基于模糊事故树的施工现场地下管线泄漏风险分析

针对施工引起的城市地下管线泄漏风险事故频发这一现实问题,考虑施工过程所具有的复杂性和不确定性,提出基于模糊事故树的施工现场地下管线泄漏风险分析方法。对近年城市地下管线泄漏事故资料原因进行统计,根据管线破坏的4种不同情形,从"人-机-料-法-环"着手对"施工现场地下管线泄漏风险事故"进行原因分析并建立事故树。在此基础上结合工程实例引入模糊理论进行定量分析,根据最小割集和结构重要度排序找出该项目引发地下管线泄漏风险事故的主要原因,通过计算顶事件和中间事件发生概率,判断该项目地下管线泄漏风险处于中等风险范围,并提出相应的对策。结果表明,基于模糊事故树的施工现场地下管线泄漏风险分析方法是可行的。     

图说严防企业粉尘爆炸五条规定

<正>为深刻吸取江苏昆山"8·2"中荣金属制品有限公司特别重大爆炸事故教训,在深度分析近几年来发生的类似事故情况的基础上,依照《粉尘防爆安全规定》等有关标准规范,8月15日,《严防企业粉尘爆炸五条规定》以国家安监总局令形式发布。《五条规定》要求:1厂房必须确保作业场所符合标准规范要求,严禁设置在违规多层房、安全间距不达标厂房和居民区内。     

山东省1997年各类事故状况分析及1998年趋势预测

本文在总结分析了去年山东省事故状况的基础上，首次尝试了在事故预测方面利用定性预测和一元线性回归、趋势预测等定量预测相结合的方法，预测出今年全省事故状况是：全省各类事故总量继续增加，死亡人数、直接经济损失增幅将加大，起数、受伤人数增幅将减少。结合全省实际情况，本文提出了控制和减少事故发生的对策     

基于事故深度调查的城市交通事故特征分析

为探索适合我国的事故数据深度采集标准,并分析城市道路交通事故特征及致因,基于《道路交通事故深度调查信息采集表》(简称采集表),调查人员随交警赴事故现场随机详细调查87起城市道路交通事故。借鉴"Haddon Matrix"思想建立致因分析矩阵系统,分析事故的致因。发现采集表对事故地点、事故形态及原因项分类更加具体、明确,女性驾驶员的事故发生率略低于男性驾驶员,驾驶员年龄超过60岁后,发生事故的危险性显著提高,"交叉口影响区"事故50%由变更车道引起,非机动车驾驶员未戴安全头盔是造成严重伤害的重要原因。     

北京市危险化学品道路运输事故的规律性

本文基于中国危险化学品事故官方网站公布的北京危化品运输事故信息,对2006~2016年期间在北京市境内发生的87起危化品公路运输事故进行统计,并对其发生时间、地点、成因、特点、结果、车辆性质等方面进行分析,旨在为减少本市危险化学品道路运输安全生产事故提供数据支持和科学决策依据。     

近年来山西省煤矿事故特点及对策措施

为掌握近年来山西省煤矿事故的发生特点,基于2015年—2018年山西省煤矿事故数据并结合全国煤矿事故数据进行对比,对事故等级、类型、地点、企业性质、煤矿辖区5个维度进行统计分析。结果表明:煤炭价格与煤矿安全形势有正负影响;近年来山西省煤矿安全总体形势好于全国,但机电、运输事故频发,水害事故时有发生,需要对该类事故加强防范措施;回采工作面为事故多发地点,这与全国煤矿事故特点不尽相同;国有重点煤矿的事故起数、死亡人数均高于地方煤矿企业,这主要是由于国有重点煤矿数量多、产量大的原因。应同时努力提高员工素质,坚决落实安全责任制,实现煤矿企业的安全生产。     

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。     

山东省1998年各类事故状况分析及1999年趋势预测

在总结分析了去年全省各类事故基本状况,发生规律和特点的基础上,在事故预测方面利用定性预测和一元线性回归,趋势预测等定量预测方法相结合的办法,利用计算机应用软件Ｅｘｃｅ１７．０,制定出电子表格２０张,并对原始数据进行计算机数据处理,预测出１９９９年乃至２０００年全省事故状况,并结合全省实际情况,提出了控制和减少事故发生的对策。     

事故经济损失分析

在研究国内外有关事故经济损失要素及分类方法的基础上,结合我国GB6721-86<企业职工伤亡事故经济损失统计标准>和<工伤保险条例>(中华人民共和国国务院令第375号)的相关内容,提出了实行工伤保险企业的一起事故的经济损失费用要素及分类方法,具有一定的现实意义.     

基于降低事故灾难风险的安全发展城市构建探讨

主要在事故灾难范围对安全发展城市进行探讨,提出了安全发展城市的概念。运用事故致因理论对城市事故灾难风险进行分析,提出两类危险源引起事故灾难风险因素的理论。并将城市事故灾难风险因素分为两类,第一类事故灾难风险因素是产生能量的能量源或拥有能量的能量载体,第二类事故灾难风险因素主要包括人、物、环境、管理控制能量方面的问题。并从降低事故灾难风险因素的途径,对安全发展城市各要素进行了构建。     

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data

IntroductionDriving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.MethodCrash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.ResultsModel estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.ConclusionsThe study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

A clustering approach to integrate traffic safety in road maintenance prioritization

Objectives: We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective.

Methods: The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster.

Results: Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters.

Conclusions: We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system

Introduction: Safety performance functions (SPF) are employed to predict crash counts at the different roadway elements. Several SPFs were developed for the various roadway elements based on different classifications such as functional classification and area type. Since a more detailed classification of roadway elements leads to more accurate crash predictions, multiple states have developed new classification systems to classify roads based on a comprehensive classification. In Florida, the new roadway context classification system incorporates geographic, demographic, and road characteristics information. Method: In this study, SPFs were developed in the framework of the FDOT roadway context classification system at three levels of modeling, context classification (CC-SPFs), area type (AT-SPFs), and statewide (SW-SPF) levels. Crash and traffic data from 2015-2019 were obtained. Road characteristics and road environment information have also been gathered along Florida roads for the SPF development. Results: The developed SPFs showed that there are several variables that influence the frequency of crashes, such as annual average daily traffic (AADT), signalized intersections and access point densities, speed limit, and shoulder width. However, there are other variables that did not have an influence in crash occurrence such as concrete surface and the presence of bicycle slots. CC-SPFs had the best performance among others. Moreover, network screening to determine the most problematic road segments has been accomplished. The results of the network screening indicated that the most problematic roads in Florida are the suburban commercial and the urban general roads. Practical Applications: This research provides a solid reference for decision-makers regarding crash prediction and safety improvement along Florida roads.     

The impact of environmental, vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash

INTRODUCTION: Compared to younger age groups, older people are more likely to be seriously injured or to die as a result of a traffic crash. METHOD: The aim of the study is to examine the impact of environmental, vehicle, crash, and driver characteristics on injury severity in older drivers involved in traffic crashes by using recently linked police crash records and hospitalization data from New South Wales, Australia. The severity of injury resulting from traffic crashes was measured using the International Classification of Diseases, 10th revision (ICD-10) Injury Severity Score (ICISS). RESULTS: Multivariate analysis identified rurality, presence of complex intersections, road speed limit, driver error, speeding, and seat belt use as independent predictors of injury severity in older people. The type of intersection configuration explained over half of the observed variations in injury severity. IMPACT ON INDUSTRY: Environmental modification such as intersection treatments might contribute to a decrease in the severity of injury in older people involved in road crashes.     

Urban street structure and traffic safety

IntroductionThis paper reports the influence of road type and junction density on road traffic fatality rates in U.S. cities.MethodThe Fatality Analysis Reporting System (FARS) files were used to obtain fatality rates for all cities for the years 2005–2010. A stratified random sample of 16 U.S. cities was taken, and cities with high and low road traffic fatality rates were compared on their road layout details (TIGER maps were used). Statistical analysis was done to determine the effect of junction density and road type on road traffic fatality rates.ResultsThe analysis of road network and road traffic crash fatality rates in these randomly selected U.S. cities shows that, (a) higher number of junctions per road length was significantly associated with a lower motor- vehicle crash and pedestrian mortality rates, and, (b) increased number of kilometers of roads of any kind was associated with higher fatality rates, but an additional kilometer of main arterial road was associated with a significantly higher increase in total fatalities. When compared to non-arterial roads, the higher the ratio of highways and main arterial roads, there was an association with higher fatality rates.ConclusionsThese results have important implications for road safety professionals. They suggest that once the road and street structure is put in place, that will influence whether a city has low or high traffic fatality rates. A city with higher proportion of wider roads and large city blocks will tend to have higher traffic fatality rates, and therefore in turn require much more efforts in police enforcement and other road safety measures.Practical applicationsUrban planners need to know that smaller block size with relatively less wide roads will result in lower traffic fatality rates and this needs to be incorporated at the planning stage.     

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.     

Ranking and selecting dangerous crash locations: correcting for the number of passengers and Bayesian ranking plots

INTRODUCTION: In this paper a sensitivity analysis is performed to investigate how big the impact would be on the current ranking of crash locations in Flanders (Belgium) when only taking into account the most serious injury per crash instead of all the injured occupants. RESULTS: Results show that this would lead to a different selection of 23.8% of the 800 sites that are currently considered as dangerous. CONCLUSIONS: Considering this impact quantity, the researchers want to sensitize government that giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. Additionally, probability plots are generated to provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Impact on industry Considering the impact quantity of giving weight to the severity of the crash instead of to all the injured occupants of the vehicle on the ranking of crash sites, the authors want to sensitize government to carefully choose the criteria for ranking and selecting crash locations in order to achieve an enduring and successful traffic safety policy. Indeed, giving weight to the severity of the crash can correct for the bias that occurs when the number of occupants of the vehicles are subject to coincidence. However, it is up to the government to decide which priorities should be stressed in the traffic safety policy. Then, the appropriate weighting value combination can be chosen to rank and select the most dangerous crash locations. Additionally, the probability plots proposed in this paper can provide policy makers with a scientific instrument with intuitive appeal to select dangerous road locations on a statistically sound basis. Note that, in practice, one should not only rank the crash locations based on the benefits that can be achieved from tackling these locations. Future research is also needed to incorporate the costs of infrastructure measures and other actions that these crash sites require in order to enhance the safety on these locations. By balancing these costs and benefits against each other, the crash locations can then be ranked according to the order in which they should be prioritized.     

基于熵权-TOPSIS的陕西省各市区道路交通安全评价

为评价陕西省各市区道路交通安全状况,选取了十亿地区生产总值死亡人数、平均每起事故死亡人数、致死率、万车死亡率和十万人口死亡率5个相对评价指标,应用熵权-TOPSIS法对陕西省10个地级市的道路交通安全水平进行了综合评价。结果表明,陕西省各市区道路交通安全水平与地区生产总值、人口数量、机动化水平等社会经济因素具有较强的相关性,不同市区道路交通安全水平存在明显的差异。