Operator-centred local error management in air traffic control

Introduction: A large number of air traffic control occurrences take place without resulting in loss of separation between aircraft. Unfortunately such occurrences are seldom reported and therefore not used for disclosing system weaknesses, such as inappropriate methods and procedures.The ATCC (Air Traffic Control Centre) Malmoe made a trial with local reporting of “learning occurrences”. The trial was ATCO-(Air Traffic Controller) centred. The study objectives were to evaluate if ATCOs would start to report after a defined training and marketing effort, if they could identify system weaknesses, if concrete actions for safety improvement would be taken as a result of the trial and to what extent expert support was necessary.Method and material: The trial period was eight months. The ATCO report would be made on a simple form, available on site. These reports would then be analysed in groups and the marketing and feedback efforts would be co-ordinated by the local flight safety group.Results: 43 reports were filed and analysed during the trial period. The initial motivational training and marketing was considered adequate. During the group discussions, the ATCOs identified system weaknesses within 40 of the reports. The resulting safety improvement actions included: the ATCC unit becoming more active in contacting the pilots and airline companies, the renaming of some waypoints (due to name similarities), the implementation of safer procedures when relieving ATCOs, the training of ATCOs in cockpit flight management systems, and the initiation of a research project primarily concerned with ATCO mental overload.Expert support was required in the beginning to help ATCOs focus on the system rather than on the individual.     

Developing the understanding of underlying causes of construction fatal accidents

A study of a small sample of construction fatal accidents was used to pilot a potential method of analysis for the UK Health and Safety Executive (HSE) Construction Division. The work was conducted in the context of a Governmental Inquiry into construction fatalities in 2009, but has been developed further since. The sample of 26 accidents (28 fatalities) was drawn from the 211 fatal accidents in the years ending 2006–2008, to be broadly illustrative of the range of accident characteristics. The accidents were analysed on the basis of available inspectorate reports and structured interviews with the investigating inspectors. A standard method of classification on four levels was developed, based on the Human Factors Analysis and Classification System (HFACS) classification of errors and task level factors, with additional categories covering the organisational and regulatory/market levels of the system. The results showed a concentration of underlying factors associated with inadequacies in planning and risk assessment, competence assurance, hardware design, purchase and installation, and contracting strategy. These findings were partially validated by comparing them with another sample of 50 accidents analysed earlier by the HSE. This paper describes the development and testing of the investigation and classification method and how it is being further developed since the initial study. It also provides a summary of the findings relating to underlying causes.     

HFACS中不安全监管因素与事故致因24 Model中不安全动作因素的对比分析

为了使HFACS这种事故分析方法能够在我国各种类型事故中得到应用,研究了HFACS的不安全监管因素中各指标的定义和分类,并将其与事故致因24 Model中的不安全动作因素进行对比,得出两者的对应关系和各自特点。结果表明:HFACS中多数不安全监管因素属于24 Model中的不安全动作,这些不安全动作的发出者都是监管者;24 Model中不安全动作的发出者既可以是事故的直接引发者,也可以是监管者。从监管的范畴来看,HFACS中不安全监管因素的范围小于24 Model中的监管范围,只包括组织内部的监管。     

Analysis on causes of chemical industry accident from 2015 to 2020 in Chinese mainland: A complex network theory approach

Chemical manufacturing is a long-process industry, where an end product may pass through numerous dangerous and complex steps. In such long chains of coordinated activity, accidents remain common. This study made loss-prevention recommendations for the chemical industry after conducting a review of accident reports and creating a complex network model. A human factor analysis and classification system (HFACS) was used to classify data from 109 investigation reports from the Chinese mainland (2015–2020). Levels Ⅱ and Ⅲ of the HFACS output were fed into a complex network model to generate a map of causes and chains of risk. It was shown that most accidents were directly or indirectly caused by human action, and human factors played a decisive role in occurrence, evolution, and resolution. The model used was visualized in Gephi, and the key cause nodes were identified by their topological characteristics. A modularity algorithm was used to derive the community structures and segment the network map. Crucial nodes in each community were compared with factors for each class in the HFACS model. It was also found that there was a biasing factor in the causal processes of explosive accidents and poisoning and asphyxiation accidents according to the associations classified by modularity. Risk abatement strategies were proposed for the crucial factors.     

空中交通管制员的情境意识与航空安全

大部分空管不安全事件与空中交通管制员情境意识下降有关，研究管制员情境意识，预防管制员工作中情境意识下降对于预防空管不安全事件具有十分重要的意义。笔者讨论了空中交通管制员情境意识的意义、常见的管制员情境意识问题、管制员情境意识下降的原因以及提高和维持管制员情境意识的方法四个重要的管制员情境意识问题。笔者特别提出，采取积极的措施预防注意力分散、提高专业技术水平、主动管理工作负荷、管理班组资源是空中交通管制员在工作中获得和维持良好情境意识的途径。     

地铁施工阶段险兆事件自愿上报意愿影响因素研究

为提高地铁施工阶段险兆事件自愿上报水平,研究地铁施工阶段险兆事件自愿上报意愿的影响因素,通过文献查阅和访谈等方法归纳出险兆事件自愿上报意愿的影响因素,建立险兆事件自愿上报意愿指标体系。基于组合赋权法计算的权重对各指标进行重要性排序,并提出提高险兆事件自愿上报水平的对策建议。结果表明:认知水平、未对上报者和当事人保密、存在责备及苛责文化和无惩罚制度没有充分落实是地铁施工阶段险兆事件自愿上报意愿的关键影响因素。     

高处坠落事故的人因失误与干预策略研究

为系统分析导致高处坠落人因事故的产生机理,通过统计152起建筑工程高处坠落事故的调查与分析报告,从组织影响、安全监管、不安全行为前提条件和不安全行为等4个层次,辨识影响高处坠落事故的人为失误因素,修订人为因素分析与分类系统框架（HFACS）。设计高处坠落人因失误调查问卷,开展一线高处作业人员问卷调查,建立高处坠落人因失误结构方程模型,对导致高处坠落事故的人为失误因素进行路径分析。结果表明:各潜在因素间均呈正相关,且高处坠落人因失误事故的关键路径为资源管理不到位→安全监督培训不充分→班组管理不良→操作违规。综合各因素间相关性,提出了针对性的预防高处坠落事故的人因干预策略。     

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.     

基于HFACS的化工企业火灾爆炸事故人因分析*

为研究化工企业火灾爆炸事故的主要人为因素,以63起火灾爆炸事故案例为样本,构建人为因素分析与分类系统（HFACS）模型,进行火灾爆炸事故人为因素分类统计与分析,并利用卡方检验和比值比分析HFACS模型上下层级间的因果关系。结果表明:HFACS模型中上下层级人为因素间存在显著的因果关系,层级1中的“不良的组织氛围”及“组织过程漏洞”和层级2中“监督不充分”在HFACS模型中可以显著增大事故发生的可能性,且“资源管理漏洞”、“不良的组织氛围”、“组织过程漏洞”→“监督不充分”→“人员因素”→“违规”是引发事故的关键路径,并根据HFACS模型中引发事故的关键路径及其人为因素的主要表现形式,提出针对性的化工企业火灾爆炸事故预防措施。     

Revising HFACS for SMEs in the chemical industry: HFACS-CSMEs

Chemical accidents have occurred frequently in recent years, and most have occurred in small and medium-sized enterprises (SMEs). SMEs in the chemical industry face greater challenges than large enterprises with regard to accident prevention. However, SMEs have been unable to effectively learn from accidents due to the limited resources. The accident causation model is an effective tool to help the analyst learn from accidents. As a systematic accident causation model, the causes classification in the human factors analysis and classification system (HFACS) can match the characteristics of SMEs, but the cause of chemical accidents can be ineffectively identified by HFACS. In this study, HFACS was revised for the SMEs in the chemical industry, mainly consisting of three parts. First, based on the definition of factors in the original HFACS, the extended HFACS framework was obtained, which include 78 manifestations with the characteristics of the chemical accidents. Second, 101 accidents occurring in a SME in the chemical industry from 2012 to 2016 were analyzed though the extended HFACS framework. Finally, a new model, known as the HFACS-CSMEs, was obtained by further revising the manifestations and causes classification according to the statistical results of the accident analysis. HFACS-CSMEs consists of 15 cause factors and 56 manifestations, which can effectually identify and distinguish the causes in chemical accidents. Moreover, the easy-to-understand and statistically acceptable features of HFACS-CSMEs can cater to the SMEs regarding accident analysis. HFACS-CSMEs solves the problem that HFACS cannot be directly applied to chemical accidents and provides new ideas about preventing accidents in SMEs in the chemical industry.     

Fatality risk assessment and modeling of drivers responsibility for causing traffic accidents in Dubai

Introduction: This paper examines crash and safety statistics from the Emirate of Dubai in an attempt to identify factors responsible for making this population at greater risk of crashes compared to other countries. Problem: In developing countries such as the United Arab Emirates (U.A.E.), motor-vehicle-related mortalities frequently exceed those of the industrialized nations of North America and Europe. Method: Fatality and injury data used in the analysis mainly come from Dubai Emirate police reports and from other relevant international sources. Groups of the population are identified according to associated risk and exposure factors. Influence and strength of the most common risk factors are quantified using relative risk, the Lorenz curve, and the Gini index. Further analysis employed logit modeling, and possible predictors available in Dubai police reports, to estimate probability and odds ratios associated with drivers that are deemed responsible for causing traffic accidents. Results: Traffic fatality risk was found to be higher in Dubai, compared to some developed nations, and to vary considerably between different classes of road users and groups of the resident population. The likelihood of a driver causing an accident is considerably higher for those driving goods vehicles, but it is also associated with other factors. Impact: Results provide epidemiological inferences about traffic mortality and morbidity, and suggest priorities and appropriate measures for intervention, targeting resident population.     

Two discrete choice experiments on laboratory safety decisions and practices

IntroductionThe path toward enhancing laboratory safety requires a thorough understanding of the factors that influence the safety-related decision making of laboratory personnel. Method: We developed and administered a web-based survey to assess safety-related decision making of laboratory personnel of a government research organization. The survey included two brief discrete choice experiments (DCEs) that allowed for quantitative analysis of specific factors that potentially influence safety-related decisions and practices associated with two different hypothetical laboratory safety scenarios. One scenario related to reporting a laboratory spill, and the other scenario involved changing protective gloves between laboratory rooms. The survey also included several brief self-report measures of attitude, perception, and behavior related to safety practices. Results: Risk perception was the most influential factor in safety-related decision making in both scenarios. Potential negative consequences and effort associated with reporting an incident and the likelihood an incident was detected by others also affected reporting likelihood. Wearing gloves was also affected somewhat by perceived exposure risk, but not by other social or work-related factors included in the scenarios. Conclusions: The study demonstrated the promise of DCEs in quantifying the relative impact of several factors on safety-related choices of laboratory workers in two hypothetical but realistic scenarios. Participants were faced with hypothetical choice scenarios with realistic features instead of traditional scaling techniques that ask about attitudes and perceptions. The methods are suitable for addressing many occupational safety concerns in which workers face tradeoffs in their safety-related decisions and behavior. Practical Application: Safety-related decisions regarding laboratory practices such as incident reporting and use of PPE were influenced primarily by workers’ perceptions of risk of exposure and severity of risks to health and safety. This finding suggests the importance of providing laboratory workers with adequate and effective education and training on the hazards and risks associated with their work. DCEs are a promising research method for better understanding the relative influences of various personal, social, and organizational factors that shape laboratory safety decisions and practices. The information gained from DCEs may lead to more targeted training materials and interventions.     

Analysis of train derailments and collisions to identify leading causes of loss incidents in rail transport of dangerous goods in Canada

The Canadian railway industry has improved safety performance in the last decade as measured by freight loss incidents per billion gross ton-miles. Further improvements in safety performance require a deeper analysis of the leading causes to identify weaknesses in implementing safety systems. In this paper, we classify the causes of railway loss incidents using a Safety Management System (SMS) framework to identify system weaknesses. The role of human factors is further analyzed through the Human Factors Analysis and Classification System (HFACS) approach. For this, we utilized data from 42 main track derailments and collisions involving the transport of dangerous goods in Canada between 2007 and 2018, which have been investigated by the Transportation Safety Board of Canada in detail. Associations between adjacent sub-categories of the HFACS framework are analyzed to identify any interdependency that exists between active and latent errors using a Chi-square test and Kruskal's lambda analysis. Furthermore, we implement the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and the Analytical Network Process (ANP) to identify causal relationships between different sub-categories of the HFACS framework and calculate the weighted influence of each sub-category on main track derailments and collisions. Finally, a comparison is made between this work and others', which have analyzed human factors in the railway industry. There is good agreement between the results of these studies that highlight the importance of supervisory and organizational factors in the prevention of railway loss incidents. Based on these findings, we make recommendations to reduce railway loss incidents.     

HFACS与24 Model:不安全动作原因的对比研究

不安全动作的定义、分类与识别尚未有统一标准,这给事故分析与预防带来许多困难。基于此,对人因分析与分类系统(HFACS)和24 Model中的不安全动作层面进行对比研究。在不安全动作对应方面:HFACS中的不安全动作均能对应到24 Model中的不安全动作,但HFACS没有涉及24 Model中不违章、未引起事故但高风险的不安全动作,因而无法实现事前预防。在间接原因对应方面,HFACS中不安全动作的产生原因均可与24 Model中的1个或多个间接原因相对应;HFACS中不安全动作的产生涉及较多生理、心理层面的原因,而考虑到实用性原则,24 Model暂未给出该两方面原因的具体分类方法。在不安全动作发出者方面:24 Model涉及组织内各层级人员,而HFACS仅指一线员工。在实际应用方面:两种模型均可用于不安全动作原因的统计分析;24 Model给出的不安全动作分类范围较HFACS广,在事故分析时所受局限性小,但易造成不安全动作遗漏;在用24 Model进行不安全动作分析时,应尽量细化动作分类及分析的深入程度,以及动作发出者的员工层级。     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

1990～2003年中国民航152起小于间隔飞行事件的分类统计研究

小于间隔飞行事件是双机空中相撞事故的潜在威胁 ,对此类事件进行人的因素的分类统计分析有助于寻找错误原因和规律。笔者以 15 2起小于间隔飞行事件为数据源 ,根据Reason模型和HFACS系统进行分类 ,统计了各个因素所占的百分比和部分因素的相关程度。结果表明 ,小于间隔事件与生物节律的关系并不明显而与交通流量密切相关 ;机组不安全行为与管制员未能有效实施监控职能密切相关 ;决策错误、操作错误和知觉错误的主要表现形式分别为 :混淆指令、调错高度窗、听错指令和记错 /忘记管制员指令以及未理解管制员指令 ;不良的机组资源管理是导致小于间隔最主要的机组因素 ,主要表现形式为机组成员之间的配合不好、未有效实施交互监视和交互检查 ;“高度指令”和“相对活动高度”提示的语序问题是一个值得进一步研究的课题。     

基于Fuzzy-ANP的空管安全风险评估研究

空中交通管制对于保障飞行安全至关重要,如何进行空管安全风险评估是亟待研究的问题。在分析三角模糊数学和ANP原理的基础上,针对空管安全风险因素相互影响的特点,建立基于Fuzzy-ANP的空管安全风险评估模型。以某空管单位为例进行实证分析,通过计算评价指标权重,找到影响空管安全风险的关键指标。结果表明,影响该空管单位安全的主要因素为管理标准效能、员工培训次数和人员语言及交流沟通状况等,该空管单位应重点改善这3个因素并兼顾其他影响因素。研究表明,Fuzzy-ANP分析法有助于确定安全风险评估的重点。     

The prevention of occupational injuries in two industrial plants using an incident reporting scheme

INTRODUCTION: The aim of this study was to examine whether the introduction of an incident reporting scheme with feedback in two industrial plants had an effect on the number of major incidents. METHOD: An intervention design with measurements before the implementation of the incident reporting scheme and two years later was used to examine the relationship between incident rates, safety climate, the willingness to report incidents and perceived management commitment to safety. RESULTS: The results showed that a successful implementation of an incident reporting scheme was followed by a decline in the incidence of major incidents at a Danish metal plant. A key factor in implementing the scheme was top management commitment, which was lacking at another plant, where the implementation of a similar scheme failed. CONCLUSION: Although the study shows some encouraging results concerning the use of incident reporting schemes to prevent occupational accidents, the possibility to draw causal conclusions is limited in the present study, and further studies are needed before the effectiveness of such schemes can be evaluated with certainty.     

Crash data analysis: collective vs. individual crash level approach

INTRODUCTION: Traffic safety literature has traditionally focused on identification of location profiles where "more crashes are likely to occur" over a period of time. The analysis involves estimation of crash frequency and/or rate (i.e., frequency normalized based on some measure of exposure) with geometric design features (e.g., number of lanes) and traffic characteristics (e.g., Average Annual Daily Traffic [AADT]) of the roadway location. In the recent past, a new category of traffic safety studies has emerged, which attempts to identify locations where a "crash is more likely to occur." The distinction between the two groups of studies is that the latter group of locations would change based on the varying traffic patterns over the course of the day or even within the hour. METHOD: Hence, instead of estimation of crash frequency over a period of time, the objective becomes real-time estimation of crash likelihood. The estimation of real-time crash likelihood has a traffic management component as well. It is a proactive extension to the traditional approach of incident detection, which involves analysis of traffic data recorded immediately after the incident. The units of analysis used in these studies are individual crashes rather than counts of crashes. RESULTS: In this paper, crash data analysis based on the two approaches, collective and at individual crash level, is discussed along with the advantages and shortcomings of the two approaches.