Fatal Accidents Research Articles

Analysis of the Relationship between Speed Consistency and Traffic Accident Probability (on the Mojokerto-Surabaya Toll Road)

Vehicle speed consistency plays a crucial role in maintaining traffic safety, with speed limits set to reduce accident fatalities. While the direct impact of speed limits on vehicle speed is minimal, their implementation significantly influences driver behavior, with about 85% of drivers complying with speed limits. However, variables such as traffic volume, weather conditions, and travel urgency often cause drivers to be inconsistent in adhering to these limits, increasing accident risk. On the Mojokerto-Surabaya toll road, data from IRSMS Korlantas Polri and Jasa Marga recorded 193 accidents with 101 fatalities from 2018 to August 2023, mostly caused by driver behavior such as excessive speed. This study analyzes the relationship between speed consistency and accident probability on this road, highlighting accident issues due to speed violations and inconsistency at ramp off/on locations. Results show the importance of toll road infrastructure considering vehicle volume, identifying accident blackspots, and implementing urgent speed management as key factors in improving safety on the Surabaya-Mojokerto toll road. The findings emphasize the need for better traffic management and speed regulation to reduce fatal accident risks.

Indoor staircase detection for supporting security systems in autonomous smart wheelchairs based on deep analysis of the Co-occurrence Matrix and Binary Classification

Detecting descending stairs and floors is a crucial aspect of implementing autonomous systems in smart wheelchairs. When the obstacle detection system used in wheelchairs fails to accurately identify descending stairs, it can lead to severe consequences for users, including injuries or, in the worst-case scenario, fatal accidents. Therefore, there is a pressing need for an algorithm that not only exhibits high accuracy in detecting obstacles on descending stairs but also operates with minimal computational delay to ensure an immediate response in wheelchair braking. In this research, We utilize the GLCM technique to extract texture characteristics. Out of these methods, the Decision Tree exhibits the highest accuracy, reaching 94%, with a remarkably fast computational time of 0.01299 s. These promising results were achieved by utilizing the GLCM method with a distance of 2 and an angle of 45°. The accuracy obtained has increased by 2.5% compared to the previous research. Such a level of accuracy, coupled with fast computational performance, enables smart wheelchairs to effectively assist users in identifying obstacles while descending stairs.

Normative Study On Criminal Liability Of The Captain For The Occurrence Of A Fatal Accident That Causes The Death Of A Person

The purpose of this study is to describe criminal liability for the captain for the occurrence of fatal accidents that cause the death of people. The method provided uses a data normative law research approach using secondary data and techniques with document and literature studies. The study results show that the captain is the highest leader on a ship and is responsible for the occurrence of ship accidents unless it can be proven otherwise. The captain's criminal liability for a fatal accident is caused by technical, human, and natural factors. Ship accidents caused by technical and human factors result from a lack of caution (negligence) or a mistake. Because of the fulfillment of the elements of criminal liability, namely mistakes (schuld), being able to take responsibility, and the absence of excuses, the captain is burdened with criminal liability. Meanwhile, ship accidents caused by natural factors are included in noodtoestand. This force majeure circumstance is a reason for forgiveness in criminal law so that the captain is not burdened with criminal liability.

On using machine learning algorithms for motorcycle collision detection

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds that of passenger car accidents, efforts have been directed towards increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria.

Lost years, mortality burden: the impact of COVID-19 pandemic on premature death due to road traffic accidents in a northern state in Malaysia

BackgroundThis study addresses the persistent global burden of road traffic fatalities, particularly in middle-income countries like Malaysia, by exploring the impact of the COVID-19 pandemic on Road Traffic Accident (RTA) fatalities in Perak state, Malaysia, with a secondary focus on applying Years of Life Lost (YLL) to understand the implications of these premature deaths.MethodologyThe cross-sectional study retrospectively reviewed certified RTA fatalities from 2018 to 2021, individually counting fatalities in accidents and excluding cases with incomplete death profiles. Data were collected from all Forensic Departments in the government hospitals in Perak. RTA fatalities were confirmed by medical officers/physicians following established procedures during routine procedures. A total of 2517 fatal accident and victim profiles were transcribed into data collection form after reviewing death registration records and post-mortem reports. Inferential analyses were used for comparison between pre- and during COVID-19 pandemic. The standard expected YLL was calculated by comparing the age of death to the external standard life expectancy curve taking into consideration of age and gender in Malaysia.ResultsThis study included 2207 (87.7%) of the RTA fatalities in Perak State. The analysis revealed a decreasing trend in RTA deaths from 2018 to 2021, with a remarkable Annual Percent Change (APC) of -25.1% in 2020 compared to the pre-pandemic year in 2019 and remained stable with lower APC in 2021. Comparison between pre-pandemic (2018–2019) and pandemic years (2020–2021) revealed a difference in the fatality distribution with a median age rise during the pandemic (37.7 (IQR: 22.96, 58.08) vs. 41.0 (IQR: 25.08, 61.00), p = 0.002). Vehicle profiles remained consistent, yet changes were observed in the involvement of various road users, where more motorcycle riders and pedestrian were killed during pandemic (p = 0.049). During pandemic, there was a decline in vehicle collisions, but slight increase of the non-collision accidents and incidents involving pedestrians/animals (p = 0.015). A shift in accident from noon till midnight were also notable during the pandemic (p = 0.028). YLL revealed differences by age and gender, indicating a higher YLL for females aged 30–34 during the pandemic.ConclusionThe decline in RTA fatalities during COVID-19 pandemic underscores the influence of pandemic-induced restrictions and reduced traffic. However, demographic shifts, increased accident severity due to risky behaviors and gender-specific impacts on YLL, stress the necessity for improved safety interventions amidst evolving dynamics.

ANALYSIS OF THE LEVEL OF OCCUPATIONAL INJURIES

Problem statement. The task of the research is evaluation the dynamics of changes in the level of injuries at work depending on the main causes of the accident. Technical, organizational, psychophysiological, technogenic, social, natural and environmental causes are closely interrelated and have a significant impact on the number of victims of accidents at the industrial plants, as well as on the number of victims of accidents with fatal consequences. The purpose of the article. The assessment of changes in industrial injuries at Ukrainian enterprises during 2010−2022. The establishing connection between social and industrial factors affecting the rate of industrial accidents. The creation of correlation-regression models for statistical evaluation and analysis of the influence of factor variables on the results of injury. Methodology. The use of descriptive statistics for the analysis of the dynamics of changes in the level of industrial injuries. Carrying out a correlation analysis to establish the density of connection between factor variables and resulting features. The use of regression-variance analysis to obtain coefficients of regression mathematical models and statistical indicators that explain the probability of significance of these coefficients. Calculation of the value of the relative error of the calculation data obtained according to mathematical models to confirm their adequacy. Scientific novelty. Multiple correlation-regression models have been developed that take into account the main causes of the accident as factor variables affecting the injury rate and the rate of fatal accidents. Practical significance. Mathematical models make estimate the impact of the main causes of accidents on the level of industrial injuries. The implementation of improved methods and means of safe activity at work is an important tool for correcting the cause-and-effect relationships of industrial injuries. Conclusions. The correlation-regression models were created to analyze the level of industrial injuries in Ukraine. The numerical calculations were carried out according to these models. The average values of the relative errors of the calculated data are 1.55 % and 6.08 %, which indicates the adequacy of the developed models.

Statistical Analysis of Occupational Fatal Accidents for Risk Assessment

Abstract According to Malaysia's Department of Occupational Safety and Health (DOSH), workplace accidents have increased dramatically as the country's economy has grown rapidly. The purpose of this study is to identify the trends that lead to fatal workplace accidents in Malaysia. This study investigated 505 fatal accident instances from fifteen industries, using both qualitative and quantitative methodologies. The DOSH discovered and reported these fatalities (2010-2020). Data was organized and coded, then analyzed using Spearman's rank order correlation, frequency analysis, Eta squared, Cramer's V, and Chi-square. Moreover, neurolinguistic programming was used to generate the word cloud and sentiment analysis. Furthermore, light gradient-boosting machine learning was used to better understand the causes of fatalities. The outcomes indicated that fatal falls from heights were the leading cause of fatal accidents (32%). General laborers were the most susceptible to fatal accidents (60%). Contract workers were more likely to die in construction accidents (64%). The findings of this study gave useful insights into workplace fatal accident trends and preventive measures throughout sectors.

Accident black spots identification based on association rule mining

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

Fatal traffic accident associated to hypercapnic coma due to kyphoscoliosis: a case report

Fatal traffic accident associated to hypercapnic coma due to kyphoscoliosis: a case report

Analysis of Road Traffic Accidents in Dense Cities: Geotech Transport and ArcGIS

Road Traffic Accidents (RTAs) provide a substantial risk to both public safety and infrastructure within severe earthquake zones. This research aims to provide a thorough analysis of the many elements that contribute to RTAs in the area. Although research on RTAs has been conducted in numerous settings, there is a lack of localized research specifically focused on developing countries. Therefore, this study is necessary to address this gap in literature. Our study focuses on examining the distinct issues and elements that are particular to this specific location. The objective is to provide customized solutions that effectively reduce the occurrence of accidents. The objective of this study is to examine the status of RTAs, ascertain the main factors contributing to RTAs, and provide viable approaches to improve road safety. A comprehensive study of data was undertaken, which included the examination of key highways, accident-prone areas, and causes contributing to accidents. Insights were derived by the use of statistical analysis, hotspot mapping, and the categorization of incidents. The results revealed a distressing roadway are afflicted by a significant frequency of accidents, even those occurring on prominent thoroughfares. Excessive speed has been revealed as the primary contributing factor, closely followed by negligence and recklessness. The common factor seen in these events was the insufficient enforcement of traffic regulations. The research highlights several practical consequences. Firstly, it is advised to implement urgent actions such as the upgrading of the traffic database, the adoption of contemporary traffic management software, and the enforcement of rigorous traffic laws. The establishment of a comprehensive centralized database system is crucial in order to enhance the documenting and management of accidents. Furthermore, the use of cost-benefit analysis provides justification for the adoption of traffic calming measures in locations with a high incidence of accidents. Furthermore, the implementation of continuous road safety awareness efforts and the establishment of clear legislative guidelines pertaining to fatal accidents are imperative measures in promoting enhanced road safety.

Analysis of tire/gripper slip forces during the handling of large off-the-road tires

Handling large off-the-road tires poses significant safety risks in surface mines, often leading to fatal accidents. This study aims to scientifically address the industry-wide knowledge gap regarding the impacts of various operational and tire parameters on tire handler gripper and tire traction performance. The specific research objectives include (i) quantifying the maximum lift force under various pad-to-tire contact conditions, (ii) identifying critical parameters influencing the point of first slip, and (iii) offering suggestions for improving tire handling operations. To achieve these goals, a portable tire handling test rig weighing 7 t was designed, constructed, and commissioned. Subsequently, in July 2021, functional performance and initial tire slip tests were conducted at the service center of Bridgestone Mining Solutions Australia in Oxley, Brisbane. Overall, 44 lifting tests were conducted on new, semi-worn, and worn Bridgestone 42/90R57 tires using a Cascade 10 t H-Series tire handler. Further, a design of an experiment matrix was formulated to test the influence of various parameters on the slip force, which is defined as the point of the first slip. The investigated parameters included tire orientation (vertical or horizontal), tread surface (dry or wet), tire wear (new, semi-worn, or fully worn), and tire handling pressure (unpressurized standalone and 0, 5, 10, 15, 20, and 30 psi for the tire and wheel set). The results revealed that to mitigate the hazard of sudden pressurized air release, tires must be preferably lifted at the lowest pressure adequate for ensuring handling performance, preventing bead-seat displacement during lifting. Furthermore, the risk of bead-seat displacement was found to be higher when lifting tires from an initial horizontal position owing to the action of shear forces on the bead seat. Considering these factors, an optimal pressure of 10 psi was recommended for lifting semi-worn 42/90R57 tires vertically under both wet and dry conditions, contingent upon the use of a concave gripper design typical of the Cascade H-Series tire handlers.

Forensic Assessment of Alcohol Intoxication in Cases of Fatal Road Traffic Accidents in Lithuania.

There is strong evidence that alcohol consumption is a significant risk factor for fatal road traffic accidents. It is estimated that the number of alcohol-related road accidents remains high in the past few years in Lithuania. This study aims to examine the prevalence of alcohol in blood samples collected from the autopsy results of road traffic accident victims. A retrospective study of 136 road traffic accident victims was performed in State Forensic Medicine Service of Lithuania in the period of 2013 to 2023. We analyzed blood alcohol concentration (BAC) in relation to sex, age, road user type, place and time of the day at death. 31% of the victims were under influence of alcohol at the time of death, with mean BAC 1.99 ± 0.92‰. The mean BAC was 2.16 ± 0.8‰ in male and 1.18 ± 1.12‰ in female group. By the type of road users, 23% of the pedestrians (mean BAC 2.45 ± 0.71‰), 32% of car drivers (mean BAC 2.13 ± 0.75‰), 41% of vehicle passengers (mean BAC of 1.73 ± 1.19‰), 37% of the motorcycle riders (mean BAC of 1.28 ± 0.53‰), 37% of the cyclists (mean BAC of 1.15 ± 0.75‰) were found to be intoxicated during the time of accident. Highest mean blood alcohol concentration was found during the night time hours (9 p. m. - 5 a. m.) 2.28 ± 0.91, comparing to in afternoon hours (12 p. m. - 5 p. m.) 1.49 ± 0.99, evening hours (5 p. m. - 9 p. m.) 2.10 ± 0.73 and morning hours (5 a. m. - 12 p. m.) 1.94 ± 1.00. The mean BAC in road traffic accidents during summer was 1.48 ± 0.71‰, spring 2.25 ± 0.76‰, autumn 2.12 ± 1‰, winter 2.42 ± 1‰. Alcohol consumption by road users is a significant contributing factor in road traffic accidents and their outcomes in Lithuania.

자율주행자동차 상용화에 대비한 자동차보험제도 연구

On May 7, 2016, in the state of Florida, USA, a fatal accident occurred involving an autonomous vehicle and a large truck, leading to the death of the driver. This incident heightened interest in autonomous vehicles and sparked discussions on their implications, including the challenges and liability issues in the event of accidents during autonomous operation. The application of existing legal frameworks, such as Article 750 of the Civil Code on tort liability and Article 3 of the Automobile Damage Compensation Guarantee Act on operator liability, to accidents involving autonomous vehicles raises questions about identifying responsible parties and compensating victims. This study examines the applicability of these legal principles to accidents occurring during autonomous driving and proposes the introduction of a No-Fault insurance system as a novel automotive insurance mechanism to address these issues. The analysis revealed that the No-Fault insurance system, a solution to the problems posed by the current fault-based liability system and already implemented in several advanced countries since the early 2000s, was considered but ultimately not adopted in South Korea due to challenges in amending existing legislation, limitations in calculating insurance rates, and concerns over moral hazard, among other issues. However, as autonomous vehicles are poised to integrate into our lives more rapidly than anticipated, and the social concerns regarding traffic accidents during autonomous operation deepen, it is imperative to form a task force comprising the Ministry of Land, Infrastructure, and Transport, the Financial Supervisory Service, the General Insurance Association, among others, before it's too late. This task force should engage in research and discussions, while building a consensus among the public, to prepare for the new automotive market driven by the autonomous vehicle industry.

Corrosion characterization of engine connecting rods using fringe projection profilometry and unsupervised machine learning

The consequences associated with corrosion, a global industrial peril, cost an estimated $ 2.5 trillion annually to inspect, rectify, and prevent. In addition to significant economic losses, corrosion-induced failure of critical components in transport systems, like automobiles, may also lead to loss of human life. Hence, it is essential to eradicate corrosion in its early stages. The most vital automobile component is its engine, whose failure can cause fatal accidents. Regular quality inspection and maintenance by skilled personnel is essential to prevent this. Automating this task will address this domain’s personnel shortage while mitigating the risk of human error. To enable the performance of this task without the need for human intervention, we determine the morphological parameters affected by corrosion in automotive engine components, namely connecting rods, using fringe projection profilometry, a high-speed 3D profiling technique capable of achieving sub-millimeter accuracy. We then perform classification using k-means clustering into low, medium, and high corrosion bands, based on the parameters obtained from 3D imaging. The model was able to achieve a high accuracy of 88.57%. The accuracy was determined by considering the visual classification performed by a Material Science Expert as the ground truth.

Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniques

The number of motorcycle accidents has increased rapidly due to different causes. Helmet is a one of the major safety equipment that helps to reduce the fatal accidents even though many of the motorcyclists are not used that. Currently many researchers have focused on traffic violator detection but they are yet to identify the violated person details as it requires high resolution cameras and expensive sensors for number plate extraction. This research article is focused on comprehensive study on the development of an automated helmet violation detection with number plate extraction in real time. This study divides the dataset into rider with helmet, rider without helmet, passenger with helmet and passenger without helmet classes, and four training model was created to detect helmet in rider and passengers. The proposed model was constructed in three stages to attain high accuracy with a vast amount of training dataset. In the first stage, the helmet was detected in the input image using the deep learning object-detection model .In the second stage, if the helmet identification confirmed in the input image then the entire motorcyclist image is segmented based on object segmentation model. In the third stage, number plate training model is deployed into the same object detection and object segmentation model to identify and segment the license plates in the motorcyclist image. This study primarily incorporates the Mask Region based Convolution Neural Networks (Mask R-CNN) for object detection and segmentation Further, it incorporates the three different deep neural networks like Dense block Network (DenseNet-121), Residual Network (ResNet-101) and Inception Residual Network architecture V2layers (Inception Res Net V2) to create accurate helmet violator detections with more extensive information. The experimental results indicates that this comprehensive helmet violator detection study improves the helmet detection accuracy by 6.2 %.In addition, the above three comprehensive proposed models are evaluated under Wilcoxon rank test for better sensitive information. Further, the less object detection loss (2 %) and significant computational time variation (69 %) indicates the performance of the comprehensive helmet violator detection system was good compared to existing study.

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

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

Occupational accidents in Malta and the role of the occupational health and safety authority: A twenty-year analysis

The Occupational Health and Safety Authority (OHSA) was established in Malta in 2002. Since then, trends indicate that non-fatal accidents have decreased in Malta, while changes in fatal accidents are less clear. Since these trends have not been statistically investigated before, this study aims to do so. The study also aims to analyse the link between specific OHSA deterrent measures and changes in non-fatal accidents. A database compiled by the OHSA on the frequency of accident statistics in Malta and OHSA deterrent measures between 2002 and 2022 was analysed. The study demonstrated that the incidence of fatal and non-fatal accidents decreased significantly during the analysed period. The incidence of non-fatal accidents was more common in the transport and storage sector, the construction sector and the manufacturing sector. Fatal accidents were most frequent within the construction sector. Fatal accidents were common among the self-employed and foreign workers. Deterrents, especially those related to inspections and fines, were significantly associated with a decrease in fatal and non-fatal accidents. The study underscores those accidents have declined significantly since the establishment of the OHSA and demonstrates the benefits of specific deterrent measures. Continued focus is required on specific areas, including the construction sector, self-employed workers and foreign workers.

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).