Examining the Risk Factors of Rear-End Crashes at Signalized Intersections

Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.

Real-time rear-end crash potential prediction on freeways

This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.

对象驱动的Linux内核crash分类技术研究

Crash(程序崩溃)分析是漏洞挖掘与利用的关键阶段,精准的crash分类是crash分析和漏洞利用的前提.针对现有的Linux内核crash存在大量重复的问题,本文提出一种对象驱动的Linux内核crash分类方法.该方法将内核crash与内核对象的关系建模为二部图结构,从而将crash分类问题转化为内核对象的相似性对比问题.首先,通过对crash执行后向污点分析提取crash相关的内核对象;其次,构造内核对象调用图计算内核与根本原因的相关性度量;最后,基于上述结果构造二部图实现crash相似性比较算法.基于上述方法,本文开发出了Linux内核crash分类的原型系统.通过在真实的数据集上进行实验,验证了系统的有效性和可用性,弥补了现有分类方法粒度较粗,存在误报较多的问题.

Injuries Associated with Auto-Tricycle Crashes in an African City: Incidence and Pattern

Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.

基于PC-Crash软件的多车碰撞事故再现研究

近三年来,全国交通事故的年均发生数量在20万次以上,多车碰撞事故的数量也逐年攀升。文章根据车辆碰撞时间的先后顺序将一则复杂的交通碰撞事故,等效成多个简化的碰撞事故,且每个碰撞过程只包含两个事故参与方。通过对事故现场中驾驶员进行损伤验证,以及事故车辆进行痕迹勘验,分析人体损伤程度、各个车辆的碰撞形式和碰撞前的运动速度,运用PC-Crash软件进行事故仿真并验证其真实性。结果表明,仿真得到的人体损伤情况与实际情况相符;事故参与方的最终停止位置、碰撞速度与实际勘验数据相比,误差均小于10%,说明仿真结果与实际勘验的结果相吻合。此方法对复杂的交通碰撞事故能够进行模拟,并且仿真具有一定的真实性,为交通管理部门进行事故责任认定提供了理论支撑。

A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

IMPACT和CRASH模型对创伤性颅脑损伤患者预后评估价值的比较研究

背景国际颅脑损伤预后临床测试研究(IMPACT)和重型颅脑损伤后皮质类固醇的随机化研究(CRASH模型是国际上具有影响力的创伤性颅脑损伤(TBI)预后预测模型,需要继续开发,完善和持续的外部验证,以确保对不同环境的普适性。目的同时在中国TBI人群中进行验证IMPACT和CRASH模型的预后评估价值并进行比较。方法选取2017—2019年在郑州大学附属郑州中心医院内接受治疗的TBI患者210例为研究对象,收集纳入患者的基本信息。随访观察患者14 d存活情况和6个月格拉斯哥预后评分(GOS),随访截止时间为2021年6月,终止事件为中途失访。绘制受试者工作特征曲线(ROC曲线)评估IMPACT和CRASH模型对TBI患者预后的预测效能,计算ROC曲线下面积(AUC)。采用Brier评分评价模型的校准度。结果患者平均年龄(54.0±17.4)岁,分别绘制IMPACT模型与CRASH模型预测TBI患者预后的ROC曲线,结果显示IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月GOS预后不良的AUC分别为0.807(95%CI=0.747~0.866,P<0.001)、0.843(95%CI=0.789~0.897,P<0.001)、0.845(95%CI=0.793~0.897,P<0.001),Brier评分分别为0.179、0.164、0.161;IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月死亡的AUC分别为0.868(95%CI=0.816~0.919,P<0.001)、0.896(95%CI=0.851~0.941,P<0.001)、0.892(95%CI=0.850~0.935,P<0.001),Brier评分分别为0.151、0.144、0.136。CRASH基本模型、CT模型预测TBI患者6个月GOS预后不良的AUC分别为0.747(95%CI=0.682~0.813,P<0.001)、0.766(95%CI=0.703~0.829,P<0.001),Brier评分分别为0.306、0.308;CRASH基本模型、CT模型预测TBI患者14 d死亡的AUC分别为0.791(95%CI=0.723~0.860,P<0.001)、0.797(95%CI=0.728~0.865,P<0.001);Brier评分分别为0.348、0.383。结论对于TBI患者的预后,IMPACT模型整体较CRASH模型显示出较好的预测能力。

An In-Depth Analysis of Road Fatal Crash Patterns and Discussions in Ho Chi Minh City

Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China

Purpose： Rear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle （W）. Methods： This paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. More- over, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. Results： The results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers＇ age, weight difference between vehicles, visibility condition and lane number of road signifi- cantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. Conclusion： All the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers＇ age and drivers＇ original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beiiing on weekdays.

基于PC-Crash的载人两轮车事故再现仿真

为开发车-载人两轮车事故再现仿真案例,提出事故现场重建、事故参与者建模、仿真再现及结果分析的事故再现流程。通过1例车-载人两轮车事故演示与验证所提流程,发现通过该流程既能解释路面痕迹,又能解释车体变形及人体损伤痕迹,通过简单且可控的方式确保仿真痕迹与事故痕迹最大限度的一致,保证再现结果的客观、可靠。该研究对相关人员开展车-载人两轮车事故的再现及仿真分析工作具有借鉴作用。

Correlating Connected Vehicle Estimated Deceleration Events with Crash Incidents near Interstate Interchanges

Historical roadway safety analyses have used labor and time-intensive crash data collection procedures. However, crash reporting is often delayed and crash locations are reported with varying levels of spatial accuracy and detail. Recent advances in connected vehicle (CV) data provide an opportunity for stakeholders to proactively identify areas of safety concerns in near-real time with high spatial precision. Public and private sector stakeholders including automotive original equipment manufacturers (OEM) and insurance providers may independently define acceleration thresholds for reporting unsafe driver behavior. Although some OEMs have provided fixed threshold hard-braking event data for a number of years, this varies by OEM and there is no published literature on the best thresholds to use for identifying emerging safety issues. This research proposes a methodology to estimate deceleration events from raw CV trajectory data at varying thresholds that can be scaled to any CV. The estimated deceleration events and crash incident records around 629 interstate exits in Indiana were analyzed for a three-month period from March 1-May 31, 2023. Nearly 20 million estimated deceleration events and 4800 crash records were spatially joined to a 2-mile search radius around each exit ramp. Results showed that deceleration events between -0.5 g and -0.4 g had the highest correlation with an R2 of 0.69. This study also identifies the top 20 interstate exit locations with highest deceleration events. The framework presented in this study enables agencies and transportation professionals to perform safety evaluations on raw trajectory data without the need to integrate external data sources.

Application of new generation low-strength hot stamping steel to improve the crash performance of BIW

With the improvement of safety performance,car parts have different requirements for material strength and energy absorption performance.The conventional 1500-MPa hot stamping steel cannot well meet the requirements.Considering the new generation 600-MPa hot stamping steel,this study investigates the applicable car parts and hot stamping process,then designs a new body-in-white(BIW)crash test for obtaining the crash performance of the new material.Through the actual part development and crash test,it is verified that the application of the new generation hot stamping steel can improve the crash performance of BIW.

Application of LiDAR Data for Deep Learning Based Near Crash Prediction at Signalized Intersection

Near crash events are often regarded as an excellent surrogate measure for traffic safety research because they include abrupt changes in vehicle kinematics that can lead to deadly accident scenarios. In this paper, we introduced machine learning and deep learning algorithms for predicting near crash events using LiDAR data at a signalized intersection. To predict a near crash occurrence, we used essential vehicle kinematic variables such as lateral and longitudinal velocity, yaw, tracking status of LiDAR, etc. A deep learning hybrid model Convolutional Gated Recurrent Neural Network (CNN + GRU) was introduced, and comparative performances were evaluated with multiple machine learning classification models such as Logistic Regression, K Nearest Neighbor, Decision Tree, Random Forest, Adaptive Boost, and deep learning models like Long Short-Term Memory (LSTM). As vehicle kinematics changes occur after sudden brake, we considered average deceleration and kinematic energy drop as thresholds to identify near crashes after vehicle braking time . We looked at the next 3 seconds of this braking time as our prediction horizon. All models work best in the next 1-second prediction horizon to braking time. The results also reveal that our hybrid model gathers the greatest near crash information while working flawlessly. In comparison to existing models for near crash prediction, our hybrid Convolutional Gated Recurrent Neural Network model has 100% recall, 100% precision, and 100% F1-score: accurately capturing all near crashes. This prediction performance outperforms previous baseline models in forecasting near crash events and provides opportunities for improving traffic safety via Intelligent Transportation Systems (ITS).

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.

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。

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

基于碰撞特性的鲜食玉米摘穗装置对比试验

【目的】探究缓冲弹簧和柔性触穗表面两种摘穗机构在摘取果穗中的低损作用及机制。【方法】以传统玉米收获用板式摘穗机构和具有缓冲弹簧加柔性触穗表面的柔性摘穗机构为2种摘穗结构,以刚性触穗表面、柔性橡胶触穗表面、柔性发泡材料触穗表面为3种触穗表面,得出6种摘穗机构,进行鲜食玉米果穗碰撞试验,采集和分析碰撞时间、碰撞加速度峰值,计算碰撞冲量,统计籽粒破碎率。【结果】柔性摘穗机构能够有效降低鲜食玉米果穗受到的碰撞时间,从而降低鲜食玉米果穗的籽粒破碎率。缓冲弹簧可缩短鲜食玉米果穗的碰撞时间,当柔性摘穗机构表面为柔性橡胶和柔性发泡材料时,碰撞时间分别比板式摘穗机构降低了66.2%、70.1%和70.1%。碰撞冲量分别降低了67.6%、77.9%和79.7%。籽粒破碎率降低了71.2%、51.3%和83.6%。对于柔性摘穗机构,鲜食玉米果穗与柔性橡胶、柔性发泡材料触穗表面的碰撞时间比刚性触穗表面分别延长了58.7%和63.2%。碰撞加速度峰值分别降低了62.5%和68.7%。碰撞冲量分别降低了42.4%和54.5%。籽粒破碎率分别降低了47.4%和68.4%。【结论】通过降低收获过程中鲜食玉米果穗受到的碰撞时间和碰撞加速度,从而有效降低了鲜食玉米果穗的籽粒破碎率,为鲜食玉米低损收获机的研制提供理论依据和设计参考。

Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

Estimating the severity levels of road traffic crashes in Bahrain with crash costs estimated with different approaches

An important issue in analyzing accident blackspots is the estimation of severity levels of different types of accidents.This study aims to estimate the severity level of accidents in Bahrain using crash costs.These crash costs were calculated by the Human Capital Approach(HCA)and total reported costs from the victims.The data was collected from the General Directorate of Traffic,insurance companies,Ministry of Works(MoW)and Ministry of Health.It was found,from the survey responses,that there was no significant effect of victim characteristics on the total cost of the accidents.The severity levels were found to be higher than those found in previous literature or adopted by local authorities which could be attributed to the economic conditions of Bahrain.Moreover,the weights found by both approaches were different from each other.Therefore,it is recommended to use the HCA approach due to its comprehensive calculations involving future costs.

复合材料在汽车防撞梁上的应用研究进展

为促进复合材料在汽车构件中的应用,以汽车保险杠防撞梁为例,对复合材料防撞梁的材料、结构设计以及保险杠内部零件连接的研究进展进行综述。简述了碳纤维、玻璃纤维以及天然纤维等复合材料在汽车防撞梁上的研究进展,介绍了不同结构复合材料防撞梁对于耐撞性能的影响,分析了保险杠内部零件不同连接方式的优缺点。最后提出了汽车复合材料防撞梁的未来发展方向,应注重新型复合材料的开发、防撞梁结构的优化设计以及新型连接方式的研究。