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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.

In the past decades, extremely serious road accidents with a death toll over ten in each have become a severe public health problem in China. This study investigates risk factors contributing to extremely serious road accidents, which will be crucial for accident prevention. Collecting data from The Road Accident Statistical Annual Report openly issued by China's Traffic Management Bureau of the Public Security Ministry for the time period 2004-2015, we used the monthly case number of extreme serious road accidents as the dependent variable. We then selected ten risk factors as primary independent variables: professional driver, driving under influence (alcohol or drug), fatigue, vehicle type, overload, brake problem, weather, road classification, terrain, and region. The method of negative binominal regression was implemented to investigate the association between these risk factors and extremely serious road accidents. A total of 346 extremely serious road accidents were included in our analysis. On a national scale, we found that professional driver [incidence rate ratio (IRR): 1.10, 95% CI: 1.02-1.19], fatigue (IRR: 1.15, 95% CI: 1.03-1.29), large vehicle type (IRR: 1.11, 95% CI: 1.03-1.21), overload (IRR: 1.09, 95% CI: 1.03-1.16), and terrain (IRR: 1.09, 95% CI: 1.01-1.18) were significantly associated with extremely serious road accidents. Besides, separate analyses on western and non-western region indicated that both regions had shared risk factors as well as distinct factors. Our study identifies professional driver, fatigue, large vehicle type, overload, and terrain as significant risk factors of extremely serious road accidents in China, and targeted and preventative measures could be taken based on our findings.

Mortality statistics are critical to determine the burden of disease. Certain causes of death are prone to being misclassified on cause of death certificates. This poses a serious risk for public health and safety, as accurate death certificates form the basis for mortality statistics, which in turn are crucial for research, funding allocation and health interventions. This study uses generalised estimating equations and regression modelling to investigate for which cause of death categories suicide and accident deaths are misclassified as. National mortality statistics and autopsy rates from North America and Europe covering the past forty years were analysed to determine the associations between the different causes of death in cross-sectional and longitudinal models. We find that suicides and deaths by accidents are frequently mutually misclassified. We also find that suicides are frequently misclassified as drug use disorder deaths, in contrast to accident deaths, which are not misclassified as drug use disorder deaths. Furthermore, suicides do not seem to be misclassified as undetermined deaths or ill-defined deaths. The frequency of misclassification shows that the quality of death certificates should be improved, and autopsies may be used systematically to control the quality of death certificates.

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers.

Official suicide statistics for England are based on deaths given suicide verdicts and most cases given an open verdict following a coroner's inquest. Previous research indicates that some deaths given accidental verdicts are considered to be suicides by clinicians. Changes in coroners' use of different verdicts may bias suicide trend estimates. We investigated whether suicide trends may be over- or underestimated when they are based on deaths given suicide and open verdicts. Method Possible suicides assessed by 12 English coroners in 1990/91, 1998 and 2005 and assigned open, accident/misadventure or narrative verdicts were rated by three experienced suicide researchers according to the likelihood that they were suicides. Details of all suicide verdicts given by these coroners were also recorded. In 1990/91, 72.0% of researcher-defined suicides received a suicide verdict from the coroner, this decreased to 65.4% in 2005 (p trend < 0.01); equivalent figures for combined suicide and open verdicts were 95.4% (1990/91) and 86.7% (2005). Researcher-defined suicides with a verdict of accident/misadventure doubled over that period, from 4.6% to 9.1% (p < 0.01). Narrative verdict cases rose from zero in 1990/91 to 25 in 2005 (4.2% of researcher-defined suicides that year). In 1998 and 2005, 50.0% of the medicine poisoning deaths given accidental/misadventure verdicts were rated as suicide by the researchers. Between 1990/91 and 2005, the proportion of researcher-defined suicides given a suicide verdict by coroners decreased, largely due to an increased use of accident/misadventure verdicts, particularly for deaths involving poisoning. Consideration should be given to the inclusion of 'accidental' deaths by poisoning with medicines in the statistics available for monitoring suicides rates.

Abstract With the aging of society, the increase in the number of elderly drivers poses a potential hazard to road traffic safety. Therefore, accurately predicting the severity of possible traffic accidents of elderly drivers is crucial to ensure the safety of drivers and passengers. In this paper, a hybrid model based on the CTGAN-ResNet-XGBoost network is proposed for classifying the severity of the accidents of elderly drivers. The model was trained and tested using traffic accident data of the United States from 2018–2022. The hybrid model first generates a small amount of categorical data via the Conditional Tabular Generative Adversarial Network to address the dataset's category imbalance. Then, the balanced dataset is transformed into feature images using the DeepInsight method and feature extraction is performed using the residual neural network to improve the feature recognition ability of the classification model. Finally, the XGBoost model is used to classify the severity of the accident and the SHAP method is used to analyse the main features affecting the accident. The superior performance of the hybrid model is verified through experimental comparative analysis. The experimental results show that the hybrid model has a significant advantage in the prediction of driving risk for elderly drivers, that the causes of accidents for elderly drivers are different from those for younger drivers and that the characteristics of speed, seat belt use and driver's age are the main factors affecting the severity of accidents. The results of this study improve the accuracy and reliability of traffic accident severity prediction and provide more scientific support for traffic safety management.

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors.

In modern society, vehicle accidents have been a factor that has adversely affected national development for a long time. Many countries have tried to solve this issue, and various solutions have been studied. This study aims to design a process for analyzing vehicle accidents to support safety interventions. In the data preprocessing section, a resampling technique was used to solve the data imbalance problem. Then, we applied five different machine learning models for classification by applying hyperparameter optimization. After classification, model-agnostic interpretation techniques were used to interpret the results of a series of machine learning models. Through the above series of processes, we were able to design a process that analyzes vehicle accident data and derives the factors that affect the accident. The classification model that uses XGBoost with ENN (Edited Nearest Neighbor) shows almost 84.3% accuracy. As a result, for “Length” and “Volume”, we found that certain points (Length: 200 m, 29,233 veh/day) were more likely to have an accident. Moreover, variables, such as volume or the volume of heavy vehicle, the probability of an accident increases as the value increases, but in the case of “Lane width” and “Shoulder width”, it can be confirmed that the probability of occurrence decreases as the value increases. These interpretations have meaningful information that could suggest policy recommendations for reducing traffic accidents and can be helpful in establishing effective traffic accident countermeasures.

Traffic accidents on the road lead to serious personal and material damage. Furthermore, preventing secondary accidents caused by traffic accidents is crucial. As various technologies for detecting traffic accidents in videos using deep learning are being researched, this paper proposes a method to classify accident videos based on a video highlight detection network. To utilize video highlight detection for traffic accident classification, we generate information using the existing traffic accident videos. Moreover, we introduce the Car Crash Highlights Dataset (CCHD). This dataset contains a variety of weather conditions, such as snow, rain, and clear skies, as well as multiple types of traffic accidents. We compare and analyze the performance of various video highlight detection networks in traffic accident detection, thereby presenting an efficient video feature extraction method according to the accident and the optimal video highlight detection network. For the first time, we have applied video highlight detection networks to the task of traffic accident classification. In the task, the most superior video highlight detection network achieves a classification performance of up to 79.26% when using video, audio, and text as inputs, compared to using video and text alone. Moreover, we elaborated the analysis of our approach in the aspects of cross-modality interaction, self-attention and cross-attention, feature extraction, and negative loss.

Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on roads by designing and applying artificial intelligence and deep learning models. However, extracting useful information from the detected objects and determining the occurrence of traffic accidents are usually difficult. This paper proposes a CCTV frame-based hybrid traffic accident classification model that enables the identification of whether a frame includes accidents by generating object trajectories. The proposed model utilizes a Vision Transformer (ViT) and a Convolutional Neural Network (CNN) to extract latent representations from each frame and corresponding trajectories. The fusion of frame and trajectory features was performed to improve the traffic accident classification ability of the proposed hybrid method. In the experiments, the Car Accident Detection and Prediction (CADP) dataset was used to train the hybrid model, and the accuracy of the model was approximately 97%. The experimental results indicate that the proposed hybrid method demonstrates an improved classification performance compared to traditional models.