车人碰撞事故再现技术研究进展

车人碰撞事故再现已成为国内外研究热点,提高结果可信性为事故再现的核心。根据事故再现所需痕迹将再现方法分为6类,即"行人抛距"、"车辆制动距离"、"行人损伤"、"车辆变形"、"其他"以及"仿真"。通过分析各类方法的优缺点,提出可综合利用这些方法获得客观、可信的事故再现结果。然后探讨提高车人碰撞事故再现结果可信性的新发展方向:开发国产、高精度的事故再现软件,研究事故再现结果的不确定性问题,以及研究痕迹间关系在事故再现中的应用。而其中仿真所得事故再现结果的不确定性问题、车人碰撞事故再现区间不确定优化方法以及事故现场痕迹间关系为值得期待的新研究内容。     

化工园区泄漏事故安全风险评估模式研究

为减少化工事故频发造成严重人员伤亡及财产损失,在分析危化品泄漏事故类型的基础上,从事故发生概率和事故后果两方面提出危化品泄漏事故风险评估模型.以中毒事故为例,对重庆长寿化工园区内某企业一液氨储罐进行风险评估.基于概率模型计算中毒事故概率,结果表明该储罐发生泄漏引起中毒的概率较小;数值模拟结果显示:影响范围随时间的增加而...     

基于模糊均生函数和最优子集回归的飞行事故率预测

为有效预防飞行事故的发生,针对飞行事故率具有随机波动性和趋势性的特点,采用模糊均生函数(FMGF)和最优子集回归(OSR)建立飞行事故率预测模型。该方法把FMGF延拓序列作为预测因子加入OSR方程,将FMGF分析和因子筛选相结合作OSR,进而对飞行事故率进行预测。通过对美国空军1988—2004年的飞行事故率进行拟合预测,结果表明:将FMGF模型和OSR模型有机结合,能够有效刻画飞行事故率的随机波动特性,并且其预测结果的相对误差也较小。     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

On the development of a new hybrid risk assessment process using occupational accidents’ data: Application on the Greek Public Electric Power Provider

The occupational accidents have a major impact upon human integrity and also bring about high costs for the social health and insurance system of a country. In addition, risk analysis is an essential process for the safety policy of a company, having as main aim the effacement of any potential of damage in a productive procedure, while the quantified risk evaluation is the most crucial part of the whole procedure of assessing hazards in the work. The main goal of this study is double: a) the development and presentation of a new hybrid risk assessment process (HRAP) and b) the application of HRAP in the Greek Public Power Corporation (PPC) (the unique electric power provider and the largest industry in Greece), by using occupational accidents that have been recorded, during the 12-year period of 1993-2004. The new process consists of four distinct phases a) the hazard sources’ identification phase, b) the risk consideration phase, c) the risk-evaluation phase, and d) the phase of the risk assessment and safety-related decision making. The results show that in some cases the risk value has been calculated in PPC to be higher than 500 (in the risk rating of 0-1000), which imposes the taking of suppressive measures for abolishing the danger source, while the fatal accident frequency rate (per 10⁸ man-hrs) is FAFR ≅ 2.4.     

基于GSK-AdaBoost-LightGBM的交通事故死亡人数预测研究

交通事故预测研究有助于降低交通事故发生的概率。为提高交通事故预测模型的精度,首先应用网格搜索法获得LightGBM模型的最优超参数,并进行5折交叉验证提升模型的抗拟合能力,建立优化的LightGBM(GSK-LightGBM)模型;然后基于AdaBoost算法训练多个GSK-LightGBM模型,加权组合得到AdaBoost-LightGBM增强集成模型,并采用网格搜索法结合5折交叉验证,实现了AdaBoost-LightGBM模型的参数优化,构建了GSK-AdaBoost-LightGBM模型;最后基于采集到的1953—2018年我国道路交通事故死亡人数相关样本数据训练模型,得到基于GSK-AdaBoost-LightGBM的交通事故死亡人数预测模型,并引入均方误差、平均绝对误差和平均绝对百分误差3项评估指标评估了模型的预测性能,探究了模型的优化效果。结果表明:GSK-AdaBoost-LightGBM模型的3项评估指标值分别为0.014、0.00035和0.077,低于LightGBM模型、GSK-LightGBM模型和AdaBoost-LightGBM模型的评估指标值,说明该模型的预测精度较高,且明显优于LightGBM模型、GSK-LightGBM模型和AdaBoost-LightGBM模型。     

危化品事故情景推演网络构建及关键节点分析*

为有效应对危化品事故发生与演变时存在的动态性及不确定性问题,基于事故演变过程中的关键情景状态及对应孕灾环境、应急活动和处置目标等要素,引入动态贝叶斯网络方法,构建危化品事故情景推演网络,并结合复杂网络知识,对孕灾环境和应急活动2类事故影响因素节点进行敏感性和重要度分析。结果表明:情景推演网络计算的情景节点概率符合事故发生的实际情况,能够对危化品事故在不同孕灾环境和应急救援下的演变路径进行推演,并且分析得到燃烧物、消防力量、火场环境和建筑密度等关键影响因素节点,研究结果可为事故处理提供应急辅助决策支持。     

基于HFACS-BN模型的危险化学品事故人因路径分析*

为提高危险化学品安全管理水平,收集我国134起以人为主因的危险化学品事故,构建危险化学品事故的HFACS-BN模型,并基于贝叶斯网络对危险化学品事故中的人因路径及各因素灵敏度进行分析。研究结果表明:行为违规是导致事故的最主要不安全行为;在复杂路径中,环境因素和操作者状态具有较高的灵敏度,与其相关的风险认知与处理不当是阻碍事故预防的关键因素;在非复杂路径中,操作者状态与运行计划不当具有较高的灵敏度,其所涉及的监督违规、组织过程和组织氛围,是导致危险化学品事故的根本起点,并可依此分析事故直接原因和间接原因;操作者状态在2种路径中均表现出高灵敏度,因此化工企业在人员管理上要加强关注职工状态,减少不安全行为的出现。     

安技人员素质与工伤事故关系问题的探讨

以中国飞行试验研究院安全技术人员配置状况为研究对象 ,结合近 2 0年来工伤事故情况 ,从安技人员文化基础、专业知识、工作年龄、安全年限等方面 ,研究分析安技人员素质与工伤事故的关系及其规律。目的是为安技人员的素质建设以及充分发挥安技人员积极性提供有效的依据和帮助