Accident Analysis Research Articles

What explains the provision of health insurance by Indonesian employers? A trend analysis of the National Labour Force Survey 2018-2022.

Indonesian laws mandate that every employer should provide health insurance and work accident insurance to their employees. Nevertheless, there is a significant gap in the coverage of employer-sponsored insurance among Indonesian workers. This study examines the coverage of employer-sponsored insurance and work accident insurance and analyses the characteristics of the uninsured working population in Indonesia. We analysed nationally representative cross-sectional data from the National Labour Force Survey (NLFS) conducted between 2018 and 2022. The primary dependent variable was the provision of health insurance and work accident insurance by employers. The independent variables included having any physical disabilities, number of working hours, duration of employment, labour union membership, earning at least the provincial minimum wage, having a written contract and working in high risk jobs. Logistic regression was employed using the R statistical software. The findings indicate that coverage of employer-sponsored health insurance is low in Indonesia-ranging from 36.1% in 2018 to 38.4% in 2022. Workers with a written contract, earning at least the provincial minimum wage, were members of a labour union, employed for at least 5 years and working more than 40 hours a week were more likely to be insured. By contrast, workers who had physical disabilities or were employed in high-risk jobs were less likely to be insured. Our study concludes that having a written employment contract is the single most influential factor that explains the provision of employer-sponsored health insurance in Indonesia. The country's labour laws should therefore formalize the provision of written employment contracts for all workers regardless of the type and nature of work. The existing laws on health insurance and work accident insurance should be enforced to ensure that employers meet their constitutionally mandated obligation of providing these types of insurance to their workers, particularly those engaged in high risk jobs.

The importance of road safety inspection for the improvement of pedestrian safety in traffic

Road safety inspection is a modern tool for improving road safety, that is, a preventive tool that is applied on roads in use. The goal of preventive action in road safety is to improve road safety, through reducing the number and consequences of traffic accidents. The road safety inspection involves the analysis of general data about the section, the analysis of traffic accidents, but also the analysis of specific road defects. 11 specific elements are analyzed, including vulnerable road users, including pedestrians, cyclists and motorcyclists. Vulnerable road users are particularly at risk because in the event of a traffic accident, they suffer more severe consequences because their body is not physically protected. This paper shows the importance of road safety inspections for the improvement of pedestrian safety in traffic.

A machine learning-based data-driven method for risk analysis of marine accidents

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods.

A Comprehensive Literature Review of Traditional and Spatial Methods for Black Spot Identification in Road Crash Analysis

The desire to greatly reduce traffic accidents, enhance road safety, and remove black spots from junctions has led to an increased interest in researching the various black spot (BS) identification systems. Conventional and spatial Black spot detection systems are created using a variety of concepts and methodologies; however, as each approach has a unique input and output based on zones, area, size, and other factors, it might not be suitable for every situation. The next step in this research is to conduct a comprehensive literature review of various Black spot detection approaches and the technology tools that support them. The goal is to identify, evaluate, and assess the acceptability, feasibility, accuracy, and appropriateness of various scenarios and parameters. Through accounting for several types of inadvertent correlation between the location of the accident and traffic information, the research discovered multiple methods for Black spot identification, each based on unique ideas and a range of methods and instruments. According to the study, each BS identification method has particular advantages and disadvantages. The principal objectives of this review are to assess the principal methods for evaluating traffic accidents, identify black spots, and explore possible distinctions between traditional and spatial traffic data analysis. To execute the statistical outcomes of occurrences that are examined, both the conventional method and GIS are used. This review paper provides an overview of three basic GIS approaches, explains a traditional method for identifying traffic incidents, and provides some often-used accident analysis tools for traffic safety.

A matched case-control analysis of autonomous vs human-driven vehicle accidents

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles’ levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous’ versus Human-Driven Vehicles’ accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Thermal analysis for evaluating the required cooling time of the emergency plan exemption for the Maanshan PWR units

The Maanshan plant is scheduled to be decommissioned in 2025. Taiwan Power Company, the owner of the plant, plans to apply exemption from the emergency plan (EP) while the plant is in decommissioning. One requirement for this exemption is that the fuel rod peak cladding temperature (PCT) must be below 565 °C for the postulated complete loss-of-coolant accident in the spent fuel pool (SFP), assuming cooled by air only. In this study, accident analysis models for the Maanshan SFP are developed. First, the decay heat power is calculated based on the specified cooling time. Second, air temperature entering the fuel assemblies is calculated using GOTHIC code, and then is provided as a boundary condition to the following PCT calculation. Finally, the PCT is calculated from the assembly model using Ansys Fluent code. The specified cooling time is acceptable or not is determined by the criterion that whether the PCT is below the limit. With the cooling time of 54 months, the calculated PCT is about 537 °C. Thus, the shortest cooling time allowed for applying EP exemption would be 54 months for the Maanshan units, which is much longer than that of the reference plants. This is because all the spent fuel assemblies are still stored in the SFP, resulting in a higher total decay power. A suggestion of removing the spent fuel assemblies to the dry storage facility is proposed if the Maanshan plant intends to apply the EP exemption while the plant is in decommissioning.

Analysis of traffic accidents on the highways of the timber industry complex of the Arkhangelsk region

In the article, based on the analysis of the current regulatory support for the export and transportation of roundwood, the authors state that currently in the sectoral regulatory and technical documents on roads of the forest complex SP 288.1325800.2016. A set of rules. The roads are forested. Rules of design and construction, and SP 318.1325800.2017. A set of rules. The roads are forested. The rules of operation are practically absent, updated in terms of traffic safety standards for the design, construction and operation of roads. The need to update the norms and requirements is dictated by the sharp dissonance that has arisen between the weight parameters and traction-dynamic characteristics of modern sorting trains, mainly of foreign production, and the unsatisfactory technical condition of forest roads. The prerequisites for road accidents that took place on the roads of the timber industry complex of the Arkhangelsk region in 2018–2022 are studied, the prevailing factors and conditions for their occurrence are identified, the content of the program of ongoing scientific research on improving traffic safety on forest highways is announced.

Understanding of causality and its mathematical representation in accident modeling

Accident models are a critical element of safety science as they provide a profound understanding of accident causality, which helps develop accident prevention and control strategies. The evolving accident situations and complex engineering systems offer significant challenges in understanding causality. This limits the realistic representation of causality in the accident model, impacting its usefulness. This paper presents a framework to demystify the understanding of causality and its mathematical representation in accident modeling. The framework uses the theory of causality, understanding interdependencies, constructing these elements in mathematical representations to formulate a mathematical accident model, and subsequently utilizing advanced probability theory and machine learning for accident analysis. The methodology is demonstrated in the case of the Champlain Tower South collapse. The ultimate objective of this work is that readers can use this framework for any engineering system accident modeling (i.e., construction, road, or process systems). The framework will help develop accident preventive and control strategies.

Study on the lower head failure in severe accidents Part II: Thermal-mechanical coupling behavior analysis on CAP1400 reactor lower head

Under severe accident conditions with core meltdown, large amounts of molten material relocate to the lower head at high temperatures. Significant thermal and mechanical loads may lead to failure of the reactor pressure vessel (RPV) lower head, spreading radioactive fission products. Lower head failure time and location are crucial for accident mitigation and consequence assessment. This study developed a thermodynamic failure analysis model for the lower head by investigating material failure mechanisms, coupling this into an Integrated Severe Accident Analysis code (ISAA), and conducting experimental validation and applied research. Part I mainly introduces the thermodynamic analysis module development, validation against OLHF experiments, and preliminary failure criteria analysis. Results indicate the model accurately predicts lower head failure behavior, and Larson-Miller life fraction and Hedl-Dorn parameter criteria accurately assess lower head integrity. Part II presents applied research after validation and preliminary analysis. Using the improved ISAA, melt pool behavior and lower head thermal–mechanical response were analyzed for a CAP1400 small break loss-of-coolant accident (SBLOCA), assuming multiple safety system failures. Results indicate the bottom of the lower head melt pool didn’t form a hard shell due to high heat release rate, only an oxide shell layer around the second layer. Decay heat from melt pool components cannot be dissipated, leading to lower head failure. Further External Reactor Vessel Cooling (ERVC) heat transfer model analysis and research are needed in ISAA. The two developed mechanical analysis models predict material failure at high temperatures, and the timing and location of lower head failure are consistent, at approximately 16,000 s at 25.38°±5.47° on the lower head. The predicted failure mode is similar to Part I validation, indicating both Larson-Miller life fraction and Hedl-Dorn parameter criteria provide consistent lower head integrity judgments.

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

Statistical Analysis of Occupational Fatal Accidents for Risk Assessment

Abstract According to Malaysia's Department of Occupational Safety and Health (DOSH), workplace accidents have increased dramatically as the country's economy has grown rapidly. The purpose of this study is to identify the trends that lead to fatal workplace accidents in Malaysia. This study investigated 505 fatal accident instances from fifteen industries, using both qualitative and quantitative methodologies. The DOSH discovered and reported these fatalities (2010-2020). Data was organized and coded, then analyzed using Spearman's rank order correlation, frequency analysis, Eta squared, Cramer's V, and Chi-square. Moreover, neurolinguistic programming was used to generate the word cloud and sentiment analysis. Furthermore, light gradient-boosting machine learning was used to better understand the causes of fatalities. The outcomes indicated that fatal falls from heights were the leading cause of fatal accidents (32%). General laborers were the most susceptible to fatal accidents (60%). Contract workers were more likely to die in construction accidents (64%). The findings of this study gave useful insights into workplace fatal accident trends and preventive measures throughout sectors.

Statistical Analysis of Traffic Accidents Involving Elderly Drivers

Statistical Analysis of Traffic Accidents Involving Elderly Drivers

Road Accident Analysis and Classification Using Machine Learning Algorithms

Abstract: India is facing a severe public health crisis characterized by a significant number of road accidents resulting in fatalities and life-altering injuries. This study undertakes a detailed analysis of road accidents in India using multi-year accident data to unveil recurring patterns and identify high-risk demographics. The research meticulously examines the primary causes of these accidents, including driver behavior such as speeding, and inadequate road infrastructure such as poor lighting, signage, potholes, and vehicle malfunctions. Moreover, the paper proposes a comprehensive approach to prevent these tragedies. It suggests strategies for enhancing road design and maintenance, enforcing traffic regulations more rigorously, and conducting targeted public awareness campaigns to promote responsible driving practices. By highlighting the alarming increase in fatalities and the substantial socio-economic costs associated with these accidents, this research emphasizes the critical need for immediate and effective interventions to establish a safer road environment for all citizens in India.

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

A Study on the Feng Shui Characteristics of “Pedestrian Accident Hotspots” in Seoul (Focusing on traffic accident analysis system data)

Analysis and Prevention of Road Accidents for Gautam Buddha Nagar District (U.P)

Road accidents pose a significant threat to public safety, necessitating proactive measures for analysis and prevention. This abstract delves into the analysis and prevention of road accidents in the Kasna to Surajpur region over a seven-year period from 2015 to 2021. Through data gathered from multiple police stations, the study aims to identify accident causes, locations, and frequencies to pinpoint blackspots for future road safety audits. Additionally, a novel solution leveraging Pascal's law is proposed to minimize accidents and we can determine the Number of vehicles and Types of vehicles. The system, triggered by vehicle movement, alerts drivers on the opposite side of the road to reduce speed, thus mitigating collision risks. This innovative approach offers promise in enhancing road safety and reducing accidents

Dynamic scenario deduction analysis for hazardous chemical accident based on CNN‐LSTM model with attention mechanism

AbstractThe evolution of hazardous chemical accidents (HCAs) is characterized by uncertainty and complexity. It is challenging for decision‐makers to expeditiously adapt emergency response plans in response to dynamically changing scenario states. This study proposes a data‐driven methodology for constructing accident scenarios and develops a novel hybrid deep learning model for scenario deduction analysis. This model aids in accurately predicting the evolution of HCAs, enabling emergency responders to prepare and implement targeted interventions proactively. First, a framework for constructing an accident scenario database is presented, based on the time‐sequential characteristics of accident progression. This framework employs a data‐driven approach to describe the evolution process of accident scenarios. Second, a deep learning model (CNN‐LSTM‐Attention) that integrates convolutional neural network (CNN), long short‐term memory (LSTM), and attention mechanism (AM) is developed for accident scenario deduction analysis. Finally, to illustrate practical application, a scenario database for HCAs is established. A major HCA case study is conducted to demonstrate the ability of this model to analyze various scenarios, thereby improving emergency decision‐making efficiency. Compared with algorithms such as CNN, LSTM, and CNN‐LSTM, the prediction accuracy of this method ranges from 86% to 93%, signifying an improvement of over 7%. This work provides a reliable framework for supporting decision‐making in emergency management.

Thermal-hydraulic characteristics analysis of unprotected accident and protection control strategy for helium-xenon cooled reactor system

The small modular helium-xenon cooled reactor has the advantages of simple and compact system structure, short construction cycle, light weight and small volume, which is very suitable for energy supply in remote area and small nuclear propulsion device. The research on accident characteristics and protection control strategies is very important to ensure the safe and reliable operation of the reactor system and mitigate the consequences of the accident. Therefore, the safety characteristics analysis program of helium-xenon cooled reactor system is developed based on Modelica language, and the transient verification of typical working conditions is carried out. The maximum relative error of the verification results was 7.91 %. Through the program, the unprotected transient conditions and corresponding protection control strategy transient conditions of external load loss accident, partial loss of heat sink accident, main valve accidental closing accident and Brayton unit efficiency step decline accident are simulated. The results show that the loss of all external loads will lead to rotation speed overspeed. The introduction of negative reactivity or bypass control can effectively control the rotation speed rise, but the introduction of negative reactivity is better than bypass control. Under unprotected control, the partial loss of the heat sink, the accidental closing of the main valve and the step down of the Brayton unit efficiency will all lead to the rapid decline of the rotation speed and mass flow rate, and there is a risk of the reactor temperature exceeding the limit. The rotation speed and mass flow rate can be effectively stabilized by load following to prevent the temperature exceeding the limit.

An integrated model combining BERT and tree-augmented naive Bayes for analyzing risk factors of construction accident

PurposeLearning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.Design/methodology/approachA total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.Findings The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.Originality/value This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

A novel data-driven method of ship collision risk evolution evaluation during real encounter situations

Aiming at realizing collision risk quantitative evaluation among encounter ships, a novel data-driven evolution model is proposed concerning encounter evolution in maritime transportation. A probabilistic velocity obstacle with an elliptic conflict region is constructed by taking into account uncertainty. The degree of and time to domain violation are introduced to quantify collision risk levels under varying velocities. Then, a ship collision risk evolution model is formulated by considering spatial attributes, macro-level and micro-level evolution based on a realistic collision avoidance decision. The model parameters and their weights are determined by statistical analysis of historical encounter scenarios and the characteristics of encounter stages. Therefore, the model encapsulates the statistical characteristics of actual data, which improves its practical values. The results of case studies indicate that the collision risk evolution model can properly reflect collision risk, so that collision evolution stages can be classified accordingly for rational anti-collision guidance. It brings new contributions to risk visualization, collision avoidance decision-making, and collision accident analysis and responsibility determination.