The correlation between gradients of descending roads and accident rates

The traffic accident rate on descending roads on mountainous highways is quite high. To study the effects of vertical gradient and length of road on traffic accidents on continuous descending roads, data from 1413 traffic accidents over an 85.43 km section of road were collected. By taking parameters such as the gradients in accident sites, and taking the average gradient in N km (N = 1, 2, 3, 4, 5) just prior to the accident sites as the profile parameter, scatter diagrams were drawn to explore the effects of the profile parameters on the distribution of traffic accidents. Analysis of the exponential regression and correlation analysis between accident rates and profile parameters was carried out as well. The research results show that the accident rate increases with average vertical gradient exponentially; the differences between accident rate and different profile parameters are significant; the accident rate has a good exponential relationship with average gradients when a section has a 2 km or 3 km slope just prior to the accident sites i.e. the accident rate at a specific section on a continuous descending road is related to the average gradient in 2–3 km descending sections just prior to the accident site. The findings indicate that a steep gradient alone is not the reason for an accident, but we also have to take into consideration the presence of a “continuous long” descent prior to it.     

超大城市昼夜交通事故严重程度致因对比分析——以深圳市为例

为研究夜间交通事故严重程度致因,基于深圳市3年3 244起交通事故数据,获取昼夜交通事故分布的时空特征;进一步选取交通事故集聚的南山区、福田区、罗湖区的1 798起交通事故,以交通事故严重程度为因变量,以事故原因、日期、事故形态等10个因素为候选自变量,构建广义有序Logit回归模型,对比分析昼夜不同严重程度交通事故的影响因素。结果表明:路口路段类型、疲劳驾驶、事故日期在夜间模型参数估计值分别为0.493,-0.363,-0.309,而在日间模型表现为不显著,道路路面材料在日间模型参数估计值为-0.232,而在夜间表现为不显著;事故原因、道路横断面渠化方式等因素在日间和夜间所引起交通事故的严重等级均存在较大差异。     

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou,China

Introduction: With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors. Method: The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark. Results: The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.     

高速公路隧道群交通事故风险致因分析

通过对高速公路隧道群特殊地理环境所造成的交通事故后果风险与救援时间的相关分析,提出隧道群交通事故风险致因模型,该模型将隧道群交通事故风险划分3个风险阶段,即初始事故风险、事故发展风险、最终事故风险,不同阶段的风险值受人的因素、车辆因素、隧道群环境因素和防灾救援能力大小的影响而改变。通过对隧道群的风险因素分析,认为隧道群在两毗邻隧道间将可能产生烟雾风险,驾驶人员视觉快速转换的照明风险,以及交通事故防灾控制风险;同时统计的隧道群防灾救援时间概率分布表明,救援队伍能在一定时间内快速到达事故现场,并对较晚到达事故现场救援情况,基于风险分析而提出相应的防范对策和措施。     

疲劳驾驶交通事故的严重程度影响因素分析

为探究和定量分析疲劳驾驶交通事故严重程度的影响因素,以广东省1 370条疲劳驾驶事故数据为基础,对比分析不同年份、时间段以及年龄段的疲劳驾驶交通事故特征;以交通事故严重程度为因变量,将其分为严重事故和非严重事故,从驾驶员年龄、驾龄、车辆类型等17个初步选择的自变量中筛选对疲劳驾驶交通事故严重程度具有显著影响的因素;采用二元Logistic回归模型分别对全体数据和不同道路类型下的数据建立疲劳驾驶交通事故严重程度预测模型,并对模型进行参数估计和检验。研究结果表明：模型拟合度良好,准确性高;对疲劳驾驶交通事故严重程度具有显著影响的因素有年龄、人员类型、车辆类型、道路类型、道路线形和能见度;车辆类型和道路线形是影响城市道路交通事故严重程度的重要因素,能见度是影响1,2级及其他更低级道路交通事故严重程度的重要因素。     

Road traffic accident severity analysis: A census-based study in China

Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.     

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, “adverse weather,” which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.     

我国公路交通事故的现状及特征分析

根据2000—2006年全国公路交通事故统计数据,以2006年为重点,分析当前我国公路交通事故的发展形势、地区差异和事故特征。研究表明:我国的公路交通事故自2003年开始总体由快速上升转为持续下降,但高速公路和西部地区的公路交通事故仍有增长可能;与经济欠发达地区相比,经济发达地区的事故总量较大,但事故死亡率较低;国道、省道和高等级公路的事故死亡率均大幅高于其他公路;约80%的死亡事故是发生在平直、一般弯和一般坡等良好路段上的车辆互碰、碰撞行人和翻车,76%的特大事故属于坠车和正面碰撞;雾、雪等恶劣天气更易在高等级公路上引发交通事故。     

高速公路事故责任认定探讨

在事故责任认定中应同时考虑司机、交警、管理部门和道路条件等存在的问题,分析并量化各方面应承担的责任,而不能像目前,事故责任几乎仅由司机一方100%承担。以开阳高速(开平到阳江)“1·23”特大交通事故为例,着重论述了当前我国高速公路管理体制存在的弊端,事故责任认定和事故赔偿的不合理性,紧急救援体系的不完善性。并对高速公路事故责任的量化,交通警察现场指挥能力的提高,高速公路管理部门责任的落实与监督,应急救援及强制保险理赔体系的进一步完善提出相应的建议和措施,促进我国交通事故责任认定与落实全过程的科学、合理、公平及规范化。     

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.     

广东省特大道路交通事故统计分析及预防对策

通过调查收集2000年1月—2010年8月广东省的特大交通事故数据,以事故形态、空间、时间为分类依据,分析广东省特大交通事故的统计分布特征;在此基础上,针对碰撞型和翻坠型事故分析事故形态、公路等级、事故时段与死亡人数之间的联系。研究表明:广东省特大道路交通事故的主要形态为碰撞型和翻坠型,高发于高速公路和低等级公路,主要发生在凌晨时分、早晨时分、午后时分以及傍晚时分。广东省境内发生的特大交通事故是疲劳驾驶、超速行驶、超载运营、行车环境等因素综合作用的结果,建议采取改进驾驶员培训考核内容、改善行车环境、加强违章监控力度等措施保障交通安全。     

Data fusion,ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea

Increasing amount of road traffic in 1990s has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving environmental factors based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we use various algorithms to improve the accuracy of individual classifiers for two categories of severity of road traffic accident. Individual classifiers used are neural network and decision tree. Mainly three different approaches are applied: classifier fusion based on the Dempster–Shafer algorithm, the Bayesian procedure and logistic model; data ensemble fusion based on arcing and bagging; and clustering based on the k-means algorithm. Our empirical study results indicate that a clustering based classification algorithm works best for road traffic accident classification in Korea.     

Effect of novel divergence markings on conflict prevention regarding motorcycle-involved right turn accidents of mixed traffic flow

Introduction: In Taiwan, segregated traffic flow countermeasures have long been in place. Although these facilities have decreased the numbers of motorcycle left-turn collisions, right-angle collisions, and sideswipe collisions, they have also induced serious right-turn accidents. The purpose of this research was to evaluate an intervention intended to decrease conflicts and motorcycle-involved crashes. In this study, the reasons why the motorcycle accident rate is higher at intersections with slow lanes than at those without slow lanes are presented, and the theory of the self-explaining road was applied to create divergence markings for a mixed traffic flow environment. An intervention that guides motorcycles and cars into appropriate locations at intersections was applied to three intersection approaches. Method: The intervention effectiveness was evaluated by comparing the number of accidents at the intersections before and after the implementation of improvement measures. Moreover, video recordings were used to analyze the traffic distributions at the cross-sections of intersections. T-test was adopted to examine whether the traffic flows at the cross-sections of the intersections before and after the intervention were statistically different. In addition, this research applied the post-encroachment time (PET), the time between the first road user leaving the encroachment zone and the second road user arriving in it, to evaluate traffic conflicts. Finally, the PET and severity index between a straight-through motorcycle and a right-turn vehicle were analyzed. Results: PET increased by 3.2%–20.4%, and the rates of right-turn collisions, sideswipe collisions, and rear-end collisions decreased by 64.3%, 77.3%, and 61.5% respectively. Conclusions: Eliminating the slow traffic lane and setting divergence markings may not effectively cause vehicles in different driving directions to drive in the proper locations in the lanes. However, divergence markings both reduce the rate of right-turn collisions and decrease the incidence of sideswipe and rear-end collisions. Practical applications: The proposed design method may be a good design reference for countries having a high motorcycle density.     

Cultural and socio-demographic predictors of car accident involvement in Norway,Ghana, Tanzania and Uganda

The prevalence of road traffic accidents is increasing towards endemic proportions in developing countries. The present study investigated cultural and demographic predictors of car accident involvement in a developed country in Europe and three developing countries in Sub-Saharan Africa. The comparison was carried out with questionnaires among a randomly obtained representative sample of the Norwegian population (n = 247) as well as stratified samples in Accra and Cape-Coast in Ghana (n = 299), Dar-es-Salaam and Arusha areas in Tanzania (n = 599), and the central Kampala and the Mbarara district in Uganda (n = 415). Measurement instruments of culture as symbol exchange and destiny orientation were used to predict self-reported road traffic accident involvement by car among the respondents. Demographic characteristics, such as gender, age and education, were also included as predictors of car accidents. The results showed that male gender was the only significant predictor of accident involvement in Norway. Introverted and extroverted culture, destiny orientation and written culture were associated with accident involvement in the African countries. Male gender also predicted accidents in these countries. Non-technical injury preventive countermeasures in developing countries could focus on cultural practice and fatalistic beliefs. Countermeasures in both developed and developing countries should target male drivers.     

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%。     

Injury severity analysis of accidents involving young male drivers in Great Britain

Introduction

Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain.

Method

Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal.

Results

Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway.

Impact on Industry

These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers.     

公路隧道火灾事故调研与对策分析

通过调研国内外近60年来,有代表性的33起公路隧道火灾事故,对公路隧道火灾事故起因、特点及危害进行了详细分析,结合国外公路隧道火灾安全措施以及防火安全评估经验,提出几点降低公路隧道火灾事故风险,减少事故危害的对策与建议。以期为公路隧道规划、设计以及建设运营等工作提供有益的参考。     

Simulating the relative influence of tire,vehicle and driver factors on forward collision accident rates

Introduction: There is currently a strong focus within the automotive industry centered on traffic safety, with topics such as distracted driving, accident avoidance technologies, and autonomous vehicles. These papers tend to focus on the possible improvements from a single factor. However, there are many factors that are present in each accident, and it is important to understand the influence of each factor on the relative accident risk in order to identify the most effective approaches for improving driver safety. Rear-end accidents tend to be the most common accident type with approximately 1.8 M cases, or 31% of all accidents, in 2012, according to NHTSA. Of the rear-end accident scenarios, approximately 18–23% occur on wet surfaces. Method: A Monte Carlo Forward Collision Simulation models the conditions of a wet rear-end accident and estimates the relative impact of various vehicle collision parameters. The model takes distributions of these parameters as inputs, and outputs a risk of collision relative to a known reference case. The parameters that can be studied include: tire grip level, road grip level, vehicle velocity, following distances, and the presence of vehicle technologies (ABS, FCW & AEB). Distributions of some of these parameters have been improved thanks to Naturalistic Driving Study data from SHRP2. Results: This study shows that these vehicle systems have a large impact on safety and can change the amount of influence attributed to other parameters such as tire grip levels. As the use of automated vehicle systems expands, so will the influence of tire grip performance levels on collision risks. Practical Applications: It is more important than ever for consumers and auto manufacturers to consider tire performance levels. Therefore, the tire industry should continue to focus on wet grip as a key performance related to safety and should strive to continue to improve tire performance.     

基于哈默“人的差错理论”的道路交通安全研究

以道路交通安全系统为背景,人的差错为研究方向,运用"哈默"人的差错分类理论,从机动车驾驶员和非机动车驾驶员、乘客及行人两种人群的差错角度,对2004—2006年3年间我国交通事故发生率和死亡人数进行较有针对性的分析归类和研究,进而分别提出基于主动性和被动性两方面的交通事故人因预防措施。通过"哈默"全新的分类方法,全面分析交通行为中人的差错类型,为道路交通事故预防体系的研究、建立以及进一步的发展,提供了有参考价值的基于人的行为机理的理论依据和合理化建议。     

Behavior and lifestyle characteristics of male Kuwaiti drivers

Introduction: The high traffic accident risk among young drivers is a well-known and well-documented fact in most countries. Lifestyle has proven to affect driving behavior as well as accident risk. This study covers the lifestyle component of the problems related to young male Kuwaiti drivers’ accident risk. Methods: The purpose of the study is to measure the relationship between lifestyle and accident risk. Lifestyle is measured through a questionnaire, where 302 male Kuwaiti drivers (mean age = 28 years; range 25-35 years) answer 39 questions related to behavioral and social factors, road conditions, police enforcement, and life satisfaction. They also report their involvement in accidents and traffic violations. Results: The questionnaire's validity and reliability (Cronbach's alpha = 0.7) were achieved. Principal component analysis reduced the 39 items on the questionnaire to 5 factors. Inadequate police enforcement is strongly correlated (r = 0.862) to accident risk and traffic violations and is thus considered the best predictor of traffic accidents in Kuwait. Impact on Industry: As driving-related incidents (on-the-job and off-the-job) are a significant source of fatalities and lost-work-days, the study points to the importance of considering cultural factors in the design of comprehensive safety programs for industry.