Accident Factors Research Articles

A data-driven ISM-BN model for safety analysis of inland shipping in the Pearl River Basin

Inland shipping of the Pearl River plays an important role in the Chinese shipping system. To ensure navigation safety, we collect reports of maritime accidents from 2015 to 2022 in the Pearl River basin. This article extracts influencing factors by collecting the experience of inland waterway safety navigation and analyzing accident reports. Then, this paper uses the interpretative structural modeling method (ISM) to build a correlation model. Using a data-driven Bayesian network (BN), it analyzes the impact of various factors on the safety navigation in the Pearl River. The model validation is completed by compared with tree augmented naive Bayes classifiers (TAN) network using the same validation samples, through validation with the test set, the prediction accuracy has improved by 25%. The results indicate factors such as vessel type, accident month, accident day and time, etc. have a significant impact on the safety of navigation in the inland Pearl River waterway. The method used can identify important risk factors for accidents and the average predictive probability of validation samples reaches 87.03%. These research results could be extended to maritime management efforts in the Pearl River Basin.

Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within this network, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Path analysis of coal mine accident risk factors based on the 24Model.

Coal mine accidents seriously affect China's coal safety production and sustainable development. The present study aimed to reveal the risk factors in coal mine accidents and explore the causal relationship among risk factors. This study utilized text mining to analyse 450 coal mine accident reports, identifying 50 risk factors and efficiently mapping them into the 24Model. The association rule algorithm was then used to mine the strong association rules among the risk factors within the 24Model, establishing the interaction mechanism among them. Based on the strong association rules, related hypotheses were proposed. Finally, the hierarchical and logical relationships of risk factors within the 24Model were analysed, and the causal and mediating effects were tested by path analysis. The safety management system has a direct effect on unsafe acts, unsafe conditions, habitual behaviour and organizational safety culture. Moreover, external influence has an effect on unsafe acts, organizational safety culture and habitual behaviour through the mediating effect of the safety management system. Based on the results obtained, this study proposes a series of specific measures to prevent risks in coal mines, providing a new perspective for the analysis and prevention of accidents.

Socioeconomic and behavioral factors of road traffic accidents among drivers in Ethiopia: systematic review and meta analysis

BackgroundRoad traffic accidents (RTAs) are a pressing public health issue in Ethiopia, exacerbated by rapid urbanization and motorization. Despite efforts, escalating rates persist due to various factors like human behavior and road infrastructure deficiencies. This study addresses the need for comprehensive data on RTAs in Ethiopia, aiming to uncover socioeconomic and behavioral factors to inform evidence-based policies.MethodsA systematic review and meta-analysis were conducted from March to April 2024, adhering to PRISMA guidelines. Major databases like Google Scholar, PubMed, and the Cochrane Library were searched using specific keywords related to RTAs and Ethiopia. Studies conducted between 2014 and 2024 were included, with eligibility criteria. Data extraction followed a standardized Microsoft Excel format. Study quality was assessed using the JBI checklist, with high-quality articles included. Statistical analysis, conducted using STATA version 17, included descriptive analysis, forest plots for RTA prevalence, and examination of publication bias using Egger and Begs tests. Subgroup analysis and meta-regression explored moderating factors and sources of heterogeneity.ResultsThe systematic review identified eight studies meeting inclusion criteria, conducted between 2014 and 2022. RTA prevalence was 36% (95% CI: 23; 49). Sensitivity analysis confirmed the robustness of the estimated prevalence. Publication bias was detected via Egger’s test, though Begg’s test was non-significant. Meta-regression found no significant influence of publication year or sample size on RTA prevalence. Socioeconomic factors like income level and driving experience, alongside behavioral factors like traffic rule violations and substance use, were identified as key drivers of RTAs in Ethiopia.ConclusionsThe study highlights the significant burden of road traffic accidents in Ethiopia, influenced by socioeconomic and behavioral factors. Socioeconomic factors such as income level and driving experience, along with behavioral factors like traffic rule violations and substance use, emerged as prominent determinants of RTAs. Policymakers should prioritize evidence-based interventions targeting these factors to improve road safety nationwide.

Critical Connections: Enhancing Aviation Safety Through Network Analysis

The objective of this study is to explore the influence of human factors on aviation accidents across different aircraft types using Network Analysis. This research specifically analyses centrality metrics to pinpoint the most influential errors, providing a more detailed understanding of their impact on aviation safety, while prior studies have acknowledged the role of human factors. The study employed Network Analysis using Python's NetworkX library. A dataset derived from the NTSB aviation accident database is used. Bipartite networks were constructed for each aircraft type, with nodes representing accidents and human error categories and edges indicating their relationships. The analysis revealed that pilot error consistently showed the highest degree centrality across all aircraft types, indicating its frequent involvement in accidents. Closeness centrality further highlighted the central role of pilot error, showing its significant influence on the network. The findings emphasized the critical importance of addressing pilot errors to improve aviation safety. While crew errors and other human errors were less frequent, their presence in the data suggested that a comprehensive safety strategy must also consider these factors. The results of this study demonstrate that pilot error is the most influential human factor in aviation accidents across various aircraft types. By focusing on targeted interventions such as enhanced pilot training and stricter safety protocols, the aviation industry can significantly reduce accident rates.

A systematic literature review on occupational accident factors in the rail construction industry: lessons learned from a quarter-century of studies globally.

The rail construction industry is notable for its large scale, substantial investment, extensive stakeholders involvement, long construction period, and intricate operation and technology. This industry is among the most dangerous due to the highest number of occupational accident cases worldwide. Therefore, it is crucial to analyse and identify the existing literature on occupational accident factors in rail construction. To address the research aim, the study identified the factors that contribute to occupational accidents using systematic review methodology. This systematic literature review adheres to the rigorous Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Relevant publications from the past 25 years were retrieved from Scopus, Web of Science (WoS), and Science Direct electronic databases. Through a meticulous review of 43 selected publications, five accident factor themes were discovered: worker, workplace, materials and equipment, organizational, and environmental influences. The detailed analysis of these themes has led to the identification of 19 specific sub-factors within these categories, providing a granular understanding of the intricate elements contributing to accidents. This study offers a foundational understanding of accident factors in the rail construction industry, paving the way for targeted OSH interventions aimed at preventing occupational accidents in the future.

Major injuries and associated factors in traffic accidents among motorcycle food delivery riders during the COVID-19 pandemic in Thailand

Motorcycle food delivery riders (MFDRs) are at a higher risk of traffic accidents compared to regular motorcyclists. Their safety is a significant issue in many developing countries, particularly in Thailand, which has the highest rate of motorcyclist fatalities globally. This study aimed to determine the prevalence of traffic accidents and explore the association between risky riding behaviors, concerns for working conditions, and accidents among MFDRs. This cross-sectional study was conducted in Chiang Mai, Thailand. Data were collected from a total of 709 MFDRs through an online platform. Collected data included working conditions, risky riding behaviors, concerns about their work conditions, and experience with traffic accidents. In the past six months, the incidence of traffic accidents among MFDRs was 18.9 %., with 2.4 % experiencing hospitalized major injuries, 11.4 % having non-hospitalized major injuries, and 5.1 % having minor injuries. A total of 84.3 % of MFDRs exhibited at least one risky riding behavior, while 5.8 % engaged in ten or more such behaviors. Multivariable logistic regression analysis with adjustments for sex, age, and body mass index indicates that red-light running (adjusted odds ratio, aOR 2.78, 95 % CI 1.07 to 7.21) and being concerned for customer behaviors (aOR 2.66, 95 % CI 1.26 to 5.62) were significantly associated with major injuries. Additionally, factors such as sleeping less than 6 h per day (aOR 1.68, 95 % CI 1.10 to 2.56), riding the wrong way (aOR 2.49, 95 % CI 1.12 to 5.52), and having concern for customer behaviors (aOR 1.91, 95 % CI 1.03 to 3.55) were associated with total number of accidents. Red-light running and concerns for customer behavior in MFDRs are associated with a higher risk of traffic accidents with major injuries. Effective strategies and interventions should focus on strengthening the enforcement of traffic risk behaviors and encouraging food delivery companies to establish regulations relating to the workers’ concerns.

A combined method for the evaluation of contributing factors to maritime dangerous goods transport accidents

This paper evaluates the contributing factors to maritime dangerous goods (DG) transport accidents by integrating the Entropy Weight (EW) and Grey Relational Analysis (GRA) methods. For this purpose, investigation reports of maritime DG transport accidents that occurred worldwide between 2000 and 2023 are derived from the International Maritime Organization’s Integrated Shipping Information System (IMO GISIS) database’s Marine Casualties and Incidents (MCI) module. Eleven main ship operations and thirteen primary causes were selected by analysing accident investigation reports. The weights of main ship operations are calculated utilizing the EW method. The correlational degrees of the primary causes are then calculated using the GRA method. Most maritime DG transport accidents occur during unberthing, bunkering, and pilotage operations. The most common contributing factors of maritime DG transport accidents are collisions and occupational accidents. Specifically, maritime DG transport accidents are most likely to be caused by collisions during sailing, passage, maneuvering, and bunkering operations, as well as occupational accidents during cargo loading, anchoring, berthing, and mooring operations. The results of this paper can support stakeholders in developing the needed policies to guarantee the safety of maritime DG transport.

A risk assessment model of spontaneous combustion for sulfide ores using Bayesian network combined with grounded theory

Sulfide ores, collectively referred to as a class of minerals with extensive applications, are increasingly susceptible to spontaneous combustion incidents as the mining environment evolves. The spontaneous combustion of sulfide ores is a complex nonlinear process, and the mechanisms of which remain not fully understood. Consequently, it is challenging to delineate the accident trajectory through traditional accident causation models and subsequently construct a probabilistic model for spontaneous combustion risk. To address this issue, this paper proposed a risk assessment model based on grounded theory and Bayesian network. The advantage of this model is that it employs grounded theory to categorize and organize factors influencing the occurrence of accidents, thereby forming a clear accident trajectory and providing a framework and basis for constructing a probabilistic assessment model. Furthermore, the model presented in this paper introduces the D number theory and the Noisy-OR model to handle uncertainties in the risk assessment process and to establish more rational conditional probability tables. The findings suggest that environmental temperature, heat dissipation conditions, safety awareness, and safety skills are the most influential factors in spontaneous combustion incidents when considering a particular point in time. However, when considering the entire process of sulfide ore pile spontaneous combustion, the temperature of the ore pile is the most critical accident factor to monitor. Therefore, the key to suppressing the spontaneous combustion of sulfide ores lies in reducing environmental temperatures, enhancing heat dissipation, and elevating the safety awareness of relevant personnel.

Injury Patterns and Contributing Factors in Two-Wheeler Road Traffic Accidents: A Cross-Sectional Analysis from a Tertiary Care Hospital in Chengalpattu District, Tamil Nadu

Injury Patterns and Contributing Factors in Two-Wheeler Road Traffic Accidents: A Cross-Sectional Analysis from a Tertiary Care Hospital in Chengalpattu District, Tamil Nadu

Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies.

A Novel Framework for Identifying Major Fishing Vessel Accidents and Their Key Influencing Factors

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

Risk analysis of accident-causing evolution in chemical laboratory based on complex network

Chemical laboratories usually have many high risks from various dangerous chemicals, dangerous devices, and experimental operators involved in the laboratory. Once a severe accident occurs, the accident consequence will significantly impact scientific research innovation and social progress. Aiming at laboratory safety problems, an accident evolution model is established to reveal the critical accident-causing factors and the most likely evolutionary path of accidents in the process of accident evolution. Firstly, the 24 model is used to identify the cause factors of laboratory accidents and the causal relationship between factors. Then, the complex network model of laboratory accident evolution is constructed with accident-causing factors as nodes and causal relationships between factors as edges. Secondly, using the TOPSIS method, four typical complex network characteristic indexes, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, are constructed into a comprehensive evaluation index to reveal the key influencing factors in the accident evolution process. Finally, the fuzzy number is introduced to quantify the uncertainty of the accident evolution path. The most likely evolutionary path is obtained through the Dijkstra and risk entropy theory. Based on the data of 151 laboratory accidents from 2001 to 2022, the accident evolution analysis of chemical laboratories was carried out. The evolution results show that the maximum probability value is the evolution path of fire accidents, and the minimum probability value is the evolution path of electric shock accidents, which accords with the general statistical law of accidents. When applied to the actual laboratory, the safety management level of the laboratory can be effectively improved by controlling the key influencing factors and cutting off the accident evolution path.

Analysis Of Worker Nutrition And Workload With Fatigue Levet At SPBE PD Bumi Wiralodra Indramayu (PD BWI) Year 2020

Fatigue at work is one of the health and safety problems that can be a risk factor for accidents at work. Fatigue can be influenced by many factors, one of which is excessive nutritional intake and heavy workload. This study aims to determine the analysis of nutritional intake and to determine the relationship between nutritional intake and workload with fatigue levels in SPBE PD. Bumi Wiralodra Indramayu. This type of research is an analytic observational study using a cross sectional design. The sample in this study were workers at SPBE PD. Bumi Wiralodra Indramayu as many as 36 respondents. The analysis used was the chi square test. The results of this study indicate that: There were 24 respondents (66.7%) who felt tired and 12 (33.3%) who felt not tired. Respondents who had more nutritional intake were 33 people (91.7%) and those who felt that they had less nutritional intake were 3 people (8.3%). Respondents who have heavy workload are 16 people (44.4%) and respondents who have light work are 20 people (55.6%). There is no relationship between workers' nutritional intake and fatigue level with a P-value of 0.359. There is a relationship between workload and fatigue level with a P-value of 0.003. Companies are expected to pay more attention to working hours for workers, especially in the production section, so that no workers do work outside of operational hours.

Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots’ precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees’ face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees’ fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.

Spatiotemporal Heterogeneity Analysis of Provincial Road Traffic Accidents and Its Influencing Factors in China

To objectively evaluate the road traffic safety levels across different provinces in China, this study investigated the spatiotemporal heterogeneity characteristics of macro factors influencing road traffic accidents. Panel data from 31 provinces in China from 2009 to 2021 were collected, and after data preprocessing, traffic accident data were selected as the dependent variables. Population size, economic level, motorization level, highway mileage, unemployment rate, and passenger volume were selected as explanatory variables. Based on the spatiotemporal non-stationarity testing of traffic accident data, three models, namely, ordinary least squares (OLS), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR), were constructed for empirical research. The results showed that the spatiotemporal heterogeneity characterizing the macro factors of traffic accidents could not be ignored. In terms of impact effects, highway mileage, population size, motorization level and passenger volume had positive promoting effects on road traffic accidents, while economic level and unemployment rate mainly exhibited negative inhibitory effects. In terms of impact magnitude, highway mileage had the greatest impact on traffic accidents, followed by population size, motorization level, and passenger volume. Comparatively, the impact magnitude of economic level and unemployment rate was relatively small. The conclusions were aimed at contributing to the objective evaluation of road traffic safety levels in different provinces and providing a basis for the formulation of reasonable macro traffic safety planning and management decisions. The findings offer valuable insights that can be used to optimize regional traffic safety policies and strategies, thereby enhancing road safety.

Exploration of Traffic Accident-Based Pilot Zones for Autonomous Vehicle Safety Validation

Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model’s classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method’s effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation.

Multiple Logistic Regression Model for Assessing the Risk Factors of Traffic Accidents: Khon Kaen Model.

Thailand has consistently held the highest global ranking in traffic accidents since 2017, with Khon Kaen displaying the highest mortality rate in the Department of Disease Control Region 7. This study aims to utilize Injury Surveillance (IS) data to identify risk factors associated with emergency room (ER) outcomes at the Emergency Department of Khon Kaen hospital in Khon Kaen Municipality. Data from the Injury Surveillance system's (IS system) database were collected, focusing on severity outcomes, time of events, and risk behaviors from January 1, 2008, to December 31, 2021. Data analysis was conducted using the R program, employing the Chi-square or independent T test to compare results and analyze associations between potential risk factors and ER outcomes. Multiple logistic regression (MLR) was used for classification analysis, and a confusion matrix was applied to evaluate the performance of the models. MLR analysis revealed that being male, age, alcohol consumption, and nighttime driving were more likely to increase the probability of severity outcomes. Being male, age, alcohol consumption, and nighttime driving are identified as potential risk factors contributing to the development of severity outcomes following traffic accidents.

Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors

Artificial intelligence seems to be a new point of view to classical problems that, in the past, could not be understood in depth, leaving certain gaps in each knowledge area. As an example of this, maritime accidents are one of the most recognised international problems, with clear environmental and human life consequences. From the beginning, statistical studies have shown that not only the typical sampled variables must be considered but the accidents are related to human factors that, at the same time, are related to some variables like fatigue that cannot be easily sampled. In this research work, the use of machine learning algorithms on over 300 maritime accidents is proposed to identify the relationship between human factors and the main variables. The results showed that compliance with the minimum crew members and ship length are the two most relevant variables related to each accident for the Spanish Search and Rescue (SAR) region, as well as the characteristics of the ships. These accidents could be understood as three main groups of accidents related to the general tendency to not meet the minimum number of crew members and its difference in the year of construction of the ship. Finally, it was possible to use neural networks to model accidents with sufficient accuracy (determination factor higher than 0.60), which is particularly interesting in the context of a control system for maritime transport.