搜索结果：Accident; analysis and prevention

Road accidents are a significant negative outcome of transportation systems, causing injuries, fatalities, traffic congestion, and economic losses. As cities expand and the number of vehicles on the road increases, traffic accidents (TAs) have become a significant problem. Studies have shown that urban development plays a more significant role in transportation safety than previously thought. Low-income countries have higher fatality rates than high-income countries, according to the Permanent International Association of Road Congress (PIARC) and the World Health Organization (WHO). Predicting and preventing the occurrence of accidents and congestion is necessary worldwide, especially in developing countries where fatality rates are higher. The objective of this study is to examine and make a comparative analysis in low-income and high-income countries of the existing literature on the global challenge of car accidents and use its prediction techniques to enhance road safety and reduce traffic congestion. The study evaluates various approaches such as logistic regression, decision tree, random forest, deep neural network, support vector machine, random forest, K-nearest neighbors, Naïve Bayes, empirical Bayes, geospatial analysis methods, and UIMA, NSGA-II, and MOPS algorithms. The research identifies current challenges, prevention ideas, and future directions for preventing accidents and congestion on the road network. Integrating GIS-based spatial statistical methods and temporal data and utilizing advanced optimization algorithms and machine learning methods can result in accurate prediction models that can help identify accident hotspots and reduce congestions and enhance traffic safety and mitigate their occurrence. Effectively preventing urban traffic congestion requires the integration of spatial data into precise accident prediction models. By employing spatial analysis, road safety planning can be enhanced, high-risk areas can be identified, interventions can be evaluated, and resources can be optimally allocated to facilitate effective road safety measures and decision-making, especially in settings with limited resources. Therefore, it is crucial to consider ML and spatial analysis techniques and advanced optimization algorithms to enhance traffic flow control, in road safety research and transport planning efforts.

Accident prevention is of great significance in avoiding or reducing all kinds of casualties and economic losses, and is one of the main challenges for social sustainable development. Hence, it has been an active research field for many decades around the world. To master the research status of accident prevention, and explore the knowledge base and hot trends, 1294 papers from the WOS retrieval platform SCIE and SSCI databases from 1990 to 2021 were selected as data samples. Co-occurrence analysis, co-citation analysis, co-authorship analysis, and keyword analysis were performed on the literature on accident prevention research with bibliometric analysis methods. The study showed that the United States ranked first in the number of publications of any country/region and Georgia Inst Technol ranked first in the number of institutional publications. System analysis and accident model establishment, analysis of construction accidents, road accident prevention, and safety culture and safety climate are the knowledge base in the accident prevention studies and the core journals in this field are Safety Science, Accident Analysis and Prevention, Pediatrics, and Reliability Engineering & System Safety. There are four major research hotspots in accident prevention studies: routine accident prevention, model-based research, systems analysis and accident prediction, and occupational safety and public health research. At present, the basic theory and structural system of accident prevention research have been basically established, with many research directions and a wide range of frontier branches. Safety management, public safety, Bayesian networks, and simulation are the research frontiers of accident prevention.

Continuity of production and employee safety are the two main concerns of today's modern, automated, or intelligent factories. To increase safety and decrease risks in hostile working environments, manufacturers must comply with laws and regulations that are implemented in codes and standards. Unfortunately, even though companies comply with these laws and regulations and use the latest technologies, tragic accidents involving pressure vessels and piping still occur. Two such, recent events occurred in North America: in Canada, the pipeline spill of Journey Energy Inc. in 2017 and in the United States, the ExxonMobil refinery explosion in 2016. The storage of a fluid under pressure can represent a serious risk of dangerousness, not only to the employees, but also to the emergency services, the population in the vicinity and the environment. Currently, the technical aspects are the main concern of regulatory authorities (TSSA O. Reg. 220/01, RBQ B-1.1, r. 6.1, US National Board of Boiler and Pressure Vessel Inspectors (NBBI)) and the scientific community is focused on risk assessment related to structural integrity and leak tightness. The present paper surveys 50 accident cases that occurred in Canada and the United States from 1997 to 2017 related to pressure vessels and piping in petrochemical and nuclear industries. The causes of these accidents are various, but the authors focus on those related to a non-compliance with the applicable standards, namely CSA and ASME. The accidents are analyzed using a risk-ranking network and Venn diagram. Furthermore, using a case study, an in-depth analysis of an accident of a miniature boiler involving non-compliance with procedures, laws, regulations, code, and standards is conducted. The analysis of two-thirds of the documented accidents revealed that the main cause was of an organizational nature: non-compliance with standards, violation or absence of health and safety management, training deficiency, noncompliance with work procedures, and lack of clear and detailed maintenance procedures.

Accidents are preventable, but only if they are correctly described and understood. Since the mid-1980s accidents have come to be seen as the consequence of complex interactions rather than simple threads of causes and effects. Yet progress in accident models has not been matched by advances in methods. The author's work in several fields (aviation, power production, traffic safety, healthcare) made it clear that there is a practical need for constructive methods and this book presents the experiences and the state-of-the-art. The focus of the book is on accident prevention rather than accident analysis and unlike other books, has a proactive rather than reactive approach. The emphasis on design rather than analysis is a trend also found in other fields. Features of the book include: -A classification of barrier functions and barrier systems that will enable the reader to appreciate the diversity of barriers and to make informed decisions for system changes. -A perspective on how the understanding of accidents (the accident model) largely determines how the analysis is done and what can be achieved. The book critically assesses three types of accident models (sequential, epidemiological, systemic) and compares their strengths and weaknesses. -A specific accident model that captures the full complexity of systemic accidents. One consequence is that accidents can be prevented through a combination of performance monitoring and barrier functions, rather than through the elimination or encapsulation of causes. -A clearly described methodology for barrier analysis and accident prevention. Written in an accessible style, Barriers and Accident Prevention is designed to provide a stimulating and practical guide for industry professionals familiar with the general ideas of accidents and human error. The book is directed at those involved with accident analysis and system safety, such as managers of safety departments, risk and safety consultants, human factors professionals, and accident investigators. It is applicable to all major application areas such as aviation, ground transportation, maritime, process industries, healthcare and hospitals, communication systems, and service providers.

OBJECTIVE: We utilized job safety analysis (JSA) and hazard identification for work accident prevention in Para rubber wood sawmills, which aimed to investigate occupational health risk exposures and assess the health hazards at sawmills in the Trang Province, located in southern Thailand. METHODS: We conducted a cross-sectional study which included a walk-through survey, JSA, occupational risk assessment, and environmental samplings from March through September 2015 at four Para rubber wood sawmills. RESULTS: We identified potential occupational safety and health hazards associated with six main processes, including: 1) logging and cutting, 2) sawing the lumber into sheets, 3) planing and re-arranging, 4) vacuuming and wood preservation, 5) drying and planks re-arranging, and 6) grading, packing, and storing. Working in sawmills was associated with high risk of wood dust and noise exposure, occupational accidents injuring hands and feet, chemicals and fungicide exposure, and injury due to poor ergonomics or repetitive work. DISCUSSION: Several high-risk areas were identified from JSA and hazard identification of the working processes, especially high wood dust and noise exposure when sawing lumber into sheets and risk of occupational accidents of the hands and feet when struck by lumber. All workers were strongly recommended to use personal protective equipment in any working processes. Exposures should be controlled using local ventilation systems and reducing noise transmission. We recommend that the results from the risk assessment performed in this study be used to create an action plan for reducing occupational health hazards in Para rubber sawmills.

His main research interests include accident analysis and prevention.His research focuses on working conditions, quality of working life, traffic accident analysis and prevention, and occupational health and safety.Darçın has over 100 scientific publications in the field of accident analysis and prevention.The sixth chapter, "Scaling Accident Coping Strategies and Testing Coping Capability", aims to outline the basis for a systematic and consistent process for modelling civil emergency response.The framework of this process consists of assessment of the risk of occurrence, judgement if the assessed risks are acceptable compared to society's benefit, and ultimately provision of a generalized emergency service that will try to mitigate the consequential impact.In other words, this chapter studies and tests whether the preparedness, response and recovery capability is adequate and then presents a new paradigm for accident management, involving the need to quantitatively scale accident and disaster coping strategies and capability. XIV VThrough different perspectives, accident phenomenon has been handled from different aspects.Thus, the reader will be provided with up-to-date information and recommendations regarding the elimination or alleviation of the consequences of accidents.Finally, I would like to express my appreciation to the contributors of this book for their invaluable efforts to complete this book.

Introduction. Organizational and systemic analyses of workplace accidents do not include systematic methods of stimulating workers' learning and empowerment. Objective. The purpose of this study is to present an incident analysis in a railway passengers transport system using the Expanded Method of Workplace Accident Analysis and Prevention (MAPAEX). Methodology. MAPAEX is a collaborative tool that looks at the accident as an unexpected result of contradictions among the different elements of an activity system. A contradiction is a historically accumulated structural tension within and between activity systems. Identifying contradictions in the activity development subsidizes the elaboration of hypotheses about their origins. The proposition of solutions implies in modeling the activity system to overcome the identified contradictions and stimulate a movement towards a safer and more efficient production. Results. In this paper, a case study on the application of MAPAEX is presented with emphasis on the phases of analysis and solution modeling, which are centered on historicity, contradictions, and mediations in activity systems. The workers who participated analyzed an incident and understood the causes of the event in a systemic way, with emergence of their protagonism. With MAPAEX as a formative intervention, the researchers stimulated local actors to analyze problems consecutively, looking for innovative solutions through reconceptualization of the object/motive of the work activity. Discussion. This new accident analysis method combines activity ergonomics and activity theory. The multi-voice collaboration and a systemic approach develop expansive learning. Differences between this method and other systemic approaches are highlighted. Conclusions. MAPAEX showed to be a powerful tool for the development of analysis of workplace accidents, contributing with the innovation of concepts and methodological and practical procedures.

Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve the problems in multi-factor complex disaster prediction, this paper proposes a new method for risk prediction and factorial risk analysis. Coal and gas outburst accidents were selected as research objects. First, a new coal and gas outburst prediction model was established that consists of 4 levels and 14 factors. Then, the Improved Fruit Fly Optimization Algorithm (IFOA) and the General Regression Neural Network (GRNN) algorithm were combined to establish the IFOA-GRNN prediction model. After that, the sensitivity analysis method was applied to the analysis of the sensitive factors of coal and gas outbursts. Finally, an apriori algorithm was used to mine the disaster information. The method proposed in this paper was applied to the Pingdingshan No. 8 Min. The application results show that the IFOA-GRNN algorithm proposed in this paper has an accuracy rate of 100% for the prediction of accident risk levels. Compared with the Back Propagation (BP), GRNN and FOA-GRNN algorithms, IFOA-GRNN has the characteristics of a smaller prediction error, higher stability and faster convergence. The sensitivity analysis method can judge the sensitive factors of coal and gas outbursts without knowing the mechanisms of the accident. The a priori algorithm can perform good data mining on the combination of high frequency factors leading to accidents and the relationships between the coal and gas outburst levels and factors. The data mining results are very helpful for the prevention and management of coal and gas outbursts.

Construction is the most dangerous land-based work sector in Europe and the United States The cost of accidents has received much attention in the recent past, and online interactive tools were developed to assess the cost of accidents to organizations. Online tools and other sources of information on costs of accidents in the construction industry were a useful development but failed to support the decision-making process in regard to construction health and safety measures. A cost-benefit analysis (CBA) methodology is presented that would enable contractors to assess the true cost of accidents prevention and the associated benefits of accident prevention as part of pre- and postcontract project evaluation. The research investigated the cost and benefit of accident prevention, with a view to drawing attention to the economic consequences of effective/ineffective management of health and safety by contractors. A quantitative research methodology was employed in investigating these costs and benefits within the UK construction industry. The results of ratio analyses indicate that the benefits of accident prevention far outweigh the costs of accident prevention by a ratio of approximately 3∶1. Further, the results demonstrate that for every £1 spent on accident prevention, contractors gained £3 as benefits. The results also show that small contractors spend relatively higher proportions of their turnover on accident prevention than medium- and large-sized contractors and that small- and medium-sized contractors gain relatively higher proportions of their turnover, in total, as benefits of accident prevention than large contractors. It is concluded that the CBA method can provide a guide to contractor’s decision making in regard to accident prevention. When acted upon, the method has the potential to contribute to a reduction in costs, deaths, and injuries in the UK construction industry and possibly in other areas internationally.

Traffic accidents are a significant factor leading to injuries and property losses, prompting extensive research in the field of traffic safety. However, previous studies, whether focused on static environment assessment, dynamic driving analysis, pre-accident prediction, or post-accident rule checks, have often been conducted independently. Our introduces V2X Environmental Perception Multi-modal Large Model AccidentGPT for accident analysis and prevention. AccidentGPT establishes a multi-modal information interaction framework based on multisensory perception. It adopts a holistic approach to address traffic safety issues, providing environmental perception for autonomous vehicles to avoid collisions and maintain control. In human-driven vehicles, it offers proactive safety warnings, blind spot alerts, and driving suggestions through human-machine dialogue. Additionally, it aids traffic police and management agencies in considering factors such as pedestrians, vehicles, roads, and the environment for intelligent real-time analysis of traffic safety. The system also conducts a thorough analysis of accident causes and post-accident liabilities, making it the first large-scale model to integrate comprehensive scene understanding into traffic safety research. Project page: https://accidentgpt.github.io

Hazardous chemicals are inflammable, explosive, and/or toxic and are prone to accidental leakage, fire, and explosion during production, storage, and transportation. It is time-consuming and laborious to study the properties of hazardous chemicals individually for systematic accident prevention because of the wide variety of hazardous chemicals and conditions resulting in accidents. Moreover, accidents have numerous causes, and the relationships among the causative factors are complex. It is a problem that is difficult to accurately identify the effects of correlations among accident factors and determine the laws governing accident occurrence. In this paper, we propose a generic method of hazardous chemical accident prevention based on K-means clustering analysis of incident information to illustrate how to solve the problems. A database of hazardous chemical incidents was constructed, and a K-means clustering algorithm was adopted to classify the incidents. The numbers of occurrences and frequencies of the words in the textual descriptions of the consequences, processes, and causes of hazardous chemical incidents were counted and calculated using a class-based method. For words with a high frequency, risk scenarios were constructed, checklist items of newly revealed dangers were developed, and a system for systematic risk assessment and accident prevention was established. Finally, the information on hazardous material transportation incidents in the Pipeline and Hazardous Materials Safety Administration database of the U.S. Department of Transportation from 2009 to 2018 had been taken as an example to illustrate the method application. The results demonstrate that the proposed method of hazardous chemical accident prevention can be used to improve accident classification. The classification results make it possible to determine the optimal sequence of key targets on which to focus and the requirements for accident prevention and formulate preventive measures. Thus, they provide a technical basis for accident prevention.

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Maritime transport faces new safety-related challenges resulting from constantly increasing traffic density, along with increasing dimensions of ships. Consequently, the number of new concepts related to Decision Support Systems (DSSs) supporting safe shipborne operations in the presence of reduced ship manning is rapidly growing, both in academia and industry. However, there is a lack of a systematic description of the state-of-the-art in this field. Moreover, there is no comprehensive overview of the level of technology readiness of proposed concepts. Therefore, this paper presents an analysis aiming at (1) increasing the understanding of the structure and contents of the academic field concerned with this topic; (2) determining and mapping scientific networks in this domain; (3) analyzing and visualizing Technology Readiness Level (TRL) of analyzed systems. Bibliometric methods are utilized to depict the domain of onboard DSSs for operations focused on safety ensurance and accident prevention. The scientific literature is reviewed in a systematic way using a comparative analysis of existing tools. The results indicate that there are relatively many developments in selected DSS categories, such as collision avoidance and ship routing. However, even in these categories some issues and gaps still remain, so further improvements are needed. The analysis indicates a relatively low level of technology readiness of tools and concepts presented in academic literature. This signifies a need to move beyond the conceptual stages toward demonstration and validation in realistic, operating environments.

This is the formal refereed proceedings of the fifth Australian Aviation Psychology Symposium. The symposium had a diverse range of contributions and development workshops, bringing together practitioners from aviation psychology and human factors, flight operations management, safety managers, pilots, cabin crew, air traffic controllers, engineering and maintenance personnel, air safety investigators, staff from manufacturers and regulatory bodies and applied aviation industry researchers and academics.The volume expands the contribution of aviation psychology and human factors to the aviation industry within the Asia Pacific region, developing the safety, efficiency and viability of the industry. It is a forward-looking work, providing strategies for psychology and human factors to increase the safe and effective functioning of aviation organizations and systems, pertinent to both civil and military operations.

The investigation and modelling of aviation accident causation is dominated by linear models. Aviation is, however, a complex system and as such suffers from being artificially manipulated into non-complex models and methods. This book addresses this issue by developing a new approach to investigating aviation accident causation through information networks. These networks centralise communication and the flow of information as key indicators of a system’s health and risk. This holistic approach focuses on the system environment, the activity that takes place within it, the strategies used to conduct this activity, the way in which the constituent parts of the system (both human and non-human) interact and the behaviour required. Each stage of this book identifies and expands upon the potential of the information network approach, maintaining firm focus on the overall health of a system. The book’s new model offers many potential developments and some key areas are studied in this research. Through the centralisation of barriers and information nodes the method can be applied to almost any situation. The application of Bayesian mathematics to historical data populations provides scope for studying error migration and barrier manipulation. The book also provides application of these predictions to a flight simulator study for the purposes of validation. Beyond this it also discusses the applicability of the approach to industry. Through working with a legacy airline the methods discussed are used as the basis for a new and prospective safety management system.

OBJECTIVE: To assess injury patterns attributable to horse kicks, to raise the issue of preventive measures, and to evaluate the role of modern accident and emergency department computer software. METHODS: Data analysis using a new kind of full electronic medical record. RESULTS: Seventeen kicked equestrians were unmounted at the time of injury. Eight of seventeen patients sustained contusions of the extremities, the back, and the trunk. In nine patients an isolated facial injury was diagnosed. Five of nine patients needed referrals to the department of plastic surgery because of the complexity of the facial soft tissue wounds. Three underwent maxillofacial surgery. CONCLUSION: Clinical: the equestrian community may underestimate the risk of severe injuries attributable to hoof kicks, especially while handling the horse. Educational lectures and the distribution of educational literature should be promoted. The introduction of additional face shields may be protective. Software related issue: the handling of an increasing amount of medical data makes a development in computerisation of emergency units necessary. Thus the increasing utilisation of new computer technology could have a significant influence on accident analysis and prevention and the quality of research in the future.

This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal smartphone. For the driver behavior monitoring, the smartphone's cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following driver's online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.

Failures during the drilling and exploitation of hydrocarbons that result in catastrophic offshore oil and gas accidents are relatively rare but if they occur the consequences can be catastrophic in terms of loss of life and environmental damage. Therefore, to gain insight into their prevention, the largest major offshore oil and gas accidents, those with more than 10 fatalities or with a large environmental impact, are analyzed in this article. Special attention is placed on fire as a cause and a consequence. Relevant technological and legislative changes and updates regarding safety that have followed such accidents and that can prevent potential future similar misfortunes are evaluated. Two main approaches to safety are compared: (1) the American prescriptive vs. (2) the European goal-oriented approach. The main causes of accidents are tested statistically in respect of failure probability, where the exact confidence limits for the estimated probabilities are computed. The results of the statistical test based on exact confidence intervals show that there is no significant difference between the analysed factors, which describe the main causes of offshore oil and gas accidents. Based on the small but carefully chosen group of 24 of the largest accidents, it can be concluded that there is no evidence of a difference between the categories of the main causes of accidents.

Increased cockpit automation on modern jet aircraft aim to reduce the risk of Undesired Aircraft State (UAS) instances such as Loss of Control in Flight (LOC-I). Although LOC-I globally accounts for only 9% of all analysed accidents IATA has reported that it was responsible for 58% of all accident fatalities in 2017. The focus of this paper is to answer whether Threat and Error Management and Crew Resources Management (CRM) techniques are an efficient risk management tool when facing a LOC-I threat. Three LOC-I final aircraft accident reports were analysed to understand the structure of Human Factors (HF) during these flights. Methods from the HF field such as the Generic Error Modelling System (GEMS) and Skill-, Rule-and Knowledge-based (SRK) error approach provided invaluable insights to identify potential findings. A holistic investigation of cognitive structures in flight path management helped to visualise latent conditions and cognitively demanding tasks during LOC-I in routine operations. Bearing in mind the limited number of cases considered in this paper it should be considered as an overview in LOC-I accident analysis. It shows that leadership and teamwork, as essential aspects of CRM training, can serve as key strategies to mitigate HF problems and LOC-I risks.