Frequency Of Accidents Research Articles

PERTANGGUNGJAWABAN PIDANA TERHADAP PENGENDARA YANG MENGAKIBATKAN KEMATIAN PADA KECELAKAAN LALU LINTAS

The development of Indonesia's transportation system is a community need. Given the rather dominant role played by land transport, it is imperative that land transport ensure that traffic is safe, orderly and smooth in order to ensure a smooth flow of various activities in the realization of the public interest. And how to be efficient. Traffic accident frequency. Many of the causes of traffic accidents are caused by negligence and human error, and these traffic accidents have resulted in many losses, such as damage to public facilities and casualties. The research methods used by the authors are prescriptive laws that research library materials or secondary data. The approaches used by the authors are the legal approach and the case approach. A legal approach is taken by directly examining the laws and regulations relevant to the legal issue under investigation. Approaching the case, the author analyzes Court Decision No.665/Pid.B/PN.JKT.PST. Article 311 of the Road Traffic Transport Law No.22 of 2009 precluded the application of Articles 338 and 359 of the Penal Code by the existence of regulations on traffic accident norms and criminal penalties as a special case for traffic accidents. General Criminal Provisions. It is known as a special law that deviates from the general law.

Association of Hearing Health with Traffic Accidents among Heavy Vehicle Drivers

Aim: Excessive exposure of drivers to traffic noise and the resulting hearing loss could affect safe driving. This study aimed to investigate the hearing status of heavy vehicle drivers and its correlation with traffic accidents. Materials and Methods: This cross-sectional study was conducted on 187 drivers of heavy vehicles (trucks and buses) in Tehran province during 2018. The hearing status of drivers was investigated by the audiometric test method, ISO 6189 (1983). A safety questionnaire and checklist of drivers' demographic variables were used to gather information on accident status and personal characteristics of drivers. SPSS version 24 software was used for the statistical analysis of data. The statistical tests used are Chi-square, Spearman's correlation, paired t-test, and odds ratio. Results: Examining the hearing status of drivers showed that 51.5% were normal and 48.5% of drivers had hearing loss. The highest mean hearing threshold was at a frequency of 4000 Hz. The mean (standard deviation) for the right and left ears was 27.82 ± 15.92 and 18 ± 27.32 dB, respectively. The results of the analysis on drivers' hearing status and accidents showed a significant correlation between the frequency of accidents and the classification of drivers' hearing loss. Thus, a significant relationship with the correlation coefficient of 0.123 and 0.307, respectively, was obtained between the average hearing loss of drivers and having one or five accidents. Conclusion: The findings of the study showed hearing loss in drivers in the left ear, especially at high frequencies. There is also a significant relationship between the number of accidents and the rate of hearing loss of drivers. Hearing loss caused by noise interferes with activities such as driving and negatively affects the safety of drivers and emphasizes the importance of preventing accidents by controlling traffic noise.

Cellphone laws and rear-end accidents

In an attempt to reduce accident frequency caused by distraction involving cellphone use, the state of California enacted two sets of laws banning cellphone use while driving. We examine the relationship between laws to ban cellphone use while driving and rear-end accident frequency in California. Using a panel data set that groups accidents by geographic location, we find evidence that the association between California’s handheld cellphone ban and the number of rear-end accidents is negative and statistically significant. In robustness testing, we find this result is unaltered when we use a broader definition of “collision accident.” These results have implications for public safety and transportation, automobile insurance markets and insurance regulation.

Study of radiation-thermal damage in white rats

Currently, despite the improvement of radiation safety measures, there is a risk of increasing frequency of radiation accidents and various disasters accompanied by explosions, fires and emissions of radionuclides. Emergencies (emergencies) of peacetime and wartime, especially accidents at radiation-hazardous facilities, including nuclear power plants, carry the danger not only of irradiation of people, but also at the same time thermal burns, mechanical injuries, chemical poisoning by harmful gorenje products, as well as other lesions. Experimental modeling of acute radiation and burn disease was carried out both separately and in combination. It was found that external total gamma irradiation of white rats at a dose of 7.5 Gy causes severe radiation sickness with characteristic clinical signs and high mortality of irradiated animals. A heat stroke simulation was performed by applying a metal plate heated to 190°C with an exposure time of 5 and 8 seconds. It turned out that the degree of burn depends on the time of contact with the surface of the body: its application within 5 seconds caused a burn of the III-A degree, an 8-second exposure of the IIIB degree, which was confirmed by the timing of formation, rejection of eshar and complete healing of the thermal lesion. The aggravating effect of ionizing radiation on the course and outcome of combined radiation-thermal pathology is demonstrated.

Road safety in public education - how to teach children to travel safely?

The high frequency of road accidents justifies the importance of education for safe road safety, which can play an extremely important role in childhood. The aim of our research was to examine textbooks of public education in Hungary through in-depth and qualitative content analysis of these documents, based on the requirements of the Hungarian National Core Curriculum. The analysis shows that the textbooks cover only a few areas of knowledge related to passenger transport. Causes of child accidents and environmental aspects of transport are barely mentioned. Parents’ role in road safety education has been found to be too strong, making it less clear to what extent skills are being acquired. The textbooks do not present the specificities of urban and rural transport and their differences, and language use is too complex in some publications, which may be less suitable for children.

VFR Into IMC Through the Lens of Behavioral Economics

Decision-making can be the difference between life and death in all types of aviation, but in general aviation (GA), where most of the flying is conducted as single-pilot operations, the decision-making of one individual becomes fundamentally important. It is critical to consider, first, why pilots make bad decisions that can ultimately lead to weather-related aviation accidents or incidents; and second, whether a better understanding of weather-related decision-making can inform regulations that will improve decision-making and consequently reduce the frequency of pilot-error accidents. Behavioral economics (BE) aims to better understand individual decision-making to model decision-making pathways. As individual decision-making is central to aviation safety, better modeling of decision-making pathways should be a central aim not just for pilots, but also for aviation regulators, such as the Civil Aviation Safety Authority (CASA) in Australia. While there has been little analysis of pilot decision-making using BE, we argue that BE, with its focus on predictive models of individual decision-making, provides a rich framework to understand pilot decision-making and inform more targeted regulation. This argument is in four parts. The first part identifies that there is an ongoing safety issue with visual flight rules (VFR) pilots flying into instrument meteorological conditions (IMC). The second part introduces some of the core concepts of BE, such as the rejection of perfect rationality and the reliance upon certain behavioral biases in decision-making. We argue that VFR into IMC is an appropriate context in which to apply BE as there is an identifiable measure of a pilot’s welfare and concerns around paternalism fall short when dealing with protecting the welfare of those likely to be impacted by a pilot’s decision-making, such as passengers and aircraft owners. The third part reviews the existing research applying behavioral models of decision-making in respect of VFR into IMC and identifies three behavioral biases that—among others—can lead to poor decision-making: (i) environmental literacy, (ii) overconfidence, and (iii) prospect theory. The final part briefly introduces some potential avenues for BE to inform regulatory reform, including better education of pilots and regulators in respect of the psychological factors to which pilots may fall victim, as well as more directed training for pilots to address the environmental literacy concerns identified in this part. We conclude that the regulatory environment should be reformulated to adequately account for predictable behavioral biases.

Collision Prediction of Smart Cities Using Co-simulation

Road traffic has become prominent in everyday living, impacting or disrupting services to people and daily routines. With the rise in automobile manufacturing and the frequency of vehicle crashes, human catastrophes, like fatalities, accidents, impairments, and destruction of property, are surging yearly. Vehicle collision detection has recently gained prominence in decreasing manually operated and autonomous vehicle fatalities. The concept of independent and self-driving cars relies on accurate object recognition, including pedestrians, vehicles, buildings, and other moving objects. Various object-detecting approaches have been proposed to help autonomous vehicles (AVs) achieve consistent, safe driving. Object prediction and detection have noticed numerous algorithmic changes that have improved speed and accuracy. In this study, I used a traffic dataset produced by a CARLA simulator to anticipate collisions using the Yolov7 model. I generated the dataset from a CARLA simulation bench in video sequences, manually annotated the frames, and used the deep learning algorithm Yolov7 to train them. The model predicts the collision a few seconds before it occurs in real-time. I implemented this framework to increase the safety of driving in self-driving vehicles.

A methodology for introducing the impact of risk analysis in local railways improvements decisions

Maintaining a high-quality service at acceptable safety level is very demanding on resources for railway sector. This is even more true for local railways, where economical and organizational resources incur various limitations if compared to the case of the national network. Furthermore, due to the significant differences not only in management but also in infrastructural characteristics, the application of criteria and standards born and commonly used in the environment of national railways to local ones leads to a non-optimal use of resources and an overall reduction in the quality of service. Another limit that local railways have to face in order to standardize safety efficiency to the national network is linked to accident and Hazardous Events data occurring in the system, that could be not sufficient to define reliable forecasting models. In order to cope with all of those constraint and to optimize management strategies for local railways, this work provides a framework of a quantitative risk management methodology able support the Infrastructure Manager in decision-making process for asset development. The proposed methodology allows to quantitatively estimate the impact on risk of changes to the system and management choices, prioritizing and optimizing the intervention strategies on the system. To overcome the problems related to the lack of accidental data, the methodology allows to broad the accidental database to networks similar to the one analysed, calibrating the information according to the differences in terms of safety equipment between the systems. Moreover, through the analysis of all factors influencing the frequency and consequences of accidents and their interrelationships, the methodology provides a complete view on the level of risk associated with all parts of the system, providing powerful support for development choices and management optimization even to heterogeneous systems such as local railways.

Occupational accidents among workers within a microarea covered by the Family Health Strategy.

Occupational accidents are events that tend to be highly underreported in Brazil's official reporting systems. In this context, surveys with primary data collection are important to provide a better dimensioning of these events. To characterize workers who had a non-fatal occupational accident within the microarea of a Family Health Unit in Salvador, state of Bahia, Brazil. An observational, analytical, individual, and cross-sectional was conducted, with data collected between January and February 2020. The questionnaires were administered during home visits. The final sample consisted of 134 workers aged 18 to 65 years. The incidence of occupational accidents was 6.7% (n = 9), all typical, particularly involving female workers (55.6%); aged 30 to 59 years (66.7%); with education up to high school (77.8%); self-identified as Black/Brown (100%); who received between one and two minimum monthly salaries (55.6%); single (55.6%); and with self-rated fair health (66.7%). In the population analyzed, the cumulative incidence of accidents was higher in female individuals (6.9%), with incomplete/complete high school (8.3%), self-identified as Black/Brown (7.7%), with informal employment, (7.2%), personal income of one minimum monthly salary or less (9.5%) and working > 44 hours per week (8.2%). A higher frequency of occupational accidents was recorded among individuals in situations of social vulnerability, which should be the focus of public policies and Occupational Health Surveillance in primary care.

Analysis of aerodynamic characteristics of car diffuser for dissimilar diffuser angles on Sedan’s using CFD

A vehicle's aerodynamic qualities have a major impact on its performance, handling, safety, and comfort. Variations in aerodynamic characteristics, such as lift and drag forces, have a noticeable impact on vehicle performance at high speeds. Researchers can lessen the frequency of accidents in which a motorist loses control of their car on our roads by lowering the pressure differential between the road and the tires. Manufacturers can save time and money on research and development by using Computational Fluid Dynamics (CFD) instead of wind tunnel testing, appreciations to advancements in computing power. The focus of this study is to learn the different aspects of a vehicle influence on its drag coefficient. Using CFD, the ideal diffuser angle was identified, which maximizes the down force produced by the device. Analysis of the drag and turbulent kinetic energy on the vehicle at varied diffuser angles (10, 11, and 12°) and vehicle speeds was performed using Ansys 16.2 Fluent. As shown by the numerical study, the drag force is reduced to 119.019 N at the 12° diffuser angle, which also provides the highest velocity and the lowest turbulent kinetic energy value. Researchers found that adjusting the diffuser's tilt significantly altered the under-body flow's wake.

Чат-бот как инструмент эффективного взаимодействия граждан с аварийными службами

The article considers the problems associated with ensuring security and prompt response to emergencies in cities. With the rapid growth of cities and the in-crease in the number of inhabitants, these aspects become key tasks for municipalities. Citizens face various problems that require the intervention of emergency services. However, there is a lack of awareness and ambiguity about the activities of these servic-es, there is no single entry point for submitting requests for troubleshooting and assess-ing the quality of work performed. This article proposes the concept of a solution in the form of developing a chatbot based on artificial intelligence, which will provide an effective and convenient way for citizens to contact emergency services. Such a chatbot will allow you to quickly send requests for emergency situations, automatically transfer requests to service operators, as well as display the status of requests and update it. In addition, the innovative capabilities of artificial intelligence will recognize and classify problems, prioritize applications, propose solutions, and automatically notify citizens when work is completed. Particular attention is paid to the use of the information ob-tained to account for the frequency of accidents and build a map of emergency areas, which contributes to making optimal decisions in the reconstruction and overhaul of utility life support systems. The use of a chatbot based on artificial intelligence is an ef-fective tool to improve the safety and quality of life of the urban population. It allows you to quickly and conveniently access emergency services, provides easy communica-tion with city residents, and allows you to explore and analyze accident and perfor-mance data to make informed decisions aimed at improving living conditions in cities

Prediction of Road Accidents Using Machine Learning Algorithms

Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

A new hybrid algorithm for maximum likelihood estimation in a model of accident frequencies

In this paper, we are interested in the numerical computation of the constrained maximum likelihood estimator (MLE) of the parameter vector of a discrete statistical model used in statistics applied to road safety. The parameter vector is divided into two blocks: one block with the parameter of interest and the second block with secondary parameters. The MLE is the solution to a system of non-linear implicit equations difficult to solve in closed-form. To overcome this difficulty, we propose a hybrid algorithm (HA) mixing the use of a one-dimensional Newton-Raphson (NR) algorithm for the first equation of the system and a fixed-point strategy for the remaining equations. Our proposed algorithm involves no matrix inversion but it partially enjoys the quadratic convergence rate of the one-dimensional NR algorithm. We illustrate its performance on simulated data and we compare it to Newton-Raphson (NR) and quasi-Newton algorithms which are two of the most used optimization algorithms. The results suggest that our HA outperforms NR and quasi-Newton algorithms. It is accurate and converges quickly for all the starting values.

Analiza udesa i ozbiljnih nezgoda u vazdušnom saobraćaju primenom Microsoft Power BI alata

Safety is a priority in aviation and for all aviation entities, including civil aviation authorities, operators, air traffic control service providers and airports. With the air traffic volume increase, there is a growth in the aviation system efficiency and therefore it is necessary to maintain a high level of safety. Practical processing of the operational data includes the representation and analysis of accidents and incidents in aviation over the certain period. The data presented in the paper are based on the location of the safety event, flight phase, aircraft type, accident frequency rate and other possible factors that can affect aviation safety. The main goal of analyzing the operational data is to raise safety awareness, as well as to maintaining safety risks on satisfactory level including safety risks mitigation measures.

Effects of road geometric parameters on safety: A case study of Mugling-Narayanghat road in Nepal

Road safety has emerged as a global priority due to the multifaceted consequences of road traffic accidents (RTAs) in modern society. The study is focused on the development of an accident prediction model (APM) and identifying the influence of geometric factors on road safety. The study section of the highway was divided into 190 identical segments based on geometric characteristics. Eight models have been developed for establishing the relationship between crash numbers and respective parameters. The significance of the models has been validated by the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study unveils that the shoulder and lane width, numbers, and radius of the horizontal curve significantly influence RTAs. However, the gradient and shoulder type have less impact on road accident frequency. The research recommendations for the improvement of safety are increasing the radius of the curves, lane, and shoulder width. Similarly, the study suggests the reduction of the number of horizontal curves for the reduction of crash frequency.

Lesões Músculo-esqueléticas relacionadas com os Acidentes de Trabalho: Avaliação diagnóstica numa empresa transformadora

Introduction Workers’ Safety, Health and Well-being should be the concerns for millions of professionals around the world, but the issue extends beyond individuals and their families, being extremely important for the productivity, competitiveness and sustainability of businesses and communities. Portugal is one of the countries with the highest rates of non-fatal Work Accidents in Europe, with Work-Related Musculoskeletal Disorders being the most common Occupational Health problem, having consequences for absenteeism and productivity falls. Objectives To analyse work accidents in a manufacturing industry in northern Portugal between 2018 and the first half of 2020; to describe the Musculoskeletal Disorders associated with work accidents. Methods Exploratory, retrospective and descriptive quantitative study. According to the inclusion criteria defined, a sample of 798 workers who suffered 1076 Occupational Accidents was selected. The data sources were the company’s workplace Accident records platform and data from the insurer. A data collection form has been created which included characterization of Work Accidents, organization and workload, causes and consequences of the accident at work and imputed costs. A descriptive analysis was made using SPSS 26.0 program. Results The 1076 work accidents affected 798 workers, observing that in 298 situations there are employees with more than one accident. On weekdays, most of the Accidents occurred in the Morning Shift (36.2%), with more Accidents occurring in the last quarter of the Shift (28.9%). The Production Department Vulcanization has a higher incidence of Accidents (34.6) followed by the Engineering Department Vulcanization (30.5). At the Weekend there was a lower absolute frequency of Work Accidents, however, are the Weekend teams in rotating Shifts that have a higher percentage of Accidents, especially when working weekdays rotating Shifts. The most frequent Injuries were wounds and superficial injuries (45.6%) and dislocations, sprains and strains (40.8%), with the Body’s Upper extremities being the most affected with 42%. Accidents at work represented a direct cost of EUR 967 799.53. Conclusion This study allowed the Department’s characterization and Jobs with the highest incidence of Work Accidents, Musculoskeletal Injuries resulting from them and the Shifts’ relevance in the occurrence of claims. These data allow us to identify the critical points to delineate an intervention plan directed to the identified specificities. Faced with this problem, it is essential to adopt effective preventive measures, interventions and strategies in order to reduce the occurrence of Occupational Accidents, contributing to the Workers Health and Well-being, and Occupational Health Services should play a preponderant role in the areas of investigation and prevention of Musculoskeletal Disorders resulting from Occupational Accidents. KEYWORDS: Work Accidents; Work-Related Musculoskeletal Disorders (WMSD); Occupational Health.

Characteristics of Traffic Accidents Frequency on Connection Road Between 3 Cities in Riau - Indonesia

The main cause of road accidents in outside of cities is the driver's lack of awareness about road safety. Efforts to reduce these accidents can be done if the characteristics and causes are known. The purpose of this study was to determine the characteristics of road accidents in three areas in Riau province. Analysis used a descriptive quantitative analysis. This study was done to identify the characteristics of traffic accidents occurred from 2014 to 2019 on the connection road between 3 cities in Riau, namely in the Kuantan Singingi, Kampar and Rokan Hulu areas of Riau Province, Indonesia. The analysis used is descriptive quantitative analysis. The greater accidents were occurred in 2018, in which 192 cases. The least accidents were occurred in 2017, in which 115 cases, the month of June had the highest number of accidents since it was a month of school holidays. The percentage of total deaths on traffic accidents reached 60%. Meanwhile, 75.6% of traffic accident victims were injured, with the most accidents occurring on flat and straight roads. The types of vehicles that are often involved are motorcycle, which occur from 06.00 to 11.59 pm.

Human Factors Analysis and Classification System - AHP Drone Model Assessment

Drone systems have become increasingly applied to various commercial, technical, agricultural, and military sectors. With the emerging significant effects on the frequency of drone accidents in many situations and areas, it has become increasingly necessary to form unmanned aircraft systems and usage limitations. The first step to do that is by highlighting the sources of accidents in the current process. The Human Factors Analysis and Classification System (HFACS) is the main technique for accident risk assessments in drone systems which clusters the accident key factors causes into 4 basic domains. In this study, 4 categories of drone experts participated in the study. The research utilized a new approach that integrates an analytical hierarchical decision-making model with the HFACS for drone accident causation investigation. The results of the study showed discrepancies among the different groups of drone operators and identified organizational factors and unsafe actions as being key issues in the evaluation.

A Review of Ontology-Based Safety Management in Construction

The construction industry is one of the most dangerous industries in terms of safety performance, with practitioners and experts actively developing various solutions to reduce accident frequency and severity. However, accident information is collected in a wide range of formats by various elements in the construction industry, leading to interoperability issues and poor productivity due to the difficulties of sharing and reusing information. To improve the management of various types of safety management (SM) records in the construction industry, practitioners and researchers have adopted ontological methods. This paper summarizes the SM trends in construction management, along with gaps and opportunities for future work. A data processing framework is developed with a phase research for objective and subjective topic analysis from a collection of articles from 2012–2022 on topics relevant to the use of ontology in SM. The analysis focuses on the ontological life cycle (development, integration, and application), revealing an increasing trend of ontology-based SM (ObSM) research in the SM maintenance phase. Increasing case size and system automation is needed for future ontology-based SM optimization. The findings of the study will help to gain a thorough knowledge of ObSM, which will increase interest in effectiveness and the use of engineering and analytical techniques in SM.

A Novel Approach to Analysis of Road Accidents Using the Trend Analysis and IPTA Method: A Case Study of Kosovo

Road accidents in Kosovo cause more than 100 deaths each year and about EUR 40 million in property damage. The paper presents an accident prediction model that relates accident frequency to various contributory factors and is developed using trend analysis and the Innovative Polygon Trend Analysis method. In addition, the study investigates road traffic accidents using a novel methodology to compare the different modes and other parameters. The presented research also discusses the road safety in Kosovo, which is a Republic with relatively low economic growth based on GDP per capita, a young population, an increase in the number of vehicles per capita, and an increase in the number of road accidents, but a decline in the number of deaths from road accidents in recent years.