Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.     

A data mining approach to deriving safety policy implications for taxi drivers

Introduction: Traffic safety issues associated with taxis are important because the frequency of taxi crashes is significantly higher than that of other vehicle types. The purpose of this study is to derive safety implications to be used for developing policies to enhance taxi safety based on analyzing intrinsic characteristics underlying the cause of traffic accidents. Method: An in-depth questionnaire survey was conducted to collect a set of useful data representing the intrinsic characteristics. A total of 781 corporate taxi drivers participated in the survey in Korea. The proposed analysis methodology consists of two-stage data mining techniques, including a random forest method, with data that represents the working condition and welfare environment of taxi drivers. In the first stage, the drivers’ intrinsic characteristics were derived to classify four types of taxi drivers: unspecified normal, work-life balanced, overstressed, and work-oriented. Next, priority was determined for classifying high-risk taxi drivers based on factors derived from the first analysis. Results: The derived policies can be categorized into three groups: ‘the development of new policies,’ ‘the improvement of existing policies,’ and ‘the elimination of negative factors.’ Establishing a driving capability evaluation system for elderly drivers, developing mental health management programs for taxi drivers, and inspecting the taxi's internal conditions were proposed as new policies. Improving the driver's wage system, supporting the improvement of rest facilities, and supporting the installation of security devices for protecting taxi drivers are methods for improving existing policies to reinforce the traffic safety of taxi drivers. Last, restricting overtime work for taxi drivers was proposed as a policy to eliminate negative factors for improving taxi traffic safety. Practical Applications: It is expected that by devising effective policies using the policy implications suggested in this study, taxi traffic accidents can be prevented and the quality of life of taxi drivers can be improved.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

均匀火灾温场下隧道管片受力分析

对于以盾构方式修建的地铁,其隧道管片是主要的受力构件。在以热辐射为主的火灾中,会形成比较均匀的温度场。由于管片受热均匀,整周管片内部将产生均匀的热力荷载,加之地应力荷载使其应力分布更为复杂。使用FLAC3D模拟在以热辐射为主的火灾中,管片应力分布和变化特征。根据温度不同材料性质不同的客观事实,在模拟过程中随温度升高实时调整材料参数,使模拟结果更加准确。分析结果表明:最大水平压力上升比较快,1000℃时其比最大竖向压力大30%。最大值的位置分别对称于经过隧道轴线的水平和竖直面。总位移在400℃后明显增加,最大位移一般在隧道左右两侧。     

一种基于层次分析法的危险化学品源安全评价综合模型

危险化学品源的安全评价是安全生产管理中的一项重要内容.目前用于危险化学品源安全评价与分级的常用方法有后果分析法、道指数法、蒙德指数法以及使用临界系数判别重大危险源的方法等.在实际应用中,单独使用某一方法时,由于存在各种片面性问题而得不到满意的评价结果; 几种方法同时使用时,其评价指标、评价结果以及结果的形式又会互相冲突.为解决上述问题,建立了基于层次分析法的综合评价模型.首先,根据安全评价要求构建3层次的评价体系,在各层次中构造判断矩阵,并计算4种常用方法相对综合评价模型的置信度; 其次,对各方法统一危险分级标准,均采用危险分数划定危险级别,并取各危险分数的加权平均值--综合危险分数作为综合评价模型下的危险源分级标准.采用综合评价模型可消除单一方法进行评价时的片面性和偏差,同时,评价结果的一致化使得判断危险化学品源的危险级别以及由此采取相应级别的管理措施成为可能,将更有利于实际安全生产管理指导.     

A systems-based approach for modeling of microbiologically influenced corrosion implemented using static and dynamic Bayesian networks

Microbiologically influenced corrosion (MIC) is a microbial community assisted degradation of materials affecting chemical processing and oil and gas industries. MIC has been implicated in incidents involving loss of containment of hazardous hydrocarbons which have led to fires and explosions, economic and environmental impact. The interplay between abiotic environmental factors and dynamic biotic factors in MIC are poorly understood. There is a lack of mechanistic understanding of MIC and very few models are available to predict or assess MIC threat. Here we report on the development of a model to assess the susceptibility to MIC. The high-resolution model utilizes 60 independent nodes, including operational and historical failure analysis data, and is built by combining empirical relationships between the abiotic and biotic variables impacting MIC. Both static and dynamic Bayesian-network (BN) approaches were used to combine heuristic and quantitative states of variables to ultimately yield a susceptibility measure for MIC. A confidence-in-information metric was generated to reflect the amount of data used in the estimation. A susceptibility to MIC of 45%–60% was estimated by the model for ten different scenarios simulated using case-studies from literature. The susceptibility to MIC estimated by these scenarios was further interpreted in the context of these cases. This systems-based MIC model can be utilized as an independent estimator of susceptibility or can be incorporated as a sub-model within comprehensive safety threat assessment models currently utilized in industry.     

安全管理新概念--关于"安全学习志"的探讨

本文提出了一种基于安全学习志的安全管理新概念.安全学习志是一种用于解答安全管理难题,将组织的经验知识转化为行动的管理工具.它继承了中华民族修志写史的优良文化传统,同时也借鉴了国外商业领域利用学习志集中学习互动的优点.具体从编撰方法、功能作用、推广应用及发展前景等几个方面阐述了这种管理概念的可行性.     

基于HAZOP分析的加氢装置工程设计方法

加氢装置属甲类火灾、爆炸危险生产装置。为了在设计阶段尽可能消除或控制潜在风险,本文总结了多套加氢装置HAZOP分析报告中的设备类别及其分析内容,提出了基于危险与可操作性(HAZOP)分析的加氢装置工程设计方法。在传统工程设计方法的基础上增加了参数敏感性工程设计方法,依据分析报告中的设计建议,利用ASPEN软件计算过程参数变化对目标参数的影响程度,确定参数稳定操作区域;建立了数据库管理界面实现了加氢装置工程安全设计经验知识的有序管理。应用基于HAZOP分析的加氢装置工程设计方法,有助于将安全隐患问题在设计阶段消除或加以控制,可为降低石化装置改造成本和提高装置的安全水平提供方法依据。     

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.     

基于3个维度的火电厂安全评价新方法

为了改善火电厂安全评价中存在的主观性和单一性,首次提出基于3个维度的火电厂安全评价方法.利用DEA(Data Envelopment Analysis,数据包络分析)模型得到相对安全度;把影响安全因素的重要参数分为3类,通过无量纲处理得到参数安全度;借助专家打分处理得到潜在安全度.3个维度的原始数据经处理被统一到[0,1]区间,进而通过岭形模糊隶属函数模糊化把最后的安全评价结果映射到危险、较危险、较安全、安全4个评语等级.最后通过某自备电厂不同月份的安全评价算例证明了该方法的可行性.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

基于PLM的矿用低压变频器漏电机理的研究

解释了矿井低压电网系统引入变频器后单相漏电故障电流增大的机理。以人身触电事故为研究对象,建立煤矿井下低压电网变频器系统单相漏电故障的分段线性模型,推导出故障电流的有效值,并经 MATLAB 仿真进行了验证。其分析结果表明,人身触电电流大幅度提高,人身安全水平显著下降。并进一步归纳出矿井低压电网变频器系统单相漏电故障电流的影响因素及一般规律。实验结果验证了理论分析与仿真的正确性。     

A risk reduction approach for academic research labs: A case study on naphthenic corrosion

Academic research and development (R&D) labs are a significant part of academic life. But there can be physical, environmental, and experiment quality risks associated to this activity. Academic labs can present specific experiments, which have associated risks for researchers. Academic labs are also characterized by a high turnover of students and many of them are not fully aware of the level of physical and environmental risks of their activity. Accidents in academic labs with injuries and loss of life are facts that have to be tackled through risk management approaches. The objective of this paper is to present an integrated management approach, tackling risk management and analysis methods. HAZOP (Hazard and Operability Study) and PFMEA (Process Failure Mode and Effects Analysis) enabled, respectively, the analysis of safety and environmental risks. By quantifying the level of risk according to the type of experiment and the research context, it is possible to provide safety to the system. The resulting Digital Poka-Yoke – a mistake-proofing approach – has brought about the desired quality of results in experiments. The proposed approach was validated through a case study monitoring naphthenic corrosion experiments conducted by the Lab of Surface Electrochemistry and Corrosion (LSEC) at the Federal University of Paraná (UFPR). As a consequence, this approach is currently in use at this lab.     

基于ISM和模糊故障树的铁路黄土隧道塌方风险分析

研究如何对铁路黄土隧道塌方风险事故进行管理控制,先从地质因素、施工方法因素、监控量测因素和施工管理因素分析了黄土隧道塌方事故的成因。接着依据铁路行业隧道风险评估指南矿山法隧道施工风险因素列表,识别影响兰渝铁路某黄土隧道项目塌方的18项主要风险因素。然后运用系统工程解释结构模型方法(ISM)分析项目各风险因素间的相互关系,得出影响该隧道项目塌方的直接原因为施工扰动过大、支护不及时、拱脚悬空时间过长和信息反馈处理不及时。底层原因则为开挖方式、特殊地质条件。在上述基础上建立项目塌方事件模糊故障树模型,进行模糊定量分析,得到项目塌方事件发生的模糊概率和关键风险因素的模糊重要度。结果表明,实例黄土隧道项目塌方事故风险概率很大,地质因素、开挖方法等对塌方事故影响最大。解释结构模型方法能揭示各风险要素间的不确定性因果关系,有利于把握事故发生机理,模糊数能客观地描述风险事件的发生概率,增强故障树诊断方法的可靠性。     

Predicting major accidents in the process industry based on the barrier status at scenario level: A practical approach

OCI Nitrogen wants to gain knowledge of (leading) indicators regarding the process safety performance of their ammonia production process. This paper answers the question whether indicators can be derived from the barrier system status to provide information about the development and likelihood of the major accident processes in the ammonia production process.The accident processes are visualized as scenarios in bowties. This research focuses on the status of the preventive barriers on the left-hand side of the bowtie. Both the quality – expressed in reliability/availability and effectiveness – and the activation of the barrier system give an indication of the development of the accident scenarios and the likelihood of the central event. This likelihood is calculated as a loss of risk reduction compared to the original design. The calculation results in an indicator called “preventive barrier indicator”, which should initiate further action. Based on an example, it is demonstrated which actions should be taken and what their urgency is.     

基于STAMP模型的建筑事故致因因素定量分析方法研究

为揭示建筑安全事故致因因素间的相互关系从而对其进行有效预防和控制,探讨1种STAMP模型的定量分析方法,用于深入剖析事故致因因素间的逻辑关系和属性特征。基于STAMP系统事故理论模型,从建筑工程安全控制结构入手逐层定性分析事故的致因因素;引入灰色DEMATEL与ISM相结合的方法对系统事故间的复杂逻辑关系进行层级划分;运用MICMAC分析计算各致因因素的驱动力和依赖度数值并判断其属性类别。结果表明:提出的定量分析方法运用到江西丰城冷却塔坍塌事故中的分析结果与事故调查报告相契合,能较全面地说明各层次结构间的相互作用。