An analysis on the statistics of occupational accidents in Turkish fisheries and aquaculture sector; 2013-2019 period

This study was carried out to make a holistic evaluation on the occupational health and safety (OHS) of fisheries and aquaculture sector with its subsectors and to reveal the actual situation of fisheries and aquaculture sector by comparing relevant OHS statistics reported in Turkey. Annual average of accidents in the fisheries and aquaculture sector was found to be 312 (± 147.6), of which 2.14 (± 1.57) resulted in permanent incapacity and 1.57 (± 1.51) of them with fatality. While general incidence rate, fatal incidence rate, permanent incapacity incidence rate and work accident frequency rate were calculated as 2211.8 (±574) 103.6 (±13.4), 5.88 (±2.41) ve 8.36 (±1.93), these values for fisheries and aquaculture sector were determined to be 3579.1 (±1549.3) 175.65 (±152.34), 240.67 (154.62) ve 13.65 (±5.84), respectively. In 2019, the fatal accident rate in Turkey was found to be 0.27% in general, 0.34% in the mining sector, 0.77% in construction and 0.80% in transportation. This ratio was calculated as 1% in the fisheries and aquaculture sector. It was determined that there were more work accidents and fatal incidents in fisheries and aquaculture activities in the seas than in fresh water. Difference between number of work accidents and the duration of temporary incapacity as a result of work accidents in fisheries and aquaculture activities in marine and fresh waters were also significant (p<0.05). Number of fatal accidents in aquaculture sector was found to be more than 4 times than in fisheries. It was determined that the all types of incident rates, work accident frequency rates and fatality rates per work accident in fisheries and aquaculture sector were higher than that of Turkey in general. Evaluation of occupational accidents in the fisheries and aquaculture activity was found to be riskier than expected. Although some evaluations and suggestions are put forward to reduce the number of accidents and the severity of these accidents, a systematic and legislative approach will be more effective. Therefore, it would be a suitable approach to raise the hazard classes of fisheries and aquaculture economic activity to the “very dangerous” class. In this way, the training period of the sector employees on occupational risks and the titles and numbers of OHS professionals working in the sector will be increased, a more qualified specialization will be realized, and control and audit activities will be more frequent.

GIS-based analysis of spatial–temporal correlations of urban traffic accidents

Background Understanding 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. Methods The 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. Results The 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. Conclusions By 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.

RAMS analysis of railway network: model development and a case study in China

Purpose This paper aims to investigate the reliability, availability, maintenance and safety analysis method for railway network operation. Design/methodology/approach The reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of accident recovery time. The safety index of the railway network is proposed to measure the safety of railway stations and sections and the K-means method is proposed to find the safety critical stations and sections. Finally, the effectiveness of the proposed method is illustrated through a real-world case study. Findings The case study shows that the proposed model can produce a big-picture averaged view of the network-wide safety level and help us identify the safety critical stations and sections by considering both the expected reduction of network efficiency and capacity. Practical implications The potential application of the proposed model is to help the safety managers determine the investments in safety management of each section and station and then increase the safety and robustness of railway network operation. Originality/value The safety analysis of the railway network should consider the reliability, availability and maintenance of the railway network. In this paper, the reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of recovery time. Finally, the safety index of the railway network is proposed to analyze the safety critical stations and sections.

Analisis Pengaruh Kecelakaan Kerja terhadap Produktivitas Kerja di CV. Karya Nugraha

Abstract— CV. Karya Nugraha is a company engaged in manufacturing which produces medical devices or medical devices. CV. Karya Nugraha was founded in 2004. CV. Karya Nugraha does not escape the problems faced, such as disorganized storage of goods before processing which hinders the production process which can lead to work accidents. So that companies need control planning in the case of work accidents. This study aims to determine whether the level of work accidents affects the productivity level of employees at CV. Karya Nugraha. Based on the results of the study, the frequency of work accidents in 2016-2017 did not increase or decrease but productivity increased from 0.9902 to 0.9921, This is influenced by the level of the number of hours worked which increases every year and is influenced by the number of hours of work lost that are less in 2017, which means a smaller accident severity.Keywords— Work Accidents, Occupational Accident Frequency, Severity, Productivity Abstrak— CV. Karya Nugraha merupakan perusahaan yang bergerak di bidang manufaktur yang memproduksi alat-alat kesehatan atau alat-alat medis. CV. Karya Nugraha berdiri pada tahun 2004. CV. Karya Nugraha tidak luput dari masalah yang dihadapi seperti tidak teraturnya tempat penyimpanan barang sebelum diolah yang sangat menghambat dalam proses produksi yang dapat mengakibatkan kecelakaan kerja. Sehingga perusahaan perlu perencanaan pengendalian dalam kasus kecelakaan kerja. Penelitian ini bertujuan untuk mengetahui apakah tingkat kecelakaan kerja berpengaruh pada tingkat produktivitas karyawan di CV. Karya Nugraha. Berdasarkan hasil penelitian, tingkat frekuensi kecelakaan kerja pada tahun 2016-2017 kecelakaan tidak mengalami kenaikan maupun penurunan tetapi produktivitas mengalami peningkatan dari 0,9902 ke 0,9921, hal ini di pengaruhi oleh tingkat jumlah jam kerja yang meningkat setiap tahunnya dan dipengaruhi oleh jumlah jam kerja hilang yang lebih sedikit di tahun 2017, yang berarti tingkat keparahan kecelakaan yang lebih kecil.Kata kunci— Kecelakaan Kerja, Frekuensi Kecelakaan Kerja, Severity, Produktivitas

Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index

As roadway and development factors are identified as the most effective factors contributing to road traffic accidents, investigating these factors could lead to reducing the accident frequency rate. However, previous works focused on investigating the effect of roadway factors on the accident frequency rate using statistical analysis. The present study aimed to evaluate the effect of roadway and development factors on the accident frequency rate using ANOVA and Chi-square tests on a rural road. Secondly, it aimed to develop a rural road safety risk index based on K-means clustering and Gaussian models. The findings indicated that the operating speed and the differences between posted speed limits and the operating speed are the pivotal influencing factors on the accident frequency rate. Moreover, clustering analysis of the roadway and development factors on the two-lane, two-way road of Borujerd-Khorramabad indicated six clusters which were identified as highly, relatively highly, moderately, relatively lowly, lowly risky, and not risky (safe) clusters. Regarding clusters, the accident frequency rate increased by decreasing the difference between the posted speed limits and the operating speed from the safe cluster. In addition, the risky index model based on the Gaussian model showed that the average reducing factor of accident frequency rate reached 0.99 by increasing per km/hr in the difference between the posted speed limits and the operating speed among low risky and safe clusters, while it was equal to 1.17 in risky and unsafe clusters. The comparison of the clusters revealed that accident occurrence probability in risky clusters was more than the ones in low risky or safe clusters. Therefore, the maximum and minimum values of the safety risk index were observed in the sixth and the third clusters, respectively.

Using Generalized Linear Mixed Models to Predict the Number of Roadway Accidents: A Case Study in Hamilton County, Tennessee

Introduction: A method for identifying significant predictors of roadway accident counts has been presented. This process is applied to real-world accident data collected from roadways in Hamilton County, TN. Methods: In preprocessing, an aggregation procedure based on segmenting roadways into fixed lengths has been introduced, and then accident counts within each segment have been observed according to predefined weather conditions. Based on the physical roadway characteristics associated with each individual accident record, a collection of roadway features is assigned to each segment. A mixed-effects Negative Binomial regression form is assumed to approximate the relationship between accident counts and several explanatory variables including roadway characteristics, weather conditions, and several interactions between them. Standard diagnostics and a validation procedure show that our model form is properly specified and suitably fits the data. Results: Interpreting interaction terms leads to the follow findings: 1) rural roads with cloudy conditions are associated with relative increases in accident frequency; 2) lower/moderate AADT and rainy weather are associated with relative decreases in accident frequency, while high AADT and rain are associated with relative increases in accident frequency; 3) higher AADT and wider pavements are associated with relative increases in accident frequency; and 4) higher speed limits in residential areas are associated with relative increases in accident frequency. Conclusion: Results illustrate the complicated relationship between accident frequency and both roadway features and weather. Therefore, it is not sufficient to observe the effects of weather and roadway features independently as these variables interact with one another.

Relationship Between Traffic Volume and Accident Frequency at Intersections

Driven by the high social costs and emotional trauma that result from traffic accidents around the world, research into understanding the factors that influence accident occurrence is critical. There is a lack of consensus about how the management of congestion may affect traffic accidents. This paper aims to improve our understanding of this relationship by analysing accidents at 120 intersections in Adelaide, Australia. Data comprised of 1629 motor vehicle accidents with traffic volumes from a dataset of more than five million hourly measurements. The effect of rainfall was also examined. Results showed an approximately linear relationship between traffic volume and accident frequency at lower traffic volumes. In the highest traffic volumes, poisson and negative binomial models showed a significant quadratic explanatory term as accident frequency increases at a higher rate. This implies that focusing management efforts on avoiding these conditions would be most effective in reducing accident frequency. The relative risk of rainfall on accident frequency decreases with increasing congestion index. Accident risk is five times greater during rain at low congestion levels, successively decreasing to no elevated risk at the highest congestion level. No significant effect of congestion index on accident severity was detected.