典型认知模型及其在人因可靠性分析中的应用评述

评述了典型认知模型及其在人因可靠性分析中的应用.首先,强调了在人因可靠性分析中使用认知模型的必要性; 然后,介绍了认知心理学、行为科学等学科所建立的几种典型认知模型,包括信息处理模型、决策过程的阶梯模型和通用认知模型,分别分析了它们的基本结构和功能,讨论了各自的优缺点; 随后,介绍了几种人因可靠性分析方法中所建立或使用的认知模型,包括HCR方法使用的SRK框架、ATHEANA方法使用的信息处理模型、CREAM方法建立的COCOM模型以及IDAC方法构建的IDA模型,分别分析了这些模型的特点; 最后,提出了建立认知模型所要遵循的基本原则,并展望了认知模型的未来发展方向.     

道路交通事故发展趋势分析与预测

根据福建省2000 -2010年交通事故相关指标,采用统计图表分析法进行交通事故发展趋势分析与安全水平比较研究,结果表明交通事故各项绝对指标总体呈下降趋势,但从万车死亡率、受伤人数与死亡人数比及交通事故死亡人数占各类事故死亡人数比重等相对指标看,交通安全总体水平偏低,交通事故后果比较严重.对交通事故死亡人数与GDP、机动车保有量、公路通车里程、人口数四项影响因素进行了多元线性回归分析,分析得出四个影响因素总体对交通事故死亡人数的线性影响是显著的,采取向后筛选策略线性回归分析得出,死亡人数与GDP的线性关系是显著的,根据回归结果建立了交通事故的预测模型.     

Data mining of tree-based models to analyze freeway accident frequency

INTRODUCTION: Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. METHOD: This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. RESULTS: The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. IMPACT ON INDUSTRY: By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.     

The indirect involvement of buses in traffic accident processes

The involvement of buses in accidents usually is assessed implicitly on the basis of the direct involvement of the bus in the collision or in injury production. This paper deals with the scope and forms of indirect involvement of buses (as a sight obstruction, for example). Accidents were selected by identifying the presence of the term ‘bus’ or synonyms in the text parts of complete police reports (testimonies, statements by the persons involved, etc.) available in electronic form, then analysed in detail. Direct or indirect involvement of a bus is found in 3.6% of traffic injury accidents reported by the police in the community studied (direct involvement: 1.4%; indirect involvement: 2.2%). The different forms of indirect involvement are then described, and some possibilities of preventive measures are discussed.     

Road traffic accident severity analysis: A census-based study in China

Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.     

医疗器械中人的可靠性分析

医疗器械产品是一类直接关系到人生命健康的特殊产品.长期以来,医疗器械产品的使用安全性一直是亟待解决的重大问题.在医疗器械产品设计中进行人的可靠性分析能够进一步提高医疗质量,防止医疗事故.但是,这方面的工作目前还处于起步阶段.本文主要从医疗器械人的可靠性方面人手,定性及定量的分析人在使用器械时的可靠性问题.用SHEL模型来定性的分析医疗器械中人的可靠性的影响因素;用故障树方法及概率树对具体的多功能医用手术床进行定量分析;用第二代可靠性分析方法-CREAM解决定量分析的主观性问题;最后提出建立医疗器械人的可靠性数据库.以此为医疗器械产品设计提供了人的可靠性分析的方法.     

基于最优尺度Logit模型的交通事故形态致因分析

为剖析典型交通事故形态致因,在利用最优尺度分析法诊断交通事故形态致因共线性的基础上,筛选关键影响因素,构建无序多分类Logit交通事故致因模型并对模型进行参数标定,并选择100个交通事故样本对模型进行精度验证,该模型相对误差仅为4.0%,能够较为准确地分析典型交通事故形态致因。研究结果表明:路侧及中央隔离设施、照明是事故的保护性因子,可有效降低事故率;路侧行道树设置不合理或未及时修剪,使车辆不能有效识别道路信息,是正面碰撞的重要影响因素;机非混合道是事故的危险因子,增大了侧面碰撞事故发生概率。研究结果可为交通隐患精准识别、交通事故主动预防以及交通设施科学设计提供理论依据和技术支撑。     

公路交通事故危险性与事故原因的灰色关联分析

交通安全既可以采用交通事故次数来评价，也可以根据交通事故的严重度进行衡量。不同的交通事故诱因，其事故后果的危险程度也不一致。运用灰色系统理论，以某段道路上交通事故多发点的“地点危险指数”为参考数列，以这些地点的不同事故诱因为比较数列，对“事故危险指数”和引发交通事故的各类事故原因之间作灰色关联分析，可以找出该路段上危害性最大的一些交通事故成因，在交通管理和交通安全宣传教育中有针对性地进行防治。本文以107国道1716—1883公里的交通事故数据为例给出了算例。     

What-You-Look-For-Is-What-You-Find – The consequences of underlying accident models in eight accident investigation manuals

Accident investigation manuals are influential documents on various levels in a safety management system, and it is therefore important to appraise them in the light of what we currently know – or assume – about the nature of accidents. Investigation manuals necessarily embody or represent an accident model, i.e., a set of assumptions about how accidents happen and what the important factors are. In this paper we examine three aspects of accident investigation as described in a number of investigation manuals. Firstly, we focus on accident models and in particular the assumptions about how different factors interact to cause – or prevent – accidents, i.e., the accident “mechanisms”. Secondly, we focus on the scope in the sense of the factors (or factor domains) that are considered in the models – for instance (hu)man, technology, and organization (MTO). Thirdly, we focus on the system of investigation or the activities that together constitute an accident investigation project/process. We found that the manuals all used complex linear models. The factors considered were in general (hu)man, technology, organization, and information. The causes found during an investigation reflect the assumptions of the accident model, following the ‘What-You-Look-For-Is-What-You-Find’ or WYLFIWYF principle. The identified causes typically became specific problems to be fixed during an implementation of solutions. This follows what can be called ‘What-You-Find-Is-What-You-Fix’ or WYFIWYF principle.     

Transferability of accident prediction models

Accident prediction models, the vast majority of which are negative binomial regression models, are of considerable importance to highway agencies since they can be used to conduct many traffic safety studies. However, not every agency possesses sufficient accident statistics that enable it to develop reliable models of its own. This problem gives rise to interest in the transferability of accident prediction models in time and space. It would save time, effort, and money if accident prediction models developed for one region in one period of time could be applied in different time periods and regions to produce reliable safety studies.This paper presents methods for recalibrating negative binomial accident models before transferring them for use in different time periods and regions of space. The paper emphasizes that the recalibration of the shape parameter of a transferred model using local data is absolutely necessary. It explains that it is also desirable to recalibrate the constant term of the transferred model in order to allow the model to better suit local conditions. A moment method is presented for recalibrating the shape parameter of a transferred model when its constant term is not recalibrated. However, a maximum likelihood method is presented for recalibrating both the shape parameter and the constant term of the transferred model and is shown to be superior to the recalibration methods existing in the traffic safety literature.     

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey

Introduction

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey.

Method

Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P < 0.05 with spatial autocorrelation analyses.

Results

Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P < 0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R² values. Geographically weighted regression provided values of 0.89-0.99 adjusted R² for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions.

Impact on industry

Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.     

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.     

CREAM追溯法在交通事故人因分析中的应用研究

人因失效是道路交通事故发生的主要原因.认知可靠性和失误分析方法(CREAM)可以追溯事故发生的根原因,并对事故隐患进行预测.它强调情景环境对人的行为的重要影响,较符合驾驶员可靠性分析的需求.研究了CREAM中的追溯分析方法在道路交通人因失效事件根原因分析中的应用,建立了道路交通事故的人因失效模式,对人因失效基本原因的分类和后果-前因关系进行归纳、整理和补充,提出了适用于道路交通人因失效事件的“后果-前因”追溯表和具体的追溯分析步骤.进而对其定量计算进行了探索,并通过实例探讨了其应用,得到事故发生的根原因.     

基于SCPN的交通应急处置流程建模及性能分析

为了更好地利用信息技术支持应急管理,提高交通应急指挥系统救援效率,以路面塌陷事故为例,利用随机着色Petri网(SCPN)对其应急响应流程进行动态离散建模,根据同构的马尔可夫链(MC)分析模型的有效性,最后利用马尔可夫链及模糊数学的相关理论对交通应急指挥系统进行性能分析。结果表明:救援信息、交通路网动态信息、应急资源信息及时态控制信息反馈环节容易产生信息堆积,应提高综合监控的技术水平;专家决策制定出警预案、善后处理耗时较长,应作为流程优化的重点。     

基于改进SIFT算法的交通事故图像匹配

为解决无人机航拍交通事故现场图像特征点数量较少、匹配成功率较低、耗时过长的问题,提出1种改进的SIFT算法,使用Gabor滤波对图像进行特征提取,基于改进的高斯金字塔和多方向多尺度Gabor频谱特点提取出具有尺度、旋转不变性的特征点,结合LLE算法对特征描述符进行降维处理,通过DBSCAN算法对特征点进行密度聚类,计算...     

基于APRIORI-TAN的交通事故伤害分析与预测*

为探究道路交通事故因素和事故伤害的相关性,以2 467起涉及人员伤亡的交通事故为数据集,运用Apriori算法分别挖掘事故伤害关联规则,并结合社会网络分析的可视化和核心-边缘分析构建受伤事故和死亡事故的关联规则网络。结果表明:事故伤害程度与事故时间、道路条件和交通环境等因素关系紧密,尤其死亡事故与碰撞固定物、人行横道事故、高速公路、高速道路、非市区、酒驾和超速存在高相关性。基于树型贝叶斯网络（TAN）构建事故伤害程度的预测模型,预测结果准确率可达87.56%。     

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。     

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.     

海底隧道交通事故风险耦合演化机理研究

为明晰海底隧道交通系统内部风险因素之间的耦合关联和事故演化机理,从驾驶人、道路、车辆、环境和管理5方面阐释海底隧道风险因素内涵,定性分析风险因素之间的耦合作用,建立海底隧道交通事故风险演化立体网络模型,提出海底隧道风险因素量化评价标准;并构建海底隧道交通事故风险耦合尖点突变模型,深入分析海底隧道交通系统风险状态演化的主要形式;结合应用实例,对海底隧道风险因素耦合进行计算评价。结果表明：多因素耦合对海底隧道交通系统产生的风险影响大于单因素耦合,驾驶人因素风险相对最大,车辆因素风险相对最小,并从人、物2个因素方面提出针对性的事故风险预防措施。研究结果可为海底隧道交通安全管控提供理论参考和借鉴。