高速公路隧道交通事故规律研究

目前我国高速公路隧道内时常发生交通事故,为研究其特点和规律,收集2 193起高速公路隧道交通事故资料,对这些资料数据进行统计,分析隧道中交通事故的时间和空间分布特征、事故形态、事故车型以及发生原因。结果表明:一天中的9:00~10:00、11:00~12:00、13:00~15:00是交通事故的频发时段,周末发生的交通事故数约占总数的40%,1、2、4、5、7月易发生交通事故;追尾和碰撞隧道壁是隧道交通事故的主要形态;违章超速和天气变化是导致事故发生的主要原因;交通事故的车型主要为底盘较轻的轿车和重型货车。     

高速公路团雾影响分析与检测预警系统开发

为解决团雾对高速公路交通运输造成的危害,保障团雾天气下高速公路的交通安全性和通畅性,通过总结团雾的特征与发生规律,团雾天气下高速公路发生交通事故的特点,分析团雾对高速公路交通安全的影响。结果表明:团雾对高速公路交通安全的影响主要在于降低能见度、削弱路面附着系数和增加驾驶人身心负担三个方面。针对雾气的消光性原理,提出高速公路团雾实时检测与预警系统。从模块的构建到设计原理的阐述,以及系统实施方式都具有较高的可行性、实用型和经济性,该系统的应用将能大大降低团雾对交通的危害,提高公路运行的科学管理水平和公路的运营效益。     

高速公路上坡段交通事故特征及致因分析

通过对宁杭高速公路东庐山段2006年1月~2010年7月的交通事故资料的统计,从事故地点、事故时段、事故形态和事故车型等方面分析了上坡段交通事故的主要特征.研究发现:高速公路上坡段发生交通事故的风险要远大于下坡及其他路段;事故多发生在长大上坡段的中后部和竖曲线段;事故形态主要是追尾、撞护栏和撞固定物,而且重特大交通事故中追尾事故比例大;夜间的事故率和严重程度高于白天,尤其是凌晨02:00~06:00时为重特大交通事故高发期;大货车和小客车是坡段内交通事故的主要车型.上坡段事故的致因为疲劳驾驶、车辆速度离散性大、交通组成复杂.      

基于多因素的高速公路团雾区限速设计

团雾作为雾天的一种,对高速公路交通安全有一定的影响,实地调研发现,中国国内多条高速公路经常性地出现团雾。通过对近年高速公路发生的交通事故统计分析,因团雾引发的事故不断增加,且事故严重程度高,造成伤亡人数多。为了降低团雾对高速公路安全造成的影响,提高高速公路团雾区的交通安全水平,分别考虑能见度、路面摩阻力、动视力以及路段交通量等因素,提出了高速公路团雾区的限速方案,为高速公路团雾区安全设计提供依据。     

泉南高速公路柳南段事故黑点鉴别及组合因素影响分析

针对泉南高速公路柳南段交通事故，采用累计频率法对道路黑点进行鉴别，曲线中累计频率大于95.0%路段有12处，即为事故黑点（段）。结合贝叶斯定理，建立了道路联合概率分布模型，以泉南高速公路柳南段94.1 km的184起交通事故数据，进行模型检验，结果表明:大雾天气时道路小半径平曲线处发生事故概率最大，约37.3%；大雨天气时12∶00～18∶00时间段发生事故概率次之，约25.7%，其中大部分事故发生在时间段12∶00～15∶00；晴天时大半径平曲线处发生事故概率最小，占事故总数约4.5%。     

互通式立交与隧道出口小间距路段事故影响因素分析

为明确隧道出口与立交小间距路段事故严重程度的影响因素,根据浙江省21处典型路段的319起事故统计数据,从事故发生天气、时间、路段特征、交通因素等方面选择8个不同的自变量,结合有序Logit模型,分析这些不同因素对交通事故的影响程度。结果表明:晴天对应的绝对事故率最高;伤亡事故在05:00—07:00及10:00—12:00高发;事故主要发生在隧道出口与渐变段起点间及出口三角端端部;尾随相撞和撞固定物为事故最主要的形态;事故发生天气、事故发生时段、隧道立交净距3个自变量对事故严重程度有显著影响,且影响大小排序为隧道立交净距、事故发生天气、事故发生时段。通过对模型的预测准确度进行分析,建立的回归模型能较好地表征实际的事故情况。     

高速公路爬坡路段交通事故特征分析

作为交通事故易发路段,高速公路爬坡路段的交通安全问题已引起广泛关注.以宁杭高速公路东庐山段为例,通过对该爬坡路段2006年1月～2010年7月的交通事故资料的统计,从事故地点、事故时段、事故形态和事故车型等方面分析了爬坡路段交通事故的主要特征.研究发现:高速公路爬坡路段发生交通事故的风险要远大于下坡及其他路段;事故多发生在直坡段的中后部和竖曲线段,坡道后段的安全性更低;事故形态主要是追尾、撞护栏和撞固定物,而且重特大交通事故中追尾事故比例大;夜间的事故率和严重程度高于白天,尤其是凌晨2:00～6:00为重特大交通事故高发期;爬坡路段大货车和小客车相互干扰严重,是爬坡路段交通事故的主要车型.研究结果可为高速公路爬坡路段交通安全的改善提供依据.     

灾害天气下高速公路运营管理系统结构研究

灾害天气对交通安全有重要影响。通过分析灾害天气下高速公路运营管理需求,对灾害天气下高速公路交通管理系统的组成和功能及实现途径进行分析,建立高速公路灾害天气交通管理系统框架和工作流程。灾害天气交通管理系统的建立将有效减少灾害天气下交通事故的数量和减轻事故的严重程度,提高运行效率。     

高速公路雾区行车限速方案研究

随着高速公路建设里程的增加以及基础建设步伐不断加快,使其通行能力强等优势方面不断显现。但同时由于自身特点在遭遇恶劣天气时,由突发团雾状况等致使车辆在高速公路行驶过程中能见度迅速下降或突变,极易发生交通事故。本论文针对当前对于团雾的形成机理进行总结,同时在分析团雾分布规律以及雾天条件下高速公路行车特征的同时,探究相应抑制雾天车速限制的模型方法,并根据车辆跟驰模型所对应的限速计算模型研究高速公路团雾条件下安全行车的速度方案。     

道路交通科学技术 2014年 第5期 内容简介

<正>基于Reti nex原理消除交通视频图像中雾影响的方法雾天由于视野不清晰,驾驶人对路况掌握不清楚。交通事故的发生概率明显高于正常天气.给交通安全造成了巨大的隐患从近年来高速公路多车相撞的事故原因看,团雾是其中一个重要方面。为增强雾天交通视频的监控效果,进一步增强交通安全控制和交通信息研判效果,消除交通视频困像中的雾影响成为研究的热点。本文介绍基于Retinae方法消除交     

2006年雾对高速公路影响规律

高速公路雾天行车存在着严重的安全隐患,而且雾天交通安全也是一个世界性的问题。目前我国通行的做法是在雾天封闭交通,这样虽然避免了交通事故的发生,但是却以完全丧失高速公路的通行能力为代价。本文通过对2006年全国主要高速公路受雾影响预报的分析,得出全国范围内各地雾发生频率,主要高速公路受雾影响特征及各路段影响频率,总结出雾对高速公路的影响规律。为今后雾天气下全国高速公路交通气象预报提供参考和指导,为驾驶员雾天行车的安全性提供保障,并为高速公路管理部门在日常工作中,重点监控雾的频发路段,有针对性采取有效地管控措施提供依据。     

浅谈一种低能见度下车辆雾灯自动开启的装置及方法

低能见度很大程度上影响着车辆行驶安全,本文主要阐述了一种低能见度下车辆雾灯自动开启的装置及方法,该研究的推广可以减少低能见度下公路交通安全事故发生的概率。     

Extremely serious crashes on urban roadway networks: Patterns and trends

Extremely serious traffic crashes, defined as having a death toll of two and greater than two, have become a serious safety concern on urban roadways in Louisiana. This study examined the different contributing factors of these crashes to determine significant trends and patterns. We collected traffic crash data from Louisiana during the period of 2013 to 2017 and found that a total of 72 extremely serious crashes (around 2% of all traffic fatalities) occurred on Louisiana urban roadway networks. As crash data contain an enormous list of contributing factors, there was an issue of ‘more features than data points’ in solving the research problem. Most of these variables are categorial in nature. We selected a dimension reduction tool called Taxicab Correspondence Analysis (TCA) to investigate the complex interaction between multiple factors under a two-dimensional map. Findings of the study reveal several key clusters of attributes that show patterns of association between different crash attributes. The conclusions of this study are exploratory, and the results can help in better visualizing the association between key attributes of crashes. The findings have potentials in designing suitable countermeasures to reduce extremely serious crashes.     

Modeling two-way stop-controlled intersection crashes with zero-inflated models on Louisiana rural two-lane highways

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.     

基于GIS分析的深圳市道路交通事故空间分布特征研究

掌握城市道路交通事故空间分布特征是城市道路交通安全管理的重要基础。基于深圳市2014~2016年的道路交通事故数据，首先应用地理编码方法对原始事故记录进行空间定位，形成事故的空间分布。其次针对考虑/不考虑路网密度的2种情况，应用密度分析方法对道路交通事故多发的区域和事故严重程度较高的区域进行鉴别，比较2种情况下区域分布的差异并分析造成这种差异的可能原因。最后利用异常点分析和热点分析2种空间聚类分析模型对事故严重程度较高的区域进行进一步鉴别，并对密度分析和聚类分析2种方法得到的结果进行了比较。密度分析结果表明：就事故频度而言，深圳市中心城区单位面积上的交通事故频度较高，而郊区单位长度道路上的交通事故分布更为密集；就事故严重程度而言，郊区的交通事故平均严重程度高于市中心区域。造成上述差异的原因可能与郊区道路限速较高等因素有关。聚类分析结果与密度分析结果相近，在郊区形成了高严重程度的事故聚类，而在中心城区形成了低严重程度的事故聚类，说明郊区的交通事故严重程度总体高于市中心区域。从2种方法的比较来看，密度分析简单易行，有助于交通管理部门对城市交通事故空间分布特征直观快速的了解；聚类分析可精确到事故点，为精细化的交通安全管理工作提供支撑。研究结果表明基于密度分析和聚类分析的研究方法对于确定道路交通事故空间分布特征有良好的作用。     

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking.     

Using machine leaning techniques for evaluation of motorcycle injury severity

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.     

考虑空间相关性的行人宏观安全研究

为了探究行人事故的发生机理,分析影响行人交通安全的显著因素,收集上海市中心城区263个交通分析小区（TAZ）的行人事故、道路、人口及土地利用数据,并开展行人宏观安全研究。考虑到TAZ之间存在的空间相关性,建立考虑空间相关性的贝叶斯负二项条件自回归模型,在条件自回归模型中对比分析了5种不同的空间权重矩阵,包括0~1邻接矩阵、边界长度矩阵、分析单元中心距离倒数矩阵、事故空间中心距离倒数矩阵这4种既有矩阵,以及首次引入的宏观安全建模中的分析单元中心距离多阶矩阵。结果表明：分析单元中心距离多阶矩阵的模型拟合效果和事故预测准确度均显著优于既有的4种空间权重矩阵,证明了在宏观安全建模过程中考虑研究对象交通特征（居民步行平均出行距离等）的必要性;人口数量、主干道长度、次干道长度、路网密度等因素均与行人事故呈现显著正相关,平均交叉口间距、三路交叉口比例等因素与行人事故呈显著负相关;相较于高等、低等土地利用强度,中等土地利用强度对行人事故的影响最大。     

Car drivers' road safety performance: A benchmark across 32 countries

The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure, or road users' behaviour. The last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology.This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties.Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter.The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.