Road Traffic Crash Data: An Overview on Sources, Problems, and Collection Methods

Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes;insurers seeking facts about traffic crash claims;road safety researchers to access traffic crash reliable database;decision makers to develop long-term, statewide strategic plans for traffic and highway safety;and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.

Real-time crash prediction on freeways using data mining and emerging techniques

Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.

Making Data-Driven Transportation Decisions for Freight Operations

Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.

基于生成对抗网络的追尾事故数据填补方法研究

深入分析交通事故数据可以为规避事故发生、降低事故严重程度提供重要理论依据,然而,在事故数据采集、传输、存储过程中往往会产生数据缺失,导致统计分析结果的准确性下降、模型的误判风险上升。本文以芝加哥2016—2021年的101452条追尾事故数据为研究对象,将原始数据按照7∶3随机分为训练集和测试集。在训练集数据上,利用生成式插补网络(Generative Adversarial Imputation Network,GAIN)实现对缺失数据的填补。为对比不同数据填补方法的效果,同时选择多重插补(Multiple Imputation by Chained Equations,MICE)算法、期望最大化(Expectation Maximization,EM)填充算法、缺失森林(MissForest)算法和K最近邻(K-Nearest Neighbor,KNN)算法对同一数据集进行数据填补,并基于填补前后变量方差变化比较不同填补算法对数据变异性的影响。在完成数据填补的基础上,构建LightGBM三分类事故严重程度影响因素分析模型。使用原始训练集数据,以及填补后的训练集数据分别训练模型,并使用未经填补的测试集数据检验模型预测效果。结果表明,经缺失值填补后,模型性能得到一定改善,使用GAIN填补数据集训练的模型,相较于原始数据训练的模型,准确率提高了6.84%,F1提高了4.61%,AUC(Area Under the Curve)提高了10.09%,且改善效果优于其他4种填补方法。

Traffic Flow Characteristics in Work Zone and Non-Work Zone Environment and Its Impact on Road Crashes at the Segment Level

This study investigates relationships between congestion and travel time performance metrics and crashes on road segments. The study focuses on work zone routes in Iowa, utilizing 2021 commercially-available probe vehicle data and crash data. Travel time performance metrics were derived from the probe vehicle data, and crash counts were obtained from the crash data. Additional variables included road characteristics (traffic volume, road type, segment length) and a categorical variable for the presence of a work zone. A mixed effect linear regression model was employed to identify relationships between road segment crash counts and the selected performance metrics. This was accomplished for two sets of models that include congestion performance measures at different defining threshold values, along with travel time performance measures. The study results indicate that the congestion indicators, certain travel time performance measures, and traffic counts were statistically significant and positively correlated with crash counts. Indicator variables for rural interstate locations and non-active work zones have a stronger influence on crash count than those for municipal interstate locations and active work zones. These findings can inform decision-makers on work zone safety strategies and crash mitigation planning, especially in high traffic volume areas prone to congestion and queues.

Scalable and Accessible Crash Hot Spot Detection for Traffic Law Enforcement

Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with a range of customizable parameters based on location, date and time, and crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on crash density and then ranks each hot spot using a standardized z-score measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.

Restraint Usage Characteristics and Other Factors Associated with Safety of Children Involved in Motor Vehicle Crashes

Involvement in road traffic crashes as vehicle occupants is a leading cause of death and serious injury among children. The objective of this study was to investigate crash severity factors and child safety restraint use characteristics in order to identify effective countermeasures to increase children＇s highway safety. Characteristics and percentages of restraint use among child passengers aged 4-13 years were examined using highway crash data from Kansas. The association between restraint use, injury severity and characteristics of children involved in crashes were investigated using OR （odds ratios） and a logistic regression model, which was used to identify risk factors. Results showed that children, who were unrestrained, were seated in the front seat, traveling with drunk drivers and on rural roads, and traveling during nighttime was more vulnerable to severe injury in the case of motor vehicle crashes. The most frequent contributing causes related to crashes involving children included driver＇s inattention while driving, failure to yield right-of-way, driving too fast, wet roads and animals in the road. Based on identified critical factors, general countermeasure ideas to improve children＇s traffic safety were suggested, including age-appropriate and size-appropriate seat belt restraints and having children seated in the rear seat. Parents and children must gain better education regarding these safety measures in order to increase child safety on the road.

基于高速公路安全的基础信息数据库设计

为提高我国高速公路安全评价研究的数字化水平,文章通过解析高速公路安全相关的基础信息数据,以广西多条山区高速公路为主要调研对象,采集了大量基础信息资料,提出了基础信息资料的采集与集成整理方法,然后对信息要素进行识别编码,并以此建立了安全基础信息数据体系,最后通过结合数据库技术概念进行了高速公路基础信息数据库的结构设计,并提出数据库数据的管理与更新方案。

Selection of optimal U-turn locations for indirect driveway left-turn treatments on urban streets

The location of U-turn bays is an important consideration in indirect driveway left-turn treatments.In order to improve the performance of right-turns followed by U-turns（RTUTs）,this study evaluates the impacts of the separation distances between driveway exits and downstream U-turn locations on the safety and operational performance of vehicles making RTUTs.Crash data are investigated at 179 selected roadway segments,and travel time data are measured using video cameras at 29 locations in the state of Florida,USA.Crash rate models and travel time models are developed based on data collected in the field.It is found that the separation distance between driveway exits and downstream U-turn locations significantly impacts the safety and operational performance of vehicles making right turns followed by U-turns.Based on the research results,the minimum and optimal separation distances between driveways and U-turn locations under different roadway conditions are determined to facilitate driver use of RTUTs.The results of this study can be used for future intersection improvement projects in China.

基于安全替代指标的城市道路交通安全高风险点位研判方法

针对城市道路开展高风险点位的判别和筛查,是交通安全改善工作的重要内容。以上海市为例,基于手机导航定位数据,提取了急减速事件、急加速事件、平均速度、速度变异系数和拥堵指数五种替代安全指标,进而分别构建了基于零截尾负二项的事故频率预测模型和基于多元logit的事故严重程度预测模型,可有效识别出城市道路交通高风险点位。模型具备较高的准确率和可解释性。该方法可为精细化的城市道路交通安全治理提供有效的技术支撑。

中美两国道路交通事故信息采集技术比较研究

为借鉴美国事故信息采集标准和经验,结合我国事故数据采集的实际情况,指出当前国内事故信息采集存在的不足,提出改善建议。在详细总结美国在事故信息采集标准制定、事故信息采集技术方法的基础上,详细调查上海市部分交警支队事故现场数据采集的实际情况,对比分析得出我国在事故信息采集上存在的不足。事故信息标准化进程缓慢和信息采集技术落后是目前面临的主要问题。参照美国标准和经验并结合事故发生实际情况,对事故关键信息(事故地点、事故形态、事故原因等)的编码优化和先进采集技术(电子采集表、芯片阅读器、现场图绘制等)在我国的应用进行分析。

数字滤波技术在汽车碰撞试验中的应用研究

为了使开发的汽车碰撞试验系统的数据处理结果满足CMVDR294法规要求,研究了汽车碰撞试验数据处理过程中的关键技术.根据汽车碰撞试验的具体要求改进了数字滤波器的设计过程,并应用Matlab进行了不同频率的幅频特性仿真分析,提出了一种能够简化设计过程、提高计算精确性的数字滤波器设计方法,并根据该方法在LabVIEW平台上开发了满足法规要求的数字滤波器.同时,通过对大量的碰撞试验数据进行分析处理,验证了该数字滤波器能够有效屏蔽原始数据中的噪声与高频分量,幅值不失真.

中国高速公路事故特点及分布规律研究

中国的高速公路建设现在正处于快速发展时期,其事故发生率与事故严重程度与国外高速公路相比均高出很多,可以预见的是高速公路的安全形势将随着经济和社会的发展,以及汽车保有量和高速公路通车里程的增加,变得越来越严峻。在大量调研高速公路事故数据的基础上,利用统计分析的方法,研究讨论了我国高速公路交通事故的基本特点及分布规律,得出了一系列有价值的结论。研究结果可以为高速公路的交通安全管理与控制,以及交通事故的预测和预防提供良好的参考和借鉴。

汽车碰撞试验测控系统的开发研究

介绍了利用虚拟仪器技术在LabVIEW平台上开发研制的汽车碰撞试验测控系统,分析了开发中遇到的采样数据丢失问题产生的原因,利用C++与汇编语言嵌套程序和LabVIEW中的子函数调用等方法成功地解决了该问题,并在试验系统中得到了验证。

基于视频数据的城市隧道交通运行特征与安全研究

为降低城市隧道交通事故率,提高车辆出行安全,以上海市翔殷路跨江隧道为研究对象,利用视频采集技术、车辆跟踪调查采集隧道交通运行数据,分析城市隧道交通运行特征及驾驶员在隧道内驾驶行为特征,并进一步研究其与隧道交通事故的关系。对隧道内车辆的速度、密度分布,变道行为进行分析,发现隧道南线入口处内外断面平均车速的显著差异可以解释该处追尾事故高发,隧道内车辆频繁变换车道导致隧道侧碰事故占较大比例。为改善城市隧道交通安全,在隧道内进行限速和限制车辆变道是必要的。

汽车碰撞试验数据处理研究

论述汽车碰撞试验数据处理的关键技术——满足设计规则 CMVDR2 94的数字滤波器的设计原理 ,并应用

浅析BIM在工程造价管理中的应用

建筑工程造价计价模式经过几十年的发展,计价管理方式得到不断地完善,但工程造价管理信息化、精细化的程度不高、水平比较低,对建筑行业工程造价管理的发展有影响,BIM技术在数据共享、高效率调取给工程造价及管理带来应用价值,为工程造价管理提供有效平台.

正面碰撞中基于中国人体尺寸的假人损伤响应研究

为探究中国乘员在汽车正面碰撞中的损伤响应,基于中国人体尺寸和现有混Ⅲ 50百分位假人有限元模型,采用全体段缩放方法建立了符合中国人体尺寸特征的中国人50百分位男性假人有限元模型。采用LS-DYNA建立并验证了某量产车型的驾驶员侧约束系统模型,对比分析了中国人50百分位男性假人与混Ⅲ 50百分位假人在同一正面碰撞工况下的损伤响应,并研究了安全气囊的起爆时间、排气孔直径和气体质量流率3个参数对两种假人的损伤影响。结果表明,与混Ⅲ 50百分位男性假人相比,中国50百分位男性假人的头部损伤风险较大,而胸部损伤可能较小;对于混Ⅲ 50百分位男性假人是最优的安全气囊参数未必能给对应的中国假人提供最优的保护,部分安全气囊参数对两种假人损伤的影响趋势甚至是相反的。

安全持久性内存存储研究综述

在计算机技术和互联网技术飞速发展的进程中,计算机安全防护和数据机密性保护一直是学术界和工业界关注的焦点.主流的内存安全机制通过提供硬件辅助的机密性与完整性验证,确保选定的代码在运行时的内存可信,达到数据保护、防止泄漏和遭篡改的目的.新型持久性内存可像DRAM一样放置在内存总线上,通过处理器load和store指令进行访问,此外,持久性内存能够提供大容量和数据持久性支持,具有高带宽和低延迟的数据访问特性.然而,由于介质特性上的差异,面向DRAM的内存安全机制无法在持久性内存上高效运行,甚至存在可用性问题.因此,构建基于持久性内存的安全内存存储系统将为大数据的安全高效存储带来新的机遇.首先,针对持久性内存的写特性,分析了将面向传统易失内存的安全防护措施应用于持久性内存会引起额外开销的原因,并介绍相关降低开销的研究工作.其次,针对持久性内存的非易失性,分析了为保障持久性内存在其生命周期内的安全性所面临的问题与挑战,并介绍了数据及其安全元数据的一致性管理相关研究工作.最后,总结了构建面向持久性内存的安全存储系统面临的挑战,对相关工作进行综合比较,并提出下一步研究展望.

基于数据段优先级分区重装策略

提出一种基于数据段优先级分区重装策略PRS-DSP,其考虑数据特征及与之相关的事务特点,根据数据段优先级对数据库进行分区,并为每个分区设置相应重装频率,故障恢复时按照数据分区的重装频率来分区重装数据库,系统恢复服务后,根据新事务对数据的请求及数据分区重装频率来设置剩余分区的重装优先级。模拟实验结果表明,该分区重装策略降低了系统事务超截止期比率,其重装性能明显优于完全重装策略。