeared by the state: totalitarian and military governments as well as various paramilitary and criminal organizations have used enforced disappearances as a tactic to control the population and create submissive citizens or subjects though fear and insecurity. Both civilians and soldiers disappear invariably in the chaotic circumstances of war and armed conflict. Some people disappear in natural catastrophes or fatal accidents; some disappear of their own free will. Whatever the reason for disappearance, it disturbs the everyday flow of life in families and communities, and in many places, it creates anomalies for modern state bureaucracies. Unaccounted-for absences give rise to search practices, but the circumstances of search are radically different in different places and different contexts of disappearance. One way to approach these differences is to analyse the infrastructures of search in each site. I am especially interested in the entanglement of the local with the global, and of the spatial reach of search infrastructures . Moreover, I consider the significance of the material affordances of some infrastructural forms, especially of the DNA as the key tool for identification. I make some observations on how the entanglements of local and transnational investment and the material affordances of the techniques allow some of the disappeared to be found and identified, while others stay more ‘disappearable’ (Laakkonen 2022).

'Unforeseeable' and 'inevitable': Constructions of prison suicide in Scotland's Fatal Accidents and Sudden Deaths Inquiries.

Rates of suicides are on the rise in Scottish prisons. Fatal Accident and Sudden Deaths Inquiries (FAIs) carried out by sheriffs following these deaths provide a valuable source of knowledge regarding how the criminal justice system understands and responds to incarcerated people's suicides. Informed by literature in critical suicide studies and narrative criminology, we conducted an abductive, narrative qualitative analysis of 37 FAI reports of incarcerated people's suicides published between 2016 and 2021, in Scotland. We argue that the FAIs explicitly individualise incarcerated people's suicides, deploying explanatory narratives of (1) mental illness, (2) social history and (3) problematic substance use, while (4) simultaneously emphasising and undermining incarcerated people's testimony and agency. By conceptualising suicide as paradoxically 'unforeseeable' and 'inevitable', these narratives shift blame onto the individual while absolving the prison system. Our analysis contributes towards understanding of how institutional procedures are implicated in social scripts and practices around suicide in Scottish prisons.

Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods.

Identifying Spatial Patterns of Road Accidents in Madaba City by Applying Getis-Ord Gi* Spatial Statistic

Road safety has become a subject of great interest among policymakers worldwide as they seek effective strategies to mitigate traffic accidents. There exist various approaches to examine the occurrences of road traffic accidents in terms of their spatial information and consequences. Geographical information systems (GIS) have been widely employed to analyse the spatial patterns of road traffic accidents; they offer various statistical analysis tools to reveal the hotspot locations of road accidents. Reducing the number of traffic accidents and overcoming their negative impact by defining the hotspot locations gain serious attention from the Public Security Directorate (PSD) in Jordan. This study analyses road traffic accidents in Madaba City using spatial statistics to determine the hotspot locations. The Gtis-Ord (Gi*) spatial statistics method was applied to 5730 reported traffic accidents between 2017 and 2019. The results accurately located the groups of chosen accidents and identified 37 hotspots, accounting for 1.89% of the reported cases. The Maximum Z score was 30.99, and 691 reported cases led to the identification of 13 high-priority hotspots. These hotspots occur at significant thoroughfares, busy roundabouts, and uncontrolled intersections. Driving errors and excessive speeding were the most common causes of fatal and non-fatal accidents in Madaba City. Efficient countermeasures to mitigate the number of accidents in Madaba City include adding more police inspectors to the city center, installing speed cameras, and putting up traffic signs at uncontrolled intersections. The outcomes of this work may encourage the PSD to adopt the GIS statistical tools in analyzing the spatial patterns of road traffic accidents to achieve more accurate results.

An Approach of Multi Criteria Decision Making in Analyzing Rural Road Safety in Nashik District

Abstract: Rural road safety evaluations play a significant role in the transportation industry, and most fatal accidents happen on these types of roads. Road Safety Analysis (RSA), a process to evaluate a road's safety criteria and support overall road management decision-making, ensures the avoidance of loss of life and property damage. The "Pradhan Mantri Gram Sadak Yojana," which is primarily utilized in rural areas, is the main emphasis of this. In order to identify the most vulnerable roadways and implement mitigation measures, roads must be prioritized depending on safety ratings. This document presents the results of a study conducted to rank the safety criteria of Pradhan Mantri Gram Sadak Yojana (PMGSY) roads, a particular category of rural roads. A questionnaire survey was used for the study's analysis, and three MCDM methods were used to examine the relationships and interactions between the various factors. The study's findings indicated that the based on input from PMGSY engineers and contractors. From the perspective of the engineers in the Nashik area, we can determine the safety impact factor that influences the safety assessment of the PMGSY route.

A RT-FDTD method of analyzing wireless propagation characteristics in underground mine

Efficient communication is crucial in reducing injuries and fatalities in coal mine accidents, necessitating the study of simulation methods for mine communication. When transceiver antennas are positioned close to the same side of the tunnel, the simulation results from the Ray Tracing (RT) method exhibit significant errors. Additionally, the Finite-Difference Time-Domain (FDTD) method demands substantial computational resources. In response to these challenges, we propose a RT-FDTD method, guided by the law of conservation of energy. This approach involves dividing the mine tunnel into a cuboidal region, using the RT method to calculate the electric field strength on the cuboid’s surface, and then employing this as the excitation source for the FDTD method. Subsequently, the FDTD method is used to calculate the electric field strength within the cuboid. Experimental results demonstrate that the RT-FDTD method effectively mitigates the limitations of the RT and FDTD methods, enhancing both the efficiency and accuracy of simulations in underground mine.

Key Insights from Preflight Planning for Safety Improvement in General Aviation: A Systematic Literature Review

This study highlights the disproportionate number of fatal and non-fatal accidents in general aviation (GA) compared to airline carriers, emphasizing the need to investigate the contributing factors to these incidents. It identifies poor decision-making and a lack of situational awareness as key issues and presents a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to analyze preflight information used by GA pilots. The findings underscore the significance of operational factors in ensuring a successful flight and suggest modifications to pilot license renewal processes, with an emphasis on the adoption of digital preflight tools. A new theoretical framework based on the operational factors identified is also introduced, which could serve as a foundation for future studies and interventions aimed at enhancing safety in general aviation.

Pattern of Head Injury in Fatal Road Traffic Accident: Retrospective Study

Road traffic Accident have been reported to be a major cause of morbidity and mortality. Due to increasing modernization, road traffic accidents have become exponentially more frequent and dangerous. Mortality rate from road traffic injuries accounts 29.2 per 100000 people5. This study was conducted in the Department of Forensic Medicine, Silchar Medical College, Silchar, from 1st November 2021 to 31st October 2022. The aim of the study is the pattern of head injuries in fatal road traffic accident. Out of the 981 autopsy cases were conducted during the above-mentioned period, a total of 200 cases were found deaths due to head injuries. Out of 200 cases, common age group involved was from 40-50 years, the deceased were more from rural areas. Maximum cases during winter season. Commonest injuries were scalp abrasion, linear fracture of skull and subdural haemorrhage. Head injury has become a common cause of death and disability among Road Traffic Accident cases. This further shows the need of strict implementations of traffic rules. Positive policy initiatives such as mandatory helmet legislation need reliable and consistent data to support ongoing monitoring and enforcement of such initiatives.

Evaluating Road Crash Severity Prediction with Balanced Ensemble Models

This study evaluates the performance of an ensemble of five ML models (Random Forest, XGBoost, AdaBoost, LightGBM and CatBoost) on crash data from New South Wales, Australia. The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. Feature importance analysis reveals the key predictors being the vehicle type involved, with sedans/hatchbacks and motorcycles being the most common in fatal crashes, and the collision type, with vehicle-to-object impacts often leading to fatalities. Furthermore, fatal crashes occur more on Saturdays, in country non-urban LGAs and speed limits of 100 km/h as the most usual settings for fatal accidents.

El crecimiento económico y su influencia sobre los accidentes de trabajo mortales en el Perú

This study aims to determine how economic growth influenced fatal occupational accidents in Peru from 2011 to 2019. Data from the 24 departments, grouped into 4 macro-regions, were used and an econometric analysis using panel data was applied. This analysis consisted of assessing the stationarity of the data, and through first generation unit root tests, it was determined that they are integrated of order one. Likewise, it was found that there is a cointegration relationship between the variables (long-run equilibrium) by Pedroni panel cointegration means testing. Finally, the regression coefficients were estimated using the DOLS (Dynamic Ordinary Least Squares) estimator. It was determined that economic growth has a direct proportional relationship with fatal occupational accidents; that is to say, economic boom would cause fatal occupational accidents to increase.

Meta-Feature-Based Traffic Accident Risk Prediction: A Novel Approach to Forecasting Severity and Incidence

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques.

Wrong, Strong, and Silent: What Happens when Automated Systems With High Autonomy and High Authority Misbehave?

Warnings about the risks of literal-minded automation—a system that can’t tell if its model of the world is the world it is actually in—have been sounded for over 70 years. The risk is that a system will do the “right” thing—its actions are appropriate given its model of the world, but it is actually in a different world—producing unexpected/unintended behavior and potentially harmful effects. This risk—wrong, strong, and silent automation—looms larger today as our ability to deploy increasingly autonomous systems and delegate greater authority to such systems expands. It already produces incidents, outages of valued services, financial losses, and fatal accidents across different settings. This paper explores this general and out-of-control risk by examining a pair of fatal aviation accidents which revolved around wrong, strong and silent automation.

Fatal road traffic accident and medico-legal aspects in Lomé (Togo)

Objectives: know the circumstances and causes of death related to traffic accidents which have been the subject of forensic expertise in Togo. Methods: It was a case series study on fatal road traffic accident victims who benefited from forensic expertise from 2017 to 2021 (5 years) in the pathological anatomy department of Lomé in Togo. Result: A total of 248 forensic expertise files were collected, representing an annual frequency of 49.6 cases. The sex ratio (M/F) was 3.1. The ages of the victims ranged from 1 to 84 years with an average age of 39.2±4.3 years. The circumstances were dominated by motorcycle collisions (101 cases; 40.7%) followed by motorcycle- vehicle (67 cases; 27.0%). Speeding was reported as the leading cause of accidents in 179 cases (72.2%). The victims were mainly passengers and motorcycle drivers in 115 cases (46.4%). The anatomical lesions were grouped into four groups: polytrauma (152 cases, 61.3%), severe head trauma isolated (55 cases, 22.2%), thoracic contusion (23 cases, 9.3%), and abdominopelvic contusion with perineal lesion (18 cases, 7.2%). Conclusion: Raising awareness and applying road safety principles among motorcycle and car drivers will help stop this threat. Government at all levels should implement strong policies to reduce the speed of vehicles on the roads. Objectifs : connaître les circonstances et les causes des décès liés aux accidents de la circulation qui ont fait l'objet d'une expertise médico-légale au Togo. Méthodes : Il s'agissait d'une étude de série de cas sur les victimes d'accidents mortels de la circulation ayant bénéficié d'une expertise médico-légale de 2017 à 2021 (5 ans) dans le service d'anatomie pathologique de Lomé au Togo. Résultats : deux cent quarante-huit (248) dossiers d'expertise médicolégale ont été recueillis, soit une fréquence annuelle de 49,6 cas. Le sex-ratio (H/F) était de 3,1. L'âge des victimes variait de 1 à 84 ans avec une moyenne de 39,2±4,3 ans. Les circonstances étaient dominés par les collisions entre motocyclettes (101 cas ; 40,7%) suivies par les collisions entre motocyclettes et véhicules (67 cas ; 27,0%). L'excès de vitesse était la cause principale des accidents dans 179 cas (72,2%). Les victimes étaient principalement des passagers et des conducteurs de moto dans 115 cas (46,4 %). Les lésions anatomiques ont été regroupées en quatre groupes : polytraumatisme (152 cas, 61,3%), traumatisme crânien sévère isolé (55 cas, 22,2%), contusion thoracique (23 cas, 9,3%) et contusion abdomino-pelvienne avec lésion périnéale (18 cas, 7,2%). Conclusion : La sensibilisation et l'application des principes de sécurité routière parmi les conducteurs de motocyclettes et de voitures contribueront à mettre un terme à cette menace. Le gouvernement à tous les niveaux devrait mettre en œuvre des politiques fortes pour réduire la vitesse des véhicules sur les routes.

Work-Related Hazards and Perceived Confined-Space Health Risk : Understanding the Correlation with Mental Workload Among Farmers in Northern Thailand’s Shallow Wells

ABSTRACT Objectives This study examined factors related to perceived health risks in confined spaces (PCSHR) and their correlation with the mental workload among farmers managing agricultural wells in northern Thailand. Methods A cross-sectional, multi-stage sample of 356 farmers was selected from four rural districts’ agricultural areas. Data were collected through interviews conducted from August to December 2022, using a self-administered structured questionnaire. The five-part questionnaire gathered demographic data, information on experiences and operations in agricultural wells, knowledge of confined spaces, PCSHR, and the six-dimension NASA Task Load Index (TLX) mental workload. Linear regression and multi-variable analyses were used to investigate factors associated with PCSHR, while Pearson correlations tested the association between PCSHR and mental workload variables. Results Most farmers were male (92.4%), worked in wells to install pumping systems (81.7%) and maintain equipment (73.3%), averaging 3.80 times per year, with an average duration of 25.81 minutes. Physical symptoms reported included difficulty breathing (72.8%), feeling swelteringly hot (55.9%), and sweating excessively (27.8%), as well as accidents such as being struck by falling soil or objects (20.2%), and falling into the well while climbing down (14.9%). Farmers’ perceived risk scores were high when working while physically exhausted or unprepared and when assisting an unconscious worker without knowing the gas concentration. In addition, the maximal mental workload scores were mental demand and effort subscale. Factors significantly associated with PCSHR (adj.R 2 = 60.6%, p < .05) encompassed education higher than lower secondary level, current alcohol consumption, smaller well width, assisted operations, number of physical symptoms experienced, absence of environmental accidents, and confined space knowledge, while increased PCSHR was positively associated with mental workload (Overall r = 0.711, p < .01). Conclusion Comprehensive education about potential hazards can improve farmers’ risk perception, potentially reducing mental workload and preventing fatal accidents. Field studies are recommended to develop community-specific work protocols and accurate measuring instruments suitable for rural settings are needed.

Tapping on Collective National Trauma

Tapping on Collective National Trauma

Mechanical trauma in children and adolescents in Berlin.

Management of severe pediatric trauma remains challenging. Injury patterns vary according to patient age and trauma mechanism. This study analyzes trauma mechanisms in deceased pediatric patients. Fatal pediatric trauma cases aged 0-18 years who underwent forensic autopsy in the Federal State of Berlin, Germany, between 2008 until 2018 were enrolled in this retrospective study. Autopsy protocols were analyzed regarding demographic characteristics, trauma mechanisms, injury patterns, resuscitation measures, survival times as well as place, and cause of death. 71 patients (73% male) were included. Traffic accidents (40%) were the leading cause of trauma, followed by falls from height > 3m (32%), railway accidents (13%), third party violence (11%) and other causes (4%). While children under 14 years of age died mostly due to traumatic brain injury (59%), polytrauma was the leading cause of death in patients > 14 years (55%). Other causes of death were hemorrhage (9%), thoracic trauma (1%) or other (10%). A suicidal background was proven in 24%. In the age group of > 14 years, 40% of all mortalities were suicides. Cardiopulmonary resuscitation was carried out in 39% of all patients. 42% of the patients died at the scene. Children between 0 and 14 years of age died most frequently from traumatic brain injury. In adolescents between 14 and 18 years of age, polytrauma was mostly the cause of death with a high coincidence of suicidal deaths. The frequency of fatal traffic accidents and suicides shows the need to improve accident and suicide prevention for children and adolescents.

Pattern and distribution of injuries associated with road traffic accident in Haldwani (Nainital) - An autopsy based study

Indiais one of the fastest growing economies. There is a rapid inflation of vehicles on the road that range from motorcycles to heavy vehicles which also shows an active surge in the fatal road accidents. This study is focussed to find out the pattern and distribution of injuries in fatal road traffic accident cases in Kumaon region of Uttarakhand as to plan at the time of policy making and traffic law implementation.: The present study was conducted in the department of Forensic Medicine &amp; Toxicology of Government Medical College, Haldwani from January 1 2019 to December 31 2019, on total 110 cases of RTA brought to mortuary for medico-legal post mortem examination.: Maximum number of cases sustained head and neck injuries (42 out of 110 externally) followed by extremities injury (28 out of 110). Total 38 cases out of 110 sustained head and neck injuries internally, followed by abdominal injuries (28 cases). Abrasions (30 cases) and laceration (29 cases) were commonest type of external injury noted in RTA cases followed by contusion (22 cases) and fracture (19 cases). Most common cause of death among RTA victims was head injury (40 out of 110 cases) followed by shock and haemorrhage (37 out of 110 cases).

Integrating perspectives from engineering ethics into the study of technology: A synthesis of research on critical cases

Ethics play a crucial role in the work of engineers, and therefore in everyone’s life, from the field of artificial intelligence ethics to the fields of environmental ethics and biomedical ethics. Analyzing critical technology case studies from the perspective of engineering ethics affords us the opportunity to understand if engineers acted ethically or not in a given situation, what they could have done otherwise, and what they should bear in mind for future case studies regarding ethics. In this article, at first, we draw upon primary and secondary sources in order to analyze two case studies that are of critical importance because, according to the engineers involved in them, it was likely they could have led to fatal accidents. Then, we offer engineering ethics perspectives into the studies. Studying engineering cases from an engineering ethics perspective is crucial for two reasons. First, engineers could have a more completed point of view regarding engineering ethics in order to understand how to act ethically during their work. Second, engineers and others (philosophers of technology, historians of technology, etc.), who analyze these cases in retrospect, could present a more adequate story that would be more useful for the engineers who are taught from it.