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.

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.

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.

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.

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.

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.

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.

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。

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.

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.

Complementary parametric probit regression and nonparametric classi?cation tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weatherrelated crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

Analyzing Crash-Prone Drivers in Multiple Crashes for Better Safety Educational and Enforcement Strategies

Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their over-involvement in crashes presents a big adverse effect on highway safety. By analyzing seven years of crash data from Louisiana, this paper investigates crash-prone drivers’ characteristics and estimates their risk to have crashes in the seventh year based on these drivers’ crash history of the past six years. The analysis results show that quite a few drivers repeatedly had crashes;seven drivers had 13 crashes in seven years;and the maximum number of crashes occurring in a single year to a single driver is eight. The probability of having crash(es) in any given year is closely related to a driver’s crash history: less than 4% for drivers with no crash in the previous six years;and slightly higher than 30% for drivers with nine or more crashes in the previous six years. Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

Driver Sleepiness and Risk of Car Crashes in Shenyang, a Chinese Northeastern City: Population-based Case-control Study

Objective To estimate the association of driver sleepiness with the risk of car crashes. Methods A population-based case-control study was conducted in Shenyang, a northeastern city in China, between November 2001 and July 2002. The case group comprised 406 car drivers involved in crashes, and 438 car drivers recruited at randomly selected sites, and on the day of week, and the time of day when they were driving on highways in the study region during the study period were used as control groups. Face-to-face interviews with drivers were conducted according to a well-structured questionnaire covering the circumstances of their current trip and their background information. Stanford sleepiness scale and Epworth sleepiness scale were used to quantify acute sleepiness and chronic sleepiness respectively. Results There was a strong association between chronic sleepiness and the risk of car crash. Significantly increased risk of crash was associated with drivers who identified themselves as sleepy (Epworth sleepiness score≥10 vs <10; adjusted odds ratio 2.07, 95% confidence interval 1.30 to 3.29), but no increased risk was associated with measures of acute sleepiness. Conclusions Chronic sleepiness in car drivers significantly increases the risk of car crash. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy.

Crash Severity Analysis of Single Vehicle Run-off-Road Crashes

Run-off-road crashes in the United States have become a major cause of serious injuries and fatalities. A significant portion of run-off-road crashes are single vehicle crashes that occur due to collisions with fixed objects and overturning. These crashes typically tend to be more severe than other types of crashes. Single vehicle run-off-road crashes that occurred between 2004 and 2008 were extracted from Kansas Accident Reporting System (KARS) database to identify the important factors that affected their severity. Different driver, vehicle, road, crash, and environment related factors that influence crash severity are identified by using binary logit models. Three models were developed to take different levels of crash severity as the response variables. The first model taking fatal or incapacitating crashes as the response variable seems to better fit the data than the other two developed models. The variables that were found to increase the probability of run-off-road crash severity are driver related factors such as driver ejection, being an older driver, alcohol involvement, license state, driver being at fault, medical condition of the driver;road related factors such as speed, asphalt road surface, dry road condition;time related factors such as crashes occurring between 6 pm and midnight;environment related factors such as daylight;vehicle related factors such as being an SUV, motorcycles, vehicle getting destroyed or disabled, vehicle maneuver being straight or passing;and fixed object types such as trees and ditches.

Understanding the Contributing Factors to Nighttime Crashes at Freeway Mainline Segments

This study investigated the crash contributing factors to the injury outcomes and the characteristics of the night time crashes at freeway mainline segments. Multinomial logit model (MNL) was selected to estimate the explanatory variables at a 95% confidence level. The six-year crash data (2005-2010) were obtained in the State of Florida, USA and five injury level outcomes, no injury, possible injury, non-incapacitating injury, capacitating injury, and fatal injury, were considered. The no injury level was selected as the baseline category.

Motor-vehicle crashes during pregnancy: a retrospective cohort study

Background: The purpose of this study was to assess the risk of motor-vehicle pregnant driver crashes in Pennsylvania using vital statistics linked to police and ambulance reports. This was supplemented with a review of national age and sex specific crash and fertility data to put this risk into perspective and rank the likelihood for pregnancy-related crashes in other states. Methods: Motor vehicle police crash reports from the Pennsylvania Department of Transportation were probabilistically linked to four years of birth and fetal death data and five years of infant death records and ambulance reports. State specific motor-vehicle traffic injury rates (fatal and non-fatal) were compared to birth rates, by age, for women ages 15 - 34. Results: 5929 (1.1%) of the women with a birth or fetal death linked to a police reported motor vehicle driver crash during pregnancy. One-third (32.5%) of these crashes resulted in minor maternal injuries and 7.5% resulted in moderate to fatal maternal injuries. Crashes were evenly distributed across gestational ages. Young drivers (20 - 24) were at highest risk. Police reported non-belt use was 10%. Conclusions: This study quantifies the risk of motor vehicle crashes during pregnancy in Pennsylvania and offers a perspective on potential variations in other states. Pregnancy related crashes occur at a higher rate than infant related crashes with a concomitant threat to the fetus and new-born not usually tracked within current crash data systems.

Comparative Analysis of Fatal Pedestrian Crashes between Kansas and USA

Due to the increasing trend of population growth and urbanization, pedestrians form one of the largest single road user groups. However, they are the most neglected group among all road users. Pedestrian safety is now a growing concern in the USA. Identifying the factors associated with fatal pedestrian crashes plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This study addresses safety issues by identifying contributory factors associated with fatal pedestrian crashes in Kansas and the USA. For Kansas, the study uses KARS (Kansas Accident Reporting System) database while for the USA FARS (Fatality Analysis Reporting System) database has been used. Different variables considered in this study are human variables (age, and gender), environmental variables (atmospheric condition and light condition), time (time of day, day of week, and crash month), location (intersection vs. mid-block), and roadway variables (speed limit). Different factors that are found to have an association with fatal pedestrian crashes are male pedestrians, older pedestrians, weekend, off peak hours, winter months, dark hours, non-intersection, clear atmospheric conditions, higher speed limit. The findings from Kansas have been compared with that from the USA. This study helps to implement potential countermeasures by identifying the factors that have an association with fatal pedestrian crashes.