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Background With the rapid development of China’s chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars’ attention. Methods Based on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises. Results The results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees’ unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc. Conclusion Compared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.

Railway level crossings (RLCs) in Poland are classified according to their protection systems. Category D, which is a form of passive RLC, aims to ensure safe and efficient operation. Surveying is essential to prepare and control the geometry of the visibility triangles used at RLCs. This article presents a new approach to monitoring the geometry of visibility triangles of RLCs using an electronic total station and a magnetic measuring square (MMS). Its main assumptions are presented together with the application of the innovative measuring instruments. Visibility is demonstrated taking into account the angles of intersection of the road axis with the track axis of the railway line and additional attributes related to the analysis and evaluation of general visibility conditions. The research highlights controversies that have received special attention against the background of the safety status of railway level crossings. As a case study, the RLC located on a single-track railway line in Poland is examined. The final section presents applications of the results obtained according to the proposed methodology. It is shown that the proposed approach is practical and effective. In addition to surveyors, the survey methodology can be used by road and rail traffic engineers and policy makers to further improve traffic safety at RLCs. This is an important global research task.

Professional drivers play a crucial role in many businesses and the lives of people. They are responsible for transferring people and goods between distant points, enabling safe and efficient flows. The road traffic death rate is from 8.3 to 27.5 per 100,000 inhabitants in the countries globally. Because professional drivers spend a significant amount of time on the road, their appropriate selection may contribute to general traffic safety. In addition, an adequate selection of candidates significantly impacts the financial costs of the employing company. However, the recruitment procedure is a very complex task where multiple criteria should be considered. By its nature, this is a typical multi-criteria decision-making problem. The purpose of this paper is twofold: to contribute to the methodological, as well as to the professional field. Considering the professional, we propose a decision-making tool in the procedure of professional driver selection. There are several methodological contributions. By reviewing the literature, we identified 14 criteria for candidate selection. In the proposed model, by using expert opinion and implementing DEMATEL and Fuller’s pairwise comparisons, we ranked these criteria and determined the seven most important for further calculation procedure. Here, we introduced an original approach for measuring the reliability of obtained answers. Then, to rank the candidates, the fuzzy AROMAN approach is applied for the first time in the literature. The input data were obtained in the form of a survey, where the experts evaluated the importance of criteria and their interrelation. We used MS Excel and MATLAB for data processing. An additional methodological contribution of this study is an advancement of the AROMAN method by the proposal of an algorithm for the calculation of parameter λ used in the final ranking formula. To illustrate the applicability of the proposed model, a case study is provided. Based on the results, we can conclude that the new methodological approaches can be successfully used in the procedure of professional driver selection, as well as in solving other multi-criteria decision-making problems.

Despite being a minor probability event, marine accidents can cause serious consequences such as casualties, environmental damage, and even massive economic losses. As an important part of the marine traffic safety system, the evaluation of navigation safety in coastal waters is of great significance to ensure the safety of ship navigation. In order to objectively evaluate the safety of navigation, this paper proposes an irregular grid division method that combines influencing factors including seabed topography, ship traffic flow, electronic charts, and marine meteorology. In this work, the navigable boundary was extracted by spatial analysis algorithms of the slope calculation. Based on the alpha-shape algorithm and the Voronoi diagram, the constrained Delaunay triangulation was used to extract the inner and outer boundaries of the ship′s navigation area, and the time-varying factors of marine hydrology and meteorology were integrated to form a dynamic irregular grid. The navigation safety evaluation indicators in coastal waters were divided into five risk levels, namely lower, low, medium, high, and higher. Then, the entropy weight theory was used to calculate the weight of the evaluation index. Finally, a safety evaluation model was constructed to evaluate the risk of navigation safety in coastal waters. Herein, taking the coastal waters of Lianyungang Port as a demonstration area, this paper divided the dynamic irregular grid and conducted the navigation safety analysis and evaluation based on the grid. The experimental results show that our method fully considers the influence of objective factors and the uncertainty of safety evaluation indicators and has favorable adaptability to the evaluation of navigation safety in coastal waters.

Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in information entropy. Particularly, this algorithm relies on the analysis of sufficient consecutive video frames. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, we construct a geometric area called Face Feature Triangle (FFT) based on the application of the Dlib toolkit as well as the landmarks and the coordinates of the facial regions; then we create a Face Feature Vector (FFV), which contains all the information of the area and centroid of each FFT. We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. Comparative experiments show that our algorithm performs better than the current ones on both accuracy and real-time performance. In simulated driving applications, the proposed algorithm detects the fatigue state at a speed of over 20 fps with an accuracy of 94.32%.

Alighting bus stops inferring is of great significance for origin–destination estimation. Cellular signaling data (CSD), a kind of individual trajectory generated by mobile phones, provides a new idea for alighting stop identification. To explore the capacity of CSD in this field, this study proposes a method of inferring alighting bus stops by integrating smart card data, bus GPS data, and CSD. Firstly, a correspondence table is generated by individual matching, which correspondingly links mobile phone users in CSD and bus passengers in smart card data. Secondly, the inferred alighting bus stops are determined by the radii of the circumscribed circles of triangles consisting of directly projective points, piecewise projective points, and CSD points. The proposed method is verified by an experimental dataset from a behavioral simulation experiment of 10 volunteers in Foshan, China. The results show that the recognition rate is 92.94% and the inference accuracy is 65.82%, or 93.67% under a one-stop error. In the case of a real dataset in Foshan, the proposed method with a recognition rate of 53.02% highly outperforms the trip-chain-based method. The difference in the recognition rate between the two datasets is due to that the real dataset is more likely to be incomplete than the experimental data, which indicates that the performance and effectiveness of the proposed method are sensitive to the data quality and completeness of CSD and bus GPS data. Having said that, the proposed method can infer both alighting stops of linked bus trips and single unlinked bus trips.

A number of modern scientific and practical problems in design of multifunctional safety systems for coal mines and requirements to such systems are discussed. The reasons and the dynamics of accidents in mines are analyzed; examples of approaches to preventing such accidents are given. Available and promising directions of the development of engineering tools and systems for ensuring miners’ safety are considered. The efficiency of using the scanning gas monitoring technology is demonstrated. Combining this technology with the automatic system of fire extinguishing allows the fire to be suppressed at the initial stage of ignition of the methane–air mixture.

Fraud detection through auditors’ manual review of accounting and financial records has traditionally relied on human experience and intuition. However, replicating this task using technological tools has represented a challenge for information security researchers. Natural language processing techniques, such as topic modeling, have been explored to extract information and categorize large sets of documents. Topic modeling, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), has recently gained popularity for discovering thematic structures in text collections. However, unsupervised topic modeling may not always produce the best results for specific tasks, such as fraud detection. Therefore, in the present work, we propose to use semi-supervised topic modeling, which allows the incorporation of specific knowledge of the study domain through the use of keywords to learn latent topics related to fraud. By leveraging relevant keywords, our proposed approach aims to identify patterns related to the vertices of the fraud triangle theory, providing more consistent and interpretable results for fraud detection. The model’s performance was evaluated by training with several datasets and testing it with another one that did not intervene in its training. The results showed efficient performance averages with a 7% increase in performance compared to a previous job. Overall, the study emphasizes the importance of deepening the analysis of fraud behaviors and proposing strategies to identify them proactively.

This paper discusses the literature that shows that declaring a zero vision for everything bad (including unsafe behaviours, incidents, injuries) does not prevent fatalities or major accidents. In fact, parts of the literature show that a reduction in minor badness increases the risk of major accidents and fatalities. This is true in several industries. Two families of explanations are discussed. The first is the concern that declaring a zero vision can reduce operational knowledge. The second is the unsubstantiated assumption that minor injuries and fatalities have the same causal pattern. In general, evidence for or against the utility of a zero vision is dogged by confounding factors (other variables responsible for changes in safety outcomes) and what Giddens called the double hermeneutic, where the results of such studies are only as stable as the attributions the original reporter (e.g. OHS official, case worker) and the subsequent analyst (e.g. researcher) made about a particular event. The paper concludes that in a complex, dynamic, resource-constrained and goal-conflicted world, zero is not an achievable target, but a zero commitment may be worth some encouragement.

Intersections are places where people mix together. People walking, biking, and operating automobiles negotiate turning and through movements at intersections. An intersection's geometry, which includes the size of the curb radii, has a particularly strong influence on the comfortable speed at which motorists can perform turns. Greater vehicular speed corresponds to increased crash risk, diminishing user reaction time and increasing pedestrian risk of injury when cars collide with pedestrians. Further, collisions between heavy vehicles and pedestrians compound vulnerable user risk of serious injury. Therefore, designing intersections that minimize a motorist's turning speed will likely reduce injuries to vulnerable roadway users. The last scenario describes a street retrofit aimed at decreasing the turning radius of passenger vehicles to its minimum of 15 ft., while still accommodating larger vehicles. This article illustrates how a truck pillow, which is small three-inch high mountable triangle installed at the apex of the curve, would affect a typical intersection.