基于文本挖掘的管制运行风险主题分析*

为充分挖掘管制运行风险信息和隐藏规律，实现数据驱动的风险管理。以某管制单位2004—2019年共269条管制原因不安全事件数据为挖掘语料，在考虑上下文语义的基础上，运用潜在狄利克雷分配（LDA）主题模型挖掘管制运行风险主题及关键词，使用Word2Vec挖掘主题之间、关键词之间的关联关系，运用社会网络分析软件UCINET、可视化工具NETDRAW构建语义网络将关联关系进行可视化并进行网络分析。结果表明:LDA主题模型可以通过运行数据实现对管制运行风险的高效提取和深层挖掘，挖掘到管制人为因素、特情处置、地空配合、班组资源管理、组织管理、运行环境、管制指挥共7个主题，其中管制人为因素主题是核心主题，与其他主题都具有较强的相互关联；Word2Vec和语义网络相结合能够更准确地挖掘风险之间的关系，确定主题的重要度排序，识别关键风险。     

基于贝叶斯网络的尾流事故机理分析

针对事故树分析法的局限性,在尾流事故树的基础上,建立贝叶斯网络(BN)。运用推理运算对BN进行定量分析,得出:空中交通密度太大、空中交通管制(ATC)间隔判断错误和短期冲突告警(STCA)被忽略是事故的关键致因。将针对致因提出的改进措施引入到BN中,评价相关措施的有效性。应用BN进行尾流事故的机理分析,能够以比逻辑门更好的形式表达变量间的不确定性关系,从而更加方便地找到导致事故发生的关键因素。     

空管安全风险评估指标体系的优化设计

结合国际民航组织提出的安全管理体系实施的相关要求及我国《民航空管安全管理体系指导手册(试行)》的指导思想,根据安全管理一线工作人员及中高层管理者的访谈记录,收集整理出影响空管安全的风险因素,并按照人员、设备、环境、管理4个方面进行分类、提炼、合并和综合,设计了风险评估指标体系并进行优化。该体系把安全科学和系统论相结合,把错综复杂的安全风险因素细化综合、提炼出脉络分明的监控对象,有助于空管系统进行风险识别和综合预警,查找安全隐患和分析不安全事件或事故征候的原因,为民航安全风险信息化管理提供基础数据和决策依据。     

Errors of perception in air traffic control

Accurate and timely perception of visual and auditory information by air traffic controllers is critical to aviation safety. The aim of this research was to investigate the types of errors of perception that occur in air traffic control (ATC). The data were gathered from interviews with 28 UK area controllers and a review of 48 area and terminal control incidents involving loss of separation in the UK spanning three years. The data gave rise to a set of classifications, which form part of the technique for the retrospective analysis of human error (TRACEr). The results are discussed in terms of theory and empirical research. Key main implications for future automation are outlined, in terms of display design, automation reliability and the operation of multiple tools designed and developed separately.     

Systemic accident risk assessment in air traffic by Monte Carlo simulation

A systemic accident model considers accidents as emergent phenomena from variability and interactions in a complex system. Air traffic risk assessments have predominantly been done by sequential and epidemiological accident models. In this paper we demonstrate that Monte Carlo simulation of safety relevant air traffic scenarios is a viable approach for systemic accident assessment. The Monte Carlo simulations are based on dynamic multi-agent models, which represent the distributed and dynamic interactions of various human operators and technical systems in a safety relevant scenario. The approach is illustrated for a particular runway incursion scenario, which addresses an aircraft taxiing towards the crossing of an active runway while its crew has inappropriate situation awareness. An assessment of the risk of a collision between the aircraft taxiing with an aircraft taking-off is presented, which is based on dedicated Monte Carlo simulations in combination with a validation approach of the simulation results. The assessment particularly focuses on the effectiveness of a runway incursion alert system that warns an air traffic controller, in reducing the safety risk for good and reduced visibility conditions.     

空中交通管制团队人误的分类及致因研究

复杂社会技术系统通常以团队方式运行,认识与预防团队人误是控制安全风险的重要途径。研究采用威胁与差错管理(TEM)的研究框架,以某空管运行自愿报告系统2004年全年数据为样本源,分析团队人误的流程与致因,统计发现团队人误占总体空管人误的31%。团队发现和指出错误后仍不能有效纠正错误的比例达到8%。根据样本统计并结合典型团队人误的案例,确认了空管团队人误的主要类型和分布情况以及团队行为促成因素(PSF)的框架及重要性排序,其中缺乏交流是团队人误的最常见PSF,报告样本量出现率达47.1%。最后,从组织文化建设、团队管理、标准操作程序、培训等方面提出了减少团队人误的建议。     

An algorithm for assessing the risk of traffic accident

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.     

基于CREAM和贝叶斯网络的空管人误概率预测方法

针对研究管制人因可靠性时存在的模糊性和片面性问题，采用认知可靠性与失误分析方法（CREAM）中的扩展预测法，计算10项管制通用任务的人误概率；在此基础上，以管制行为形成因子作为根节点构建贝叶斯网络，建立其与情景控制模式的不确定关系模型，对管制员在多任务中的人误概率进行预测。研究结果表明:在由相同评判者给出行为形成因子影响效应的前提下，由CREAM扩展预测法和构建贝叶斯网络的方法预测得到的多数任务的人误概率差异较大，从方法的客观性、合理性和适用性角度分析，贝叶斯网络在研究该问题时更具优势。     

Work domain analysis for air traffic controller weather displays

INTRODUCTION: Adverse weather conditions have a major impact on National Airspace System (NAS) operations. They create safety hazards for pilots, constrain the usable airspace for air traffic control (ATC), and reduce the overall capacity of the NAS. A system-wide dissemination of weather information to controllers could theoretically improve safety and efficiency. PROBLEM: However, it is currently unclear what weather information would be beneficial for tactical operations. Furthermore, no previous research has empirically evaluated optimal presentation designs for ATC weather displays. Ill-designed weather displays can cause safety hazards by presenting redundant information (i.e., by increasing the cognitive load) and display clutter (e.g., by interfering with the visual extraction of traffic data). METHOD: In the present paper, we outline our use of cognitive work analysis (CWA) techniques for the assessment of weather information needs for terminal controllers. RESULTS: Specifically, we describe how the CWA modeling tools helped us reveal instances in the terminal domain where weather information is lacking or insufficiently disseminated. We used our CWA results to drive the development of weather display concepts and to set up a high-fidelity simulation capability. IMPACT ON INDUSTRY: By means of high-fidelity simulations, we can empirically evaluate controller weather information needs in order to propose weather displays for increased aircraft safety and efficiency of terminal operations.     

The development of a more risk-sensitive and flexible airport safety area strategy: Part II. Accident location analysis and airport risk assessment case studies

This two-part paper presents the development of an improved airport risk assessment methodology aimed at assessing risks related to aircraft accidents at and in the vicinity of airports and managing Airport Safety Areas (ASAs) as a risk mitigation measure. The improved methodology is more quantitative, risk-sensitive, flexible and transparent than standard risk assessment approaches. As such, it contributes to the implementation of Safety Management Systems at airports, as stipulated by the International Civil Aviation Organisation.The second part of the paper presents the analysis of accident locations, including the plotting of Complementary Cumulative Probability Distributions for the relevant accident types. These were then used in conjunction with the improved accident frequency models to produce Complementary Cumulative Frequency Distributions that could be used to assess risks related to specific runways and determine Airport Safety Area (ASA) dimensions necessary to meet a quantitative target level of safety. The approach not only takes into account risk factors previously ignored by standard risk assessments but also considers the operational and traffic characteristics of the runway concerned. The use of the improved risk assessment technique and risk management strategy using ASAs was also demonstrated in two case studies based on New York LaGuardia Airport and Boca Raton Airport in Florida.     

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

IntroductionThe effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece.MethodRandom Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively.ResultsRegarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity.ConclusionsThe study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms.Practical applicationThe identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.     

基于Hankel-DMD的城市交通事故风险时空预测*

为解决城市交通事故风险时空分布预测任务中时空关联性捕捉困难的问题,提出基于动态模态分解（DMD）的城市交通事故分析时空预测模型,模型利用总最小二乘法去除交通事故数据中的噪声,应用结合Hankel矩阵的动态模态分解模型（Hankel-DMD）捕捉交通事故风险的时空关联性,对交通事故风险的时空分布进行预测。研究结果表明:DMD框架能够为高维预测任务提供低秩解决方案,从高维数据中捕捉时空关联性;Hankel-DMD模型在预测评价指标平均绝对误差和均方根误差方面的表现明显优于统计学及机器学习等方法;Hankel-DMD模型产生的动态模态和特征值,对事故风险系统的时空动态特征具有一定的可解释性,同时验证Hankel-DMD模型的适用性。     

Relationship between traffic fatalities and drunk driving in Japan

OBJECTIVE: The present study was performed to clarify the relation between alcohol use and traffic fatalities in accidents involving motor vehicles in Japan. METHODS: Data on traffic accidents were collected from Fukuoka Prefectural Police records of traffic accidents which occurred in that prefecture between 1987 and 1996. Multiple logistic regression models were used to assess the effect of alcohol use on the risk of traffic-accident death. RESULTS: The data showed that 58,421 male drivers were involved in traffic accidents during the 10-year study period, and that 271 of these were killed as a result of the accident. Alcohol use was significantly associated with speed, seat belt use, time, and road form. Among male motorcar drivers, the odds ratio of alcohol use before driving, after adjusting for age, calendar year, time, and road form, was 4.08 (95% confidence interval, 3.08-5.40), which means that about 75% of fatalities (attributable risk percent among exposed) might have been prevented if drivers had not drunk before driving. CONCLUSIONS: Alcohol use before driving resulted in a 4.08-fold increase in the risk of death in a traffic accident. It is suggested that alcohol use is considered an important risk factor for fatality in traffic accidents.     

山区二级公路运营期常态交通风险评价

为客观准确地评价山区二级公路常态交通风险,将风险源分为动态风险源和静态风险源.首先,基于白化权函数的综合评价法建立事故后果模型,基于历史事故数据建立事故概率模型,以评价静态风险;其次,结合白化权函数和层次分析法(AHP)建立事故后果模型,采用有序Logit模型建立事故概率模型,以评价动态风险;最后综合动静态风险结果评价...     

空中交通管制中人的可靠性模糊综合评价研究

人是空中交通管制系统中最灵活、最具适应性和最有价值的因素 ,而其行为也是最易受到不利影响的。由于空中交通管制中产生的人为失误 ,往往会导致航空器空中危险接近 ,严重的后果甚至会酿成空难。笔者从人 -机 -环境系统工程的观点出发 ,提出了空中交通管制中人的可靠性评价的指标体系结构模型。从人自身因素、软件、硬件、环境等方面指出了影响空中交通管制中人的可靠性的心理、空中环境等 17个子因素。运用模糊数学的方法 ,建立空中交通管制中人的可靠性定量评价模型 ,并用实例进行了验证。研究表明 ,该方法应用于空中交通管制中人的可靠性评价是一种新的尝试 ,其评价结果可为各级领导机构提供航空安全管理的决策依据。     

高速公路隧道群交通事故风险致因分析

通过对高速公路隧道群特殊地理环境所造成的交通事故后果风险与救援时间的相关分析,提出隧道群交通事故风险致因模型,该模型将隧道群交通事故风险划分3个风险阶段,即初始事故风险、事故发展风险、最终事故风险,不同阶段的风险值受人的因素、车辆因素、隧道群环境因素和防灾救援能力大小的影响而改变。通过对隧道群的风险因素分析,认为隧道群在两毗邻隧道间将可能产生烟雾风险,驾驶人员视觉快速转换的照明风险,以及交通事故防灾控制风险;同时统计的隧道群防灾救援时间概率分布表明,救援队伍能在一定时间内快速到达事故现场,并对较晚到达事故现场救援情况,基于风险分析而提出相应的防范对策和措施。     

Application of generalized link functions in developing accident prediction models

The Bayesian Poisson–Gamma hierarchy, leading to the negative binomial distribution, has been the standard practice in developing accident prediction models. To linearize the relationship connecting the mean of the negative binomial distribution to relevant covariates, a canonical log link has traditionally been used. Typically, little information is available regarding the choice of a particular link. To avoid link misspecification, it is proposed to nest the canonical log link model within a generalized link family and subsequently use the full Bayes method for parameter estimation, performance evaluation and inference. The proposed approach was applied to a sample of accident and traffic volume data corresponding to 99 intersections in the city of Edmonton, Alberta. The results showed that both the generalized link model and the traditional canonical link model provided adequate fit to the data. However, the Bayes factor provided a clear statistical support for the use of the generalized link approach. A procedure for link validation is also described. It allows the users (e.g., road authorities) to consider the changes in predicted accidents that will result if a generalized link is used instead of a canonical link. If a certain maximal change is tolerated, the canonical link can be used to analyze the data; otherwise the generalized link is worth the extra efforts and should be adopted. When compared with the traditional approach, the generalized link model was found to predict a lower number of accidents whenever there is a heavy traffic at the major approach, especially if combined with light flow on the minor approach. The paper concludes by identifying out areas for further research.     

Accident models and organisational factors in air transport: The need for multi-method models

Event analysis is needed to learn and improve safety. In air transport, ‘occurrences’ are routinely reported by pilots and air traffic controllers, and in-flight data analysis systems automatically monitor aircraft system behaviour and capture parameter threshold exceedances. The safety analyst of a large airline has to analyse dozens of occurrences each day. To understand why events happened the analyst has to go beyond the given information and make causal inferences. The analyst is able to do this for causal factors closely related in time and space to the event itself by applying individual knowledge and expertise. But typically the result of the analysis is ad hoc reaction to each individual event. Systematic analysis is needed to find areas of improvement for factors that are further removed from the event (latent factors). New tools are needed to help the analyst in this respect. There is a need for models that represent possible causal event sequence scenarios that include technical, human, and organisational factors. Building such models is a huge task, and requires the combination of detailed knowledge of all aspects of the system, processing huge amounts of data, a substantial mathematical background and the ability to capture this all in a user friendly software tool to be used by the safety analysts. Experience in Causal Modelling of Air Transportation System (CATS) in the Netherlands and similar projects in FAA and Eurocontrol in aviation shows that this is indeed a formidable task, but it has to be done to further improve safety.     

Using a driving simulator to identify older drivers at inflated risk of motor vehicle crashes

PROBLEM: To develop appropriate assessment criteria to measure the performance of older drivers using an interactive PC-based driving simulator, and to determine which measures were associated with the occurrence of motor-vehicle crash. METHOD: One hundred and twenty-nine older drivers residing in a metropolitan city volunteered to participate in this retrospective cohort study. Using the driving simulator, appropriate driving tasks were devised to test the older drivers, whose performances were assessed by 10 reliable assessment criteria. Logistic regression analysis was then undertaken to determine those criteria that influence the self-reported crash outcome. RESULTS: As expected, driving skill of older drivers was found to decline with age. Over 60% of the sample participants reported having at least one motor-vehicle crash during the past year. Adjusting for age in a logistic regression analysis, the cognitive abilities associated with the crash occurrence were working memory, decision making under pressure of time, and confidence in driving at high speed. SUMMARY: The findings of this retrospective study indicated those individuals at inflated risk of vehicle crashes could be identified using the PC-based interactive driving simulator. Prospective studies need to be undertaken to determine whether the driving simulator can predict future crash events. IMPACT ON INDUSTRY: This study demonstrated an economical driving simulator approach to screen out problematic or unsafe older drivers before a more detailed but expensive road test is considered.