灰色关联分析在道路交通事故中的应用

全国或某一地区的道路交通安全系统是一个灰色系统,可应用灰色关联分析法方便地确定影响交通安全的主要相关因素。由灰色关联度导出灰色关联序,以进行优势分析,从而得到交通事故的最(准)优因素及最(准)优特征。根据1990—2000年全国道路交通事故以及全国人口、机动车、公路里程和客货运输量等主要相关因素统计资料可知,货运量、货运周转量和客运量是影响交通安全的主要相关因素,死、伤人数是交通事故的主要特征和指标。由哈尔滨市2000年道路路段交通事故与交通组成统计资料可知,城市道路交通事故的主要影响因素是各类货车、摩托车和大型车。因此,加强货车和货运管理对提高我国交通安全水平具有重要意义。     

道路交通事故

翻阅历史资料,一件件血淋淋道路交通惨案,催人泪下,逼人思考。 1999年5月5日早晨,浙江省常山县一辆载有芙蓉乡初级中学三年级43名学生和教师赴镇参加考试的中巴车,向右冲出公路,翻入落差５１．08米的长厅水库中,造成32人死亡（其中学生29人、教师1人,搭车客和售票员各1人）,１７人受伤。经查,事故的直接原因：一是违章超载驾驶。肇事车辆额定载客19人,实际载客49人,致使车辆的驾驶机动性能以及制动性能受到严重影响,不能正常减速和拐弯;二是车辆带病行驶。该车两只后轮制动分泵漏油严重,制动失效。右前轮挡泥板与轮胎有磨擦…     

基于灰色关联分析的车辆交通事故规律研究

针对车辆交通事故统计数据少、事故原因划分模糊等特点,运用灰色系统理论思想,建立车辆交通事故灰色关联分析模型。根据车辆交通事故起数、死亡人数、受伤人数与各事故原因之间的灰色关联度,计算车辆交通事故因素关联序,确定车辆交通事故各原因的重要程度。分析影响车辆交通事故的最优支配因素,并据此研究车辆交通事故规律。研究结果表明,部队汽车驾驶员交通违法是造成车辆交通事故的主要原因,违法违纪成为制约车辆安全管理的一个重点和难点问题。     

减少道路交通事故的措施

据统计资料,我国目前汽车保有量近1200万辆,且每年以15%左右的幅度增长,农用机动车及摩托车等轻型机动车的数量已超过2000万辆.机动车总量的增加既带动了运输业的发展和提高了人们工作与生活的方便程度,但也相应的造成了道路交通事故的不断发生,给国家财产和人们生命带来巨大损失.     

基于灰色马尔可夫的道路交通事故预测

探讨灰色马尔可夫模型在道路交通事故中的具体应用。灰色模型适用于短期、数据量少和波动不大的预测问题,在长期预测时,数据序列拟合较差,预测精度偏低;而马尔可夫链适用于长期、数据序列随机波动大的预测问题。灰色马尔可夫模型结合了灰色GM(1,1)模型和马尔可夫理论的优点,利用灰色模型进行长期预测,再利用马尔可夫链理论进行波动状态预测,最后得到期望值。该模型克服了随机波动性数据对道路交通事故预测精度的影响,提高了灰色预测的准确度。实例结果,证明灰色马尔可夫GM(1,1)模型具有较好的应用价值,为道路交通安全管理提供了有用依据。     

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。     

道路交通事故多发的特点及原因

发生道路交通事故的特点 1.机动车驾驶员超速行驶，临危措施不当．疏忽大意、违章占道等违章行为导致的道路交通事故占事故总数的90%以上。     

火灾事故致因的多因素灰色关联分析

运用灰色关联分析方法,以河南省6年的火灾事故统计数据为例,通过计算和分析纵火、电气、违反操作规程等多因素与火灾事故间的灰色关联度及灰色关联序;确定出影响火灾事故的主要相关因素;得出火灾多因素与火灾事故间关系的定量化分析结果。该研究为确定火灾因素间的关系提供了一种较为科学的定量评价方法,对分析事故主要原因、掌握火灾事故发生规律提供支撑,对提出有效对策有重要的理论意义和实用价值;同时,有利于减少火灾事故的发生,促进社会的安全与稳定。     

矿井安全状况影响因素的灰色关联分析

本文运用关联分析理论对影响矿井安全状况的各种因素进行了关联分析，找出并分析了影响矿井安全状况的主要因素，次要因素，为安全管理决策提供了科学依据。     

事故车辆影响下的城市道路交通仿真研究

为研究事故车辆影响下城市道路交通的特征，构建考虑车辆抢道行为的元胞自动机交通流模型，研究给定冲突区域长度下不同进车率和不同事故持续时间对城市道路交通流的影响。研究结果表明:事故车辆会诱发交通瓶颈，对城市交通产生显著干扰并形成拥堵带，且拥堵带向事故车辆上游传递。不同事故持续时间下交通流演化存在差异，道路平均车流量、车流平均密度随着事故持续时间的增加而增加，车辆平均速度随之减小。当道路中车辆较少（pin=0.3）且事故持续时间达到15 min时，交通处于严重拥堵状态；当道路中车辆较多（pin=0.5）、事故持续时间达到5 min时，交通即处于严重拥堵状态。研究结果可为优化城市交通事故处理机制提供依据。     

重大道路交通事故救援“任务—职责—履责”探讨

突发事件应对中因救援任务不明确、应对主体职责不清、履责路径不通畅而导致事态扩大和严重问责的情况时有发生,这已成为应急绩效评估中常见的、亟需解决的问题。以某次特别重大道路交通事故应急为切入点,借助高速公路特殊路段典型交通事故救援演练实践,梳理了道路交通事故救援任务单元,匹配了各任务单元的责任主体和应急职责,并析出基于各责任主体应急角色交互的履责路径。该研究对提升当地道路交通事故应急救援整体水平具有借鉴价值。     

The intercoder agreement when using the Driving Reliability and Error Analysis Method in road traffic accident investigations

Many different classification schemes have been used in the analysis of road traffic accidents but the agreement between coders using the same classification scheme is rarely tested and/or reported. As a high intercoder agreement is a prerequisite for a study’s validity, this is a serious shortcoming. The aim of the present study was, therefore, to test the intercoder agreement of the Driving Reliability and Error Analysis Method (DREAM) version 3.0 by letting seven coders from different European countries analyse and classify the causes of the same four accident scenarios. The results showed that the intercoder agreement for genotypes (contributing factors) ranges from 74% to 94% with an average of 83%, while for phenotypes (observable effects) it ranges from 57% to 100% with an average of 78%. The results also showed that weaknesses in classification schemes, methods, training of coders as well as in presentation of accident information can be identified by testing the intercoder agreement.     

隧道爆破振动影响因素的灰色关联分析

为优化隧道爆破方案,降低爆破振动危害,采用灰色关联分析法(GRA)对爆破振动的影响因素进行分析。首先确定振动速度、主振频率和振动持续时间作为系统特征变量,确定总药量、最大段药量、掏槽孔最大段药量、雷管段数、爆心距、最小延期间隔时间、临空面数等作为相关因素变量;再结合厦门某隧道爆破实测数据,进行灰色关联计算;然后对计算结果进行排序和优势分析,得到爆破振动影响因素的主次顺序,其中准优因素为爆心距,可控准优因素为雷管段数和掏槽眼最大段药量;最后对隧道爆破参数进行优化,测点爆破振速由1.2 cm/s减小到0.74 cm/s,降振率达38.3%。结果表明,采用GRA确定爆破振动主要影响因素,为有效控制爆破振动提供理论依据。     

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

IntroductionThe effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece.MethodRandom Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively.ResultsRegarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity.ConclusionsThe study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms.Practical applicationThe identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.     

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.     

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AHP和GRA的橡胶火灾危险评价

表征火灾危险性的指标包括点燃时间、质量损失速率、比消光面积和峰值热释放速率等,但采用不同指标对材料的评价结果不一致。基于锥形量热仪测试的火灾危险指标参数,分别采用改进层次分析法和灰色关联分析方法综合评价了5种橡胶生胶的火灾危险性。结果表明,改进层次分析法与灰色关联分析得到的5种材料的火灾危险程度排序是一致的,火灾危险性从小到大依次为氯丁橡胶、三元乙丙橡胶、丁腈橡胶、天然橡胶、顺丁橡胶。     

煤层自燃危险程度的灰色关联分析

将灰色关联分析法应用于煤矿自燃危险程度的评价中,全面考虑影响煤矿生产系统安全的各种因素,结合实例建立了煤矿自燃危险程度的数学模型,阐明了灰色关联分析法的原理和算法,较好地反映了评价过程和评价因素的灰色概念,为制定防范措施和管理决策提供科学依据。