Identification of Intervention Program Effectiveness at Work Accident revention

Dada a necessidade de prover rodovias mais seguras aos usuários, este trabalho buscou identificar os impactos dos parâmetros geométricos na frequência e na severidade dos acidentes em curvas de rodovias de pista simples. Esta análise foi conduzida para 225 quilômetros de trechos rodoviários de pista simples da BR 116 no estado do Rio Grande do Sul. A reconstituição geométrica rodoviária viabilizou a construção de uma base de dados que reúne as características dos acidentes registrados entre 2007 e 2016 pela Polícia Rodoviária Federal (PRF) e os parâmetros geométricos, permitindo a modelagem da frequência e da severidade dos acidentes a partir desses parâmetros. Foram estimados os modelos econométricos: (i) binomial negativo (BN) para a modelagem da frequência; e (ii) logit ordenado (LO) para a severidade de acidentes. Os resultados indicam que o aumento da declividade e da superelevação impactam no aumento da frequência de acidentes e na redução da severidade.

Industrial fishing for piramutaba (Brachyplatystoma vaillantii) at the mouth of the Amazon River, encompassing the coastal zone of northern Brazil, is an activity characterized by the intense presence of industrial fishing fleets and notable aquatic biodiversity. However, this activity faces significant challenges in the realm of occupational health and safety. The aim of this study was to identify the species of venomous fish present in piramutaba fishing and to assess the impact of these species on the health-disease processes of the fishermen. Through questionnaires, a high incidence of accidents related to fish spines was evidenced. The prominent presence of potentially dangerous species in the region's ichthyological diversity directly influences the frequency and severity of accidents. The educational profile of the fishermen, mostly with incomplete Acta of Fisheries and Aquatic Resources Oliveira et al. (2024): ActaPesca 12(2): 110-120 DOI 10.46732/Actafish.2024.12.2.110-120 111 elementary education, highlights the urgent need for training programs adapted to the local reality. The need for the implementation of Personal Protective Equipment (PPE) is evident, complemented by educational strategies aimed at accident prevention and understanding the risks associated with interaction with venomous fish. The research also underscores the importance of future studies that can evaluate the effectiveness of different training and awareness strategies for fishermen, aiming for a more comprehensive approach to occupational safety in fishing activities.

Spatio-Temporal Analysis on Urban Traffic Accidents: A Case Study of Tehran City, Iran

Occupational safety is an essential element in the prevention of occupational accidents, but more importantly, employees should be aware of the necessity of occupational safety and must avoid dangerous movements that can cause accidents. The safe behavior of employees can be considered as the most important factor in preventing occupational accidents, especially in the construction industry where work place accidents are very common. Working conditions in the construction sector have been changing because of rapid development of technology. Also, demand for different construction types has been increasing. The most difficult task is undertaken by workers to meet this increased demand. Working conditions have become inharmonious because of the need to meet the demand and the construction with the costumer and also the difference between the working areas. This situation brings about the employees are treated like machines and forced to work in hazardous areas. The most common reasons of accidents in this sector are mainly the various dangers arise from the structural features of the sector, lack of knowledge about the conditions of occupational health and safety, not to change the standard working style and the desire of employers to make more profit. The aim of occupational safety in this sector is to diminish the level of the workplace accidents and to make workers conscious of the issue consequently transforming their safety attitudes to a culture. The aim of this paper is to investigate the opinions of workers in construction areas on occupational health and safety. For this purpose, a questionnaire is conducted and applied in various construction sites in Nigde. The obtained results are thoroughly analyzed by SPSS 24 packet programme.

This research aims at describing data for an epidemiological profile, as well as to contribute to the improvement of systems of information, prevention and risk control of accidents in the workplace in processing and further processing broiler plants. In this study, data from documents of 1,274 investigations of typical work accidents were analyzed. Descriptive statistics were used for the analysis, and the frequency and severity of accidents were calculated according to NBR 14280:2001. The results show that men tend to have more accidents than women; 69.8% of those injured had been in the company for less than 3 years; 37% of the accidents were cuts; 35.6% were bruises; the most affected body parts were hands and fingers, representing a total of 48% of all accidents; 41.6% of victims were not using personal protective equipment (PPE) at the time of the accident; the underlying causative condition of 54.9% of the accidents was the lack of personal safety. The proportion for each accident with leave was 1.7 accidents without leave but with a need for relocation of activity and 10.9 accidents without leave without relocation. There were amputation cases among some of those given leave of absence. For every million hours worked, there were 3.2 accidents with leave and 139.4 days of absence were registered. The study concludes that accident rates in in processing and further processing broiler plants are high, and that it is extremely important to establish an epidemiological profile in order to guide prevention and control actions.

The prevention of occupational accidents is not only an important factor for productivity and profitability, but also for the job satisfaction of employees in any company. Striving for a high level of occupational safety is therefore crucial, as the shortage of skilled workers has not significantly abated permanently because of the Corona crisis and preventing accidents and maintaining the working capacity of existing employees ensures the functioning of the company. Organizations use various strategies to improve occupational safety performance, but not all strategies succeed. In particular, it also shows that there are differences in the average frequency of accidents among organizations with different numbers of employees. This paper explores the question of whether small and medium-sized enterprises, in contrast to large enterprises, pursue different strategies in occupational safety and whether these different strategies have an influence on the number of occupational accidents. To answer the research questions, the authors used the methods of standardised questioning according to Reinecke, literature analysis according to Webster and Watson, deduction, comparison, and synthesis. Based on a survey of 118 companies in the Federal Republic of Germany, it was found that the strategy of small and medium-sized companies differs significantly from the strategy of large companies. The strategies in the respective size classes differ above all in the aspects of degree of organisational structure, systematics, investigation of occupational accidents and existence of continuous improvement.

BackgroundUnderstanding the spatial–temporal distribution characteristics of urban road traffic accidents is important for urban road traffic safety management. Based on the road traffic data of Wales in 2017, the spatial–temporal distribution of accidents is formed.MethodsThe density analysis method is used to identify the areas with high accident incidence and the areas with high accident severity. Then, two types of spatial clustering analysis models, outlier analysis and hot spot analysis are used to further identify the regions with high accident severity.ResultsThe results of density analysis and cluster analysis are compared. The results of density analysis show that, in terms of accident frequency and accident severity, Swansea, Neath Port Talbot, Bridgend, Merthyr Tydfil, Cardiff, Caerphilly, Newport, Denbighshire, Vale of Glamorgan, Rhondda Cynon Taff, Flintshire and Wrexham have high accident frequency and accident severity per unit area. Cluster analysis results are similar to the density analysis. Finally, the temporal distribution characteristics of traffic accidents are analyzed according to month, week, day and hour. Accidents are concentrated in July and August, frequently in the morning rush hour and at dusk, with the most accidents occurring on Saturday.ConclusionsBy comparing the two methods, it can be concluded that the density analysis is simple and easy to understand, which is conducive to understanding the spatial distribution characteristics of urban traffic accidents directly. Cluster analysis can be accurate to the accident point and obtain the clustering characteristics of road accidents.

On the road safety benefits of advanced driver assistance systems in different driving contexts

Introduction Health-care organizations are facing a high burden of ergonomic occupational accidents, and prevention is a continuous point of interest. In this manuscript, we describe the characteristics of ergonomic accidents in a large Belgian university hospital and discuss the value of near misses. Methods Combining databases, we identified the frequency [number of accidents × 106 hours worked per year], severity (number of days off work × 103 hours worked per year), and profile of the victims of occupational ergonomic accidents (with absence from work) or incidents or near-misses (without absence from work). Ergonomic accidents and incidents include slips, trips, falls, injurious body movements, overexertion, and handling heavy weights. Results In a period of 23 years, we noticed a significant decrease in the frequency of ergonomic accidents (from about 7 to about 4 standard units), without changes in the severity. The decrease in the frequency of accidents is mirrored by an increase in the frequency of incidents (from about 4 to about 6 standard units). Female and older employees are more vulnerable to accidents, and the frequency was between two and four times higher for employees mostly involved in manual tasks compared to employees mostly involved in managerial tasks. The profile of the victims and the causes of accidents and incidents were identical. Conclusion Although it is premature to assume a cause–consequence relationship between incidents and accidents, it is tempting to speculate that the increased ratio of the frequencies of incidents over accidents might be one of the variables reflecting the adequacy of preventive measures and the growth of safety behavior.

While some initial and significant progress has been made in the development of a highway safety manual, much remains to be done in the areas of conceptual development and the diagnostics of safety problems. The concept of level of service of safety (LOSS) in the framework of safety performance function is introduced, and the problem of diagnostics is addressed. LOSS reflects how the roadway segment is performing in regard to its expected accident frequency and severity at a specific level of annual average daily traffic. It provides a comparison of accident frequency and severity only with the expected norms; it does not, however, provide any information related to the nature of the safety problem itself. If the safety problem is present, LOSS will describe only its magnitude. The nature of the problem is determined through diagnostic analysis by direct diagnostics and pattern recognition techniques, which are also discussed.

Road traffic safety is a social issue of widespread concern. It is important for traffic managers to understand the distribution patterns of road traffic accidents. To this end, this study examines the spatial and temporal patterns of road traffic accidents from both accident frequency and accident severity perspectives. Road traffic accident data from 2016 to 2018 in Harbin, China, were used for the analysis. First, the spatial localization of accidents was completed using geocoding, and the localized accident data were classified by season. Then, density analysis was performed both with and without considering road network density. The results of the density analysis showed that when road network density was considered, accidents were mainly distributed in urban centers, while accidents were more dispersed when road network density was not considered. Third, a cluster analysis considering accident severity found that low-severity accident clusters occurred mostly in urban centers. High-severity accident clusters were mostly present in suburban areas. Finally, the results of these two methods are shown by using the comap technique. Areas of the city with a high frequency and severity of crashes in each season were identified. This study will help traffic management to have a more visual and intuitive understanding of the urban traffic safety situation and to take targeted measures to improve it accordingly.

Efficiency of speed limits in cities: A spatial computable general equilibrium assessment

The aim of the present study is the representation of a method to identify and prioritize accident-prone sections (APSs) based upon efficiency concept to emphasize accidents with regard to traffic, geometric and environmental circumstances of road which can consider the interaction of accidents as well as their casual factors. This study incorporates the segmentation procedure into data envelopment analysis (DEA) technique which has no requirement of distribution function and special assumptions, unlike the regression models. A case study has been done on 144.4km length of Iran roads to describe the approach. Eleven accident-prone sections were identified among 154 sections obtained from the segmentation process and their prioritization was made based on the inefficiency values coming from DEA method. The comparisons demonstrated that the frequency and severity of accidents would not be only considered as the main factors for black-spots identification but proper rating can be possible by obtaining inefficiency values from this method for the road sections. This approach could applicably offer decision-making units for identifying accident-prone sections and their prioritizations. Also, it can be used to prioritize intersections, roundabouts or the total roads of the safety organization domain.

Distracted driving is a major contributor to motor vehicle accidents, causing injury and loss of life. It is one of the major factors that affect the overall driving behavior of a person. Insurance companies take into consideration factors like gender, age, etc. to set insurance premiums for their customers. Today, machine learning and artificial intelligence can eradicate this bias. A machine learning model can analyze driving behavior, such as the frequency and severity of accidents, the speed at which they drive, and their habits such as distracted driving. Based on this information, the model can then determine the risk of accidents for the driver and set a corresponding insurance premium. In this project, we initiated multiclass driver distraction classification through transfer learning, utilizing pre-trained models such as ResNet50 and VGG16 to achieve 92.2% and 94.1% accuracies. Subsequently, we introduced a custom CNN model which attained a validation accuracy of 98.7%. To enhance practicality and accuracy, we transformed the multiclass task into binary classification. Here, we leveraged the fusion of deep learning and traditional machine learning, treating the convolutional filters as a feature extractor and employing Random Forest for binary classification, resulting in an impressive 99.6% validation accuracy. Our model was trained on the State Farm Distraction dataset that is available to the public on Kaggle. Additionally, we computed a driver-specific distraction score, offering potential applications in assessing accident risk, setting equitable insurance premiums, and promoting safer driving habits.

Rapid technological development and shorter innovation cycles are driving significant changes in the field of autonomous vehicle systems. The trend towards autonomous driving has increased the importance of existing and new assistance systems in vehicles. The aim of developing such systems is to reduce the frequency and severity of accidents as traffic density increases. In addition, the presence of driver assistance systems should have a positive impact on the safety perception of vehicle occupants. At the same time, understanding human perception is becoming increasingly important as technology advances. For vehicle manufacturers, occupant safety perception is critical to improving their market position, as safety is an important decision criterion for users.The objective of this paper is to identify the factors that influence occupant and driver safety perception in Level 3 autonomous vehicles and to embed them in a comprehensive model. For this purpose, the current state of the art was reviewed and analyzed. The results were then used to identify a total of 17 factors in five categories of influence. The model shows the interrelations and dependencies between these factors. With the help of the developed model, it should be possible in the future to systematically evaluate vehicle interiors with regard to perceived safety.The developed model integrates objective factors that influence perceived safety and aims to be applicable across manufacturers and vehicle models. The model considers objective factors with measurable influences and defined optimal ranges, such as interior temperature. It is intended to serve as a basis for understanding the complex interactions that influence human perception of safety in autonomous driving scenarios.The development of a model that can be used as a basis for assessing perceived safety in autonomous vehicles proved to be complex, especially given the ongoing research in this area and the lack of fully autonomous vehicles on the market. Therefore, the model is designed to be extended in the future. Further studies are needed to improve the model, including experiments in real vehicles with different levels of autonomy and the development of appropriate measurement tools. In addition, it is important to investigate the composition of an ideal survey group, taking into account factors such as geographical origin and socio-economic background. The results of the experiments and surveys must be statistically analyzed in order to continuously improve the model. External influences, such as market trends, should be taken into account in the ongoing monitoring and improvement process.In conclusion, the developed model captures the influences on drivers and occupants of Level 3 autonomous vehicles and forms the basis for further research. Future research should focus on objective assessments using a catalog based on the model. In addition, ongoing research is essential to keep the model relevant in the face of technological advances and market dynamics.