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。     

山区高速公路组合线形路段车道偏移行为

为在道路设计阶段确定平纵组合与相邻路段线形对车道偏离的影响,并为减少因道路线形因素引发的侧碰、追尾甚至车辆驶出路外事故提供改善依据,基于真实的山区高速公路道路设计参数及周边地形,搭建驾驶模拟场景,利用驾驶模拟试验获取小客车车道偏离数据,并对应获取车辆当前所在路段及上、下游路段的线形参数。以车辆车道内行驶为参照,沿道路行进方向,将车道偏离行为分为左偏驶离车道与右偏驶离车道。因车道偏离受驾驶人影响,采用双层Logit模型,分别判定道路线形及驾驶人层的影响。研究结果表明：相比直线路段,曲线更易引发车道偏离行为,驾驶人易偏向于曲线内侧行驶;上游300 m路段曲率差越大、平均车速越大,则车道偏离的概率增大;相对于缓坡（-2%≤坡度S≤2%）,行驶于上坡（S>2%）或下坡（S<2%）路段时,车辆车道偏离概率减小;车辆行驶于外侧车道的左偏驶离车道概率大于行驶于内侧车道;驾驶人因素对左偏驶离车道的影响比例为8.8%,对右偏驶离车道的影响比例为25.6%。研究结论可从组合线形角度帮助工程师设计更安全的山区高速公路。     

长下坡路段避险车道入口设计速度取值方法分析

为了改善长下坡路段车辆的交通安全状况,在连续下坡路段设置避险车道是减轻事故严重程度的重要被动应急措施,也是为长下坡路段失控车辆提供强制减速的最为有效的安全防护工程措施.该文通过对中国高速公路长下坡路段事故特征的分析,对避险车道入口设计速度的影响因素进行了研究,提出了避险车道入口设计速度的取值方法.     

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.     

半挂汽车列车高速公路弯道下坡路段运行安全研究

为提升半挂汽车列车在高速公路弯道下坡路段的运行安全,采用TruckSim仿真软件,构建了车辆模型、道路模型和驾驶人动力学仿真模型;基于蒙特卡罗可靠性分析法,分别建立了半挂汽车列车发生侧滑失效、侧翻失效、折叠失效和系统失效的功能函数,并选取设计速度80 km·h~(-1)的高速公路为研究路段,通过进行大量车辆动力学仿真试验,对不同圆曲线半径、纵坡坡度、路面附着系数、车速和车辆总质量对半挂汽车列车的运行安全的影响进行了数值分析。研究结果表明:半挂汽车列车发生侧滑和侧翻的概率随着圆曲线半径的增加而显著降低,在一般最小半径400 m的情况下,半挂汽车列车发生侧滑失效和侧翻失效的概率趋近于0;随着下坡坡度的增加,半挂汽车列车发生侧滑失效和侧翻失效的概率基本呈线性增长趋势;车速对于半挂汽车列车运行安全的影响尤为显著,当车速均值由60 km·h~(-1)增加到90 km·h~(-1)时,发生侧滑失效和侧翻失效的概率分别增加了634倍和336倍;车辆总质量的增加对半挂汽车列车侧翻有显著影响;在路面附着系数较低的条件下,半挂汽车列车的主要事故形态为侧滑和折叠,在路面附着系数较高的情况下,半挂汽车列车的主要事故形态为侧翻。因此,在道路设计中,应避免极限最小半径与陡坡组合,严格限速和限载可确保半挂汽车列车的运行安全性能。     

山区公路小半径弯道路段事故严重度影响因素及其异质性比较分析

为分析影响山区公路小半径路段典型事故的严重程度的相关因素及其异质性效应，基于某山区双车道公路1 067起交通事故数据，从驾驶员、车辆、道路和环境4个方面选取15个潜在特征变量，采用二项Logit模型和随机参数二项Logit模型，分别构建小半径弯道路段上追尾碰撞、正面碰撞和侧面碰撞3类典型事故的严重度分析模型，分析3类典型事故严重度的显著影响因素，并采用边际弹性系数量化分析影响因素的作用强度。结果表明，小半径弯道路段上不同形态事故的严重度影响因素存在明显差异:①追尾碰撞严重度的显著影响因素依次为摩托车、夜间、弯道转角、驾驶员年龄、季节，摩托车和冬季分别是服从(2.716.1.5642)和(-1.495，2.1162)正态分布的异质性影响因素，导致发生伤亡事故的概率为95.72%和23.58%；②正面碰撞严重度的显著影响因素依次为货车、摩托车、驾驶员超车、弯道转角和弯道长度，货车导致其伤亡事故概率增加108.8%，摩托车和弯道长度分别是服从(6.941，9.9012)和(-0.004，0.0032)正态分布的异质性影响因素，导致发生伤亡事故的概率为76.11%和9.18%；③侧面碰撞严重度的显著影响因素依次为摩托车、驾驶员年龄及弯道有接入口，摩托车和接入口分别是服从(5.211，5.1112)和(-1.408，2.1462)正态分布的异质性影响因素，导致发生伤亡事故的概率为88.87%和25.47%。④与传统二项Logit模型相比，追尾碰撞、正面碰撞和侧面碰撞的随机参数二项Logit模型的拟合优度分别提高了2.85%，4.15%，6.76%，且定量捕捉了异质性影响因素，更适用于事故严重度的精细化分析。      

高速公路交通事故数据分析方法研究

高速公路交通事故数据对管理部门提升道路交通安全具有重要意义。为研究贵州省某两条高速公路历史交通事故数据分布规律与事故发展趋势,首先利用邻近度与关联性分析方法,完善事故数据;然后分析道路特征对交通安全的影响,划分连续下坡路段、隧道路段单元范围;最后对路段单元进一步划分为区块,建立不同区块范围内的事故概率与区块位置的预测模型,其中连续下坡路段后半段符合线形关系,隧道进出口段符合二次函数关系,并根据事故分布特征提出改善方案,进而辅助管理者掌握不同特征路段未来可能发生交通事故的路段范围以及改善的优先级。     

半挂汽车列车高速公路弯道下坡路段运行安全研究（双语出版）

为提升半挂汽车列车在高速公路弯道下坡路段的运行安全，采用TruckSim仿真软件，构建了车辆模型、道路模型和驾驶人动力学仿真模型；基于蒙特卡罗可靠性分析法，分别建立了半挂汽车列车发生侧滑失效、侧翻失效、折叠失效和系统失效的功能函数，并选取设计速度80 km·h^-1的高速公路为研究路段，通过进行大量车辆动力学仿真试验，对不同圆曲线半径、纵坡坡度、路面附着系数、车速和车辆总质量对半挂汽车列车的运行安全的影响进行了数值分析。研究结果表明：半挂汽车列车发生侧滑和侧翻的概率随着圆曲线半径的增加而显著降低，在一般最小半径400 m的情况下，半挂汽车列车发生侧滑失效和侧翻失效的概率趋近于0；随着下坡坡度的增加，半挂汽车列车发生侧滑失效和侧翻失效的概率基本呈线性增长趋势；车速对于半挂汽车列车运行安全的影响尤为显著，当车速均值由60 km·h^-1增加到90 km·h^-1时，发生侧滑失效和侧翻失效的概率分别增加了634倍和336倍；车辆总质量的增加对半挂汽车列车侧翻有显著影响；在路面附着系数较低的条件下，半挂汽车列车的主要事故形态为侧滑和折叠，在路面附着系数较高的情况下，半挂汽车列车的主要事故形态为侧翻。因此，在道路设计中，应避免极限最小半径与陡坡组合，严格限速和限载可确保半挂汽车列车的运行安全性能。     

基于混合Logit模型的高速公路交通事故严重程度分析

为从多方面掌握影响高速公路事故严重程度的因素,基于统计分析方法构建事故严重程度模型,分析其与道路、环境、驾驶员及车辆等因素间关系.鉴于多项Logit模型难以解析异质性及各因素对事故影响的交互作用,构建了混合Logit模型,并提出了刻画参数间相关性的方法.结果表明,考虑参数间相关性的混合Logit模型比多项Logit模型有更好的拟合优度,且能更合理地反映各因素对事故严重程度的作用效果;碰撞护栏或桥墩、女性驾驶员或驾驶员超过56岁时,更易产生受伤和死亡事故;能见度低于200 m、驾驶员驾龄小于3年或超过10年、责任车辆为重型货车或车辆变更车道时,发生财产损失事故的概率增加,而发生死亡和受伤事故的概率有所降低;湿滑路面将导致受伤事故的概率增加3.7%,而混凝土护栏和夜间无照明时将使死亡事故的概率分别增加8.7%和28.8%.      

高速公路连续下坡路段线形质量评价方法研究

针对高速公路连续下坡路段交通事故频繁发生,而设计规范对该类路段线形设计又没有制定特别标准的状况,通过分析高速公路连续下坡路段线形指标的特点构造线形质量评价参数,通过收集国内多条高速公路10个连续下坡路段的线形几何指标、交通事故数、交通量等数据,应用统计分析方法,建立了连续下坡路段线形评价参数与亿车公里事故率之间的关系模型,通过分析线形评价参数与亿车公里事故率之间关系曲线的拐点及事故多发的阈值,确定了线形质量评价参数的行车安全区间,为高速公路连续下坡路段线形设计提供指导。     

基于负二项分布的高速公路交通事故影响因素分析

为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系.鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法.结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随...     

考虑多模式失效概率的长下坡路段重型卡车事故预测模型

为挖掘多模式失效概率与长下坡路段重型卡车事故之间的关系，建立了重型卡车在长下坡路段的多模式失效概率与车辆事故之间的关系模型。并针对重型卡车在长下坡路段可能的失效模式，如侧滑、侧翻、视距不足、制动失效，在此基础上建立了多模式失效概率预测模型；通过蒙特卡罗法模拟并求解单模式失效的概率，宽界限法求解失效系统的多模式失效概率；将多模式失效概率作为解释变量与其他道路因素结合，分别建立泊松模型、随机效应泊松模型、随机参数泊松模型，将多模式失效概率与重型卡车事故建立函数关系；对比3种模型的拟合优度指标，优选出最优事故预测模型，用来挖掘重型卡车事故与多模式失效概率之间的关系。以华盛顿州71段长下坡10年的重型卡车事故数据及道路设计数据进行方法验证。结果表明:随机参数泊松模型与随机效应泊松模型的拟合优度相差较小，二者均优于泊松模型；当考虑多模式失效概率时，平曲线半径、纵坡坡度、超高对重型卡车事故的影响均不显著，即三者的影响被削弱，尤其是平曲线半径和超高，多模式失效概率的弹性(0.239)远大于二者的弹性(平曲线半径和超高的弹性分别仅为0.097和0.002)；重型卡车的事故与多模式失效概率近似线性关系，且截距不为0。即多模式失效概率可用于道路安全分析的表征指标，但与事故概率不等价。      

Factors influencing crash involvement during early years of owning a new vehicle

Though automobile manufacturers are investing efforts to make newer vehicles safer to drive, an element of uncertainty with the new vehicle seems to persist with the drivers during the early years of ownership. This could be due to a lack of familiarity of the vehicle's power, dimensions or available technologies/features. While the uncertainty in itself is a potential cause of a crash, it is important for the policy-makers, practitioners, and automobile manufacturers to understand the factors that could further aggravate the problem. This research focuses on identifying the factors influencing the likelihood of getting involved in a crash and its severity when driving a new vehicle. Crash data for North Carolina for the years 2013 to 2018 (six years) was used develop partial proportionality odds models, compute the odds ratios, analyze the effects of explanatory variables, and identify factors influencing crashes by the age of the vehicle. The likelihood of getting involved in a severe or moderate injury crash when driving a new vehicle is less for drivers in the age group ≤19 years. Erratic driving behavior (like making wide turns, weaving and swerving in traffic, driving with headlights off, driving on center-line or lane-line, etc.) and speeding increase the risk of getting involved in a moderate injury crash when driving a new vehicle. Likewise, the odds of getting involved in a crash are high on weekends and in adverse weather conditions when driving a new vehicle. They are higher when driving a new motorcycle, heavy vehicle or farm machinery. The findings help policy-makers and practitioners formulate strategies to educate drivers on factors influencing crash risk when driving a new vehicle. Further, automobile manufacturers can establish guidance programs and documentation that explain what to expect when buying and driving a new vehicle.     

城市下穿隧道纵坡坡度和速度对驾驶人心率增长率的影响

为研究城市下穿隧道纵坡段驾驶人生理和行为特征变化规律,选取22名驾驶人在早晨5：00至7：00非高峰时段,交通状况几乎无差别的环境下,开展城市下穿隧道纵坡段实车试验。利用MP150生理测试仪和ECU车速采集设备采集驾驶人的心率值和车速值,应用单因素方差分析对数据进行差异性显著检验;并分析城市下穿隧道纵坡坡度和速度对驾驶人心率增长率的影响规律,构建城市下穿隧道上下坡段坡度、速度和驾驶人心率增长率关系度量模型,量化了坡度、速度与驾驶人心率增长率之间的关系。然后采用单因素敏感性分析方法对模型中的2个自变量（坡度和速度）进行敏感性分析。结果表明：在城市下穿隧道上、下坡段行驶时,不同坡度范围下的车速和心率增长率有一定的差异性,车速和心率增长率均随坡度增大呈现先增加后减少的趋势;城市下穿隧道上、下坡段,车辆速度均是在3.5%~4.0%坡度范围下的达到最大,在城市下穿隧道上坡段行驶时,3.5%~4.0%坡度范围下的驾驶人心率增长率达到最大,而在下坡段行驶时,4.0%~4.5%坡度范围下的驾驶人心率增长率达到最大;驾驶人在城市下穿隧道下坡段行驶时,心率增长率均值均高于上坡段,驾驶人在城市下穿隧道下坡段行驶时比上坡段更紧张;驾驶人心率增长率对坡度敏感程度要高于其对速度的敏感程度,坡度的变动比速度更易引起驾驶人心率增长率的变动,驾驶人的心理紧张程度受坡度的影响较大。     

Modeling wrong-way crashes and fatalities on arterials and freeways

Wrong way driving (WWD) research and mitigation measures have primarily focused on limited access facilities. This is most likely due to the higher incidence of fatal WWD crashes with dramatic consequences on freeways, media attention, and a call for innovative solutions to address the problem. While public agencies and published literature address WWD incidence on freeway systems, the crash analyses on non-limited access facilities, i.e., arterial corridors, remains untouched. This research extends previous works and attempts to provide many new perspectives on arterial WWD incidence. In particular, one work showed that while WWD fatalities are more likely to occur on freeways, the likelihood of these crashes is higher on arterials. Hence this work with univariate and multivariate analyses of WWD and non-WWD crashes, and fatal and non-fatal WWD incidents. Results show the impressive negative impacts of alcohol use, driver defect, nighttime and weekend incidence, poor street lighting, low traffic volumes, rural geography, and median and shoulder widths. The objective here is to highlight the need for paying greater attention to WWD crashes on arterial corridors as is done with fatal WWD incidents on freeway systems. It suffices to say that while engineering countermeasures should evolve from the traditional signing and pavement markings to connected vehicle technology applications, there is a clear and compelling need to focus on educational campaigns specifically targeting drunken driving, and enforcement initiatives with an objective to mitigate WWD in the most efficient manner possible.     

Modeling injury severity of crashes involving trucks: Capturing and exploring risk factors associated with land use and demographic in addition to crash,driver, and on-network characteristics

Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. The focus of this research, therefore, is on developing a methodology, capturing and integrating data, exploring, and identifying risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics by modeling injury severity of crashes involving trucks. Crash data for Mecklenburg County in North Carolina from 2013 to 2017 was used to develop partial proportional odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit >40 mph and curves, and driver fatigue, impairment, and inattention have a significant influence on injury severity of crashes involving trucks. These outcomes indicate the need for effective geometric design and improved visibility to reduce the injury severity of crashes involving trucks. The likelihood of a severe or moderate injury crash involving a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to identify potential risk areas, proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas.     

Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.     

Impact of driving style,behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers.     

Applying interpretable machine learning to classify tree and utility pole related crash injury types

In spite of enormous improvements in vehicle safety, roadway design, and operations, there is still an excessive amount of traffic crashes resulting in injuries and major productivity losses. Despite the many studies on factors of crash frequency and injury severity, there is still further research to be conducted. Tree and utility pole/other pole related (TUOP) crashes present approximately 12 to 15% of all roadway departure (RwD) fatal crashes in the U.S. The count of TUOP crashes comprise nearly 22% of all fatal crashes in Louisiana. From 2010 to 2016, there were 55,857 TUOP crashes reported in Louisiana. Individually examining each of these crash reports is not a realistic option to investigate crash factors. Therefore, this study employed text mining and interpretable machine learning (IML) techniques to analyze all TUOP crashes (with available crash narratives) that occurred in Louisiana from 2010 to 2016. This study has two major goals: 1) to develop a framework for applying machine learning models to classify injury levels from unstructured textual content, and 2) to apply an IML framework that provides probability measures of keywords and their association with the injury classification. The present study employed three machine learning algorithms in the classification of injury levels based on the crash narrative data. Of the used modeling techniques, the eXtreme gradient boosting (XGBoost) model shows better performance, with accuracy ranging from 0.70 to 24% for the training data and from 0.30% to 16% for the test data.     

Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes.