深圳地铁二期工程建设期安全事故分析

通过统计与分析深圳市地铁二期工程在建设期所发生的安全事故,发现坍塌是地铁工程建设期间的多发事故,地铁工程在基坑工程、盾构推进、隧道暗挖和高支模施工的过程中易发生安全事故.结合地铁建设过程中的主要工法,通过进一步分析,发现对辅助工法施工重视不够,临时承重结构施工时抢工期常常是引发事故的重要原因.结合工程事故的分析及建设管理经验,提出事故预防的重点,以期为下阶段大规模的地铁工程建设活动提供安全管理经验.     

长江干线船撞桥事故分析

对发生在长江干线上的船撞桥事故进行了分析研究。通过调研掌握了一百余份有关资料 ,包括四十年来发生在十余座长江大桥的 172起船撞桥事故 ,在对事故进行统计分析与研究的基础上 ,得出了一些重要的结论。长江船撞桥事故在数量上略呈增长态势 ,船队撞桥事故远多于单船 ,约占 86 %。船撞桥的概率与桥梁跨距、洪水、能见度等因素有关 ,尤其是洪水对船撞桥概率影响较大 ,近似呈正态分布。发生船撞桥事故的第一位原因是人员失误 ,占 78%以上。     

汽车碰撞事故再现估算速度的不确定度分析

不确定度分析是一种考察人试验测量输入值得到计算结果输出之间不确定度传播的数学方法。本文中将不确定度分析方法引入汽车碰撞事故再现的模型算法中，推算碰撞前速度的不确定度，给出了在汽车正面斜碰撞事故再现中的一个应用实例。通过对计算结果的分析，初步得到了不确定度的传播规律，并指出了相关问题和发展方向。     

基于动量定理的汽车碰撞事故再现模型抗扰性分析

基于摄动理论，应用3种常用的数学方法对基于动量定理建立的汽车碰撞事故模型的抗扰性进行分析。结果表明：对于特定模型在复杂的扰动条件下，采用不同的数学方法，处理效果截然不同。通过理论分析，建立了解决此类问题的方法，给出了数学表达式。经过对代表车型的实际分析，验证了该方法的有效性。     

基于能量释放事故致因模型的地铁火灾防治思路

从系统安全原理的角度建立了地铁火灾的能量释放事故致因模型,并在此模型的基础上系统性地提出了地铁火灾的防治思路。事故致因模型中将地铁火灾划分为五个阶段,根据每个阶段的不同特点,提出了相应的防治思路：潜伏期为“管理”、孕育期为“屏蔽”、发生期为“防护”、发展期为“疏导与控制”、灾后为“恢复”。在防治思路的指导下,探讨了地铁火灾不同阶段下的防治途径与措施。     

“12·7”事故对珠江口区域溢油应急合作的启发

2004年发生于珠江口的“12·7”船舶碰撞溢油事故（以下简称“12·7”事故）被成功处置后,广东海事局在总结该事故处置经验的基础上,于2005年在珠江口开展了代号为“碧洋行动”的溢油应急演练。通过对“碧洋行动”设定的事故场景之一的推演和评估可以看到珠江口区域溢油应急合作对于该区域溢油事故应急处置的重要意义。     

Organizational effects of experience from accidents. Learning in the aftermath of the Tretten and Åsta train accidents

This article explores possible signs on learning in organizations following two major railway accidents in Norway, the Tretten accident in 1975 and the Åsta accident in 2000. These are the most serious railway accidents to have occurred in the past decades and both involved collisions on a single track system. The two events have been analyzed and compared in order to investigate possible learning effects and possible differences in learning given the 25-year span between them. The study is based on the analysis of selected documents related to the accidents in general and the narrative components in the documentation related to learning in particular.Our findings indicate that learning from these events was not expressed as an explicit goal in the aftermath. Learning in these cases seems to be linked for a large part to a change in measures, technical, operational and organizational, but we have not observed any managed confirmation or comprehension processes. The Tretten accident report suggests that the possibility of human error should be mitigated through the implementation of technological barriers, while it omits all other elaborations and risk reducing measures. The Åsta accident report recommended that the work to introduce modern safety management principles that had already been initiated before the accident should be continued. Neither of the accidents yielded any new insights and the knowledge brought forward was merely perceived as confirmation of already-agreed changes.     

An application of the directional distance function with the number of accidents as an undesirable output to measure the technical efficiency of state road transport in India

By using the directional distance function (DDF) of data envelopment analysis (DEA), this study measures the technical efficiency of 37 Indian state road transport undertakings (SRTUs) for the year 2012–13. We employ the DDF as a tool for analyzing a joint production function with both desirable and undesirable outputs (i.e., the number of accidents). A comparison between the results with and without accidents shows that several SRTUs have experienced significant changes in their efficiency scores as well as in their rankings after accounting for the undesirable output. This indicates the importance of including the number of accidents – a safety standard – as representative of the undesirable output in computing the efficiency scores of SRTUs. The results of the Tobit model indicate that SRTUs with greater vehicle productivity are more efficient under both conventional DEA and DDF approaches. We also employed zero-truncated negative binomial model to assess the factors influencing the number of road accident experienced by the Indian SRTUs and found that the accident count was significantly influenced by fleet utilization and vehicle productivity.     

Strategic planning support for road safety measures based on accident data mining

When actions and measures to increase road safety are to be planned by the police and local authorities, it is necessary to consider the specific accident circumstances as well as their historical, current, and predicted course. In particular, combinations of accident circumstances not contained in existing police statistics are often neglected, but may nevertheless be relevant, e. g., due to an increasing frequency. In order to identify these undiscovered interesting combinations, we propose a framework to support strategic planning of road safety measures based on several consecutive data mining stages. The scope, type, and location of road safety measures must be planned at a strategic level several months in advance to be fully effective. Therefore, it is essential to investigate and predict the accident circumstances and the temporal changes in their frequency comprehensively. Only with the knowledge, e. g., about the temporal pattern, locations, conditions of roads or speeds, meaningful actions can be derived. The embedded data mining approaches, i. e., frequent itemset mining, time series clustering, time series classification, forecasting, and scoring, are carefully selected, coordinated, and aligned. As a result, the framework provides police users with information about circumstances of accidents that are of interest in the future and presents their previous temporal and local patterns in a dashboard. In this study, the framework is applied in four different geographical regions. Thereby, default parameter settings for all approaches are found that are particularly suitable for the framework to investigate novel geographic regions.     

Impacts of accident severity factors and loss values of crashes on expressways in Thailand

The number of road accidents and the level of accident severity have been extensively applied as the indicators for measuring the efficiency of service provision in road network systems of each country. This research utilized accident data on expressway networks during B.E.2550 (2007) to B.E.2553 (2010) (updated data was collected), in which Expressway Authority of Thailand (EXAT) as legislatively mandated unit has taken responsibility for the execution of nine expressway routes covering distances totaling over 207 km with a record of 2194 crashes. The chief objective of the study aims to forecast the accident severity through formulating Multiple Logistic Regression Model to analyze the probability of injury accident and fatal accident in comparison with property damage only accident. Its measurement comprehensively considers statistical relationship among variables such as average speed on road section, average traffic volume per day, period of time, weather conditions, physical characteristics of accident area, and causes of accident. Together, the research question is to verify whether these variables affect the opportunity or probability of three levels of accidents and investigate impacts of accident loss values due to the reduction in crash severity measures.