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.

Mapping Road Traffic Crashes in Sfax Governorate (Tunisia)

Sfax is one of the Tunisian governorates with a large number of road accidents, injuries and fatalities every year. This study aimed to analyze and map traffic accidents in this governorate. We analyzed the spatial distribution of accidents, their distribution by cause, by type of road, by size of traffic, by months of the year and days of the week. Accidents were correlated with several variables such as population numbers and densities, motorization rate, length and structure of the road network, and the amount of traffic. On the cartographic level, we have built a database, through which we have produced a series of thematic maps to argue this analysis. Through cartographic production, we also aimed to help road users, decision-makers and researchers in this area and in the field of transport. This work showed that Sfax occupies, among the other Tunisian governorates, an advanced position in gravity. Various human, climatic and technical factors explained this situation, of which human factors were the most important, and contributed to almost 90% of accidents. The current situation of accidents in Sfax requires a series of measures and actions to alleviate and mitigate the gravity of this phenomenon.

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.

Exploring injury severity of children and adolescents involved in traffic crashes in Greece

For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece,disaggregate crash data including 13,431 involving children and adolescents from all regions of Greece for the period 2006–2015 were utilized.In order to identify factors affecting injury severity and account for potential unobserved heterogeneity,a series of mixed logit models were utilized.To explore and address potential temporal instability of crash-related risk factors,the likelihood ratio test was applied.Results indicated that night crashes,crashes outside urban areas as well as crashes involving bicycles or powered-two-wheelers are associated with higher injury severity of children and adolescents.Interestingly,crashes involving pedestrians are associated with lower injury severity than head-on collisions and run-off-road collisions with fixed objects.Side and sideswipe crashes also result in lower injury severities.The likelihood ratio test indicated that crash-related factors are instable when comparing the models utilizing data before and after 2010 respectively.This study contributes to the current knowledge in the field,as to the best of our knowledge this is the first study that addresses unobserved heterogeneity when analyzing child and adolescent injury severity.Overall,the findings of this study provide useful insights and could assist in unveiling crash risk factors and prioritize programs and measures to promote road safety of children and adolescents.

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.

考虑空间溢出效应的高速公路交通事故随机参数频次模型

为提高高速公路事故频次模型参数估计的准确性,本文以精细的事故记录、道路属性、交通流状态和天气条件数据为样本,首先,构建传统的事故频次模型并对比拟合效果,选择效果最佳的泊松-对数正态分布模型作为优化的基础模型;之后,为挖掘更多的影响高速公路事故频次的空间效应,在加入条件自回归先验的基础上,本文考虑相邻路段的空间溢出效应,建立一个带有空间溢出协变量的模型分析空间溢出效应对路段事故频次的影响。同时,为描绘数据的异质性对路段事故频次的影响,进一步构建一个随机参数模型。结果表明,空间溢出效应显著有效,考虑空间自相关和空间溢出效应的随机参数模型的拟合优度相较于对照模型明显提升。根据最优模型的参数估计结果进行风险因素甄别,其中,“ln(MADT)”“ln(路段长度)”“1类车”“4类车”“降水”等普通变量以及“1类车S“”圆曲线长S”等空间溢出协变量与事故频次显著相关。

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。

基于Pc-crash的交通事故再现误差分析

对交通事故再现软件Pc-crash中的特征参数进行分析,设计具普遍意义的车-车斜撞模型,运用分析法、控制变量法先提取较主要的参数;再设定正交试验将这些较重要参数按权重大小排序.基于对特征参数的研究,探求这些重要参数对模拟结果影响的权重及产生影响的类型,以此寻求减小事故再现误差的方法,并用国内一起典型的车-两轮车交通事故验证结论的正确性,为提高Pc-crasch软件再现国内事故精度的研究建立基础.

基于Pc-Crash的车辆侧滑事故再现方法

为准确再现车辆侧滑事故,基于Pc-Crash软件,提出一种分步再现事故的方法。该方法首先仿真事故车辆侧滑运动,获得侧滑车辆碰撞前瞬间运动状态参数;然后定义新仿真以再现侧滑车辆与事故其他参与者的碰撞;重复此2步骤,以确保仿真中痕迹与事故中痕迹最相符合。最后就一例车辆侧滑后与过往车辆发生碰撞的事故,演示该分步再现方法各步骤的实现途径。结果显示,利用Pc-Crash软件分步再现车辆侧滑事故,能较为容易地控制再现过程,保证仿真中痕迹与现实情况的一致性。

基于Pc-Crash软件的人-车碰撞事故仿真规律研究

以Pc-Crash软件为平台,仿真分析两例实际的人-车碰撞试验。通过对比仿真结果与试验的吻合情况,并结合其他学者所提及的现象,总结了仿真中人-车接触部位、行人终止点位置及抛距、行人被车辆抛出后的运动情况等与实际情况的耦合程度的规律。发现利用Pc-Crash软件实现对人-车碰撞事故仿真时,人-车接触部位与实际情况可以耦合得很好,行人终止点位置一般总体抛距与实际情况差不多,而行人被车辆抛出后的运动情况则与实际不符,针对这些现象提出一些建议。对更好地利用Pc-Crash软件实现对人-车碰撞事故的仿真分析有一定的意义。

基于PC-Crash的车-人碰撞事故仿真与分析

基于多刚体动力学仿真分析软件PC-Crash,建立车-人碰撞事故的再现模型,分别对同一工况下汽车有无ABS刹车防抱死制动系统、不同碰撞初速度以及利用随机函数确定汽车与行人的随机参数,进行大量的数字化仿真试验,分析事故发生后人体头部重要器官与车体前部碰撞点以及损伤程度的分布规律。根据分析结果,从汽车生产厂商保护行人的角度改善车身布置结构,提出有价值的参考意见。

中国网约车服务对道路交通事故的影响研究

为深入探究网约车服务是否直接影响道路交通事故,以及是否通过改变公交车和私家车的使用量而间接影响道路交通事故。基于2005—2017年我国114个城市的面板数据,采用多期双重差分法(DID)和逐步法,研究网约车服务对道路交通事故的影响机制。结果表明:网约车服务进入城市导致道路交通事故增加5.6%,这一结论在进行平行趋势和倾向得分匹配(PSM)-DID等稳健性检验后仍然成立;网约车服务显著降低约19.3%公交使用量,从而间接增加0.71%道路交通事故,说明公交使用量发挥中介效应;网约车服务显著增加5%私家车使用量,从而间接增加0.14%道路交通事故,说明私家车使用量发挥中介效应。公交使用量的减少是网约车服务间接增加道路交通事故的重要原因。政府部门应当加强网约车服务的监管,同时,鼓励网约车服务与公共交通协同发展。

基于PC-Crash的车辆侧翻事故再现仿真实验

PC-Crash再现交通事故是根据交通现场勘查结果及事故调查结果,构建交通事故现场场景并导入模型,设定模型的参数和初始运动状态,对交通事故发生过程进行模拟演示,将演示结果与实际交通事故进行比较,分析是否存在差异并判断存在差异的原因,进一步调整模型的参数和初始运动状态,直至演示结果与实际事故高度吻合。在分析引发车辆侧翻事故各种可能因素的基础上,着重介绍了PC-Crash软件仿真车辆侧翻事故的基本步骤。通过交通事故实例,演示了PC-Crash软件变量参数的设置和调整方法。

基于PC-Crash的交通事故仿真实验设计

建立了PC-Crash软件进行交通事故仿真实验的步骤,用一例行人参与的人车碰撞事故进行了事故过程再现的研究。结果表明,结合事故中的车损情况、人伤分析和现场物证,所设计的交通事故仿真实验能达到交通事故再现的目的,且具有事故再现过程的直观可视性,可用于交通事故再现的本科实验教学课程。

基于PC-Crash的人车事故再现研究

文章对一起人车相撞的事故进行分析,建立了车辆模型、道路模型、行人模型,运用PC-CRASH软件进行了分析计算,结合事故勘查时的痕迹,行人的受伤情况,再现了事故发生时人车的接触部位和行驶行走方向,为更准确地进行人车交通事故鉴定提供了一种方法。

基于PC-Crash事故多发路段诱因分析

运用车辆运动模型、车辆碰撞模型碰撞过程机理进行分析,把事故发生的周边环境资料如路面状况、路面宽度、行人车辆等外界情况资料数据输入PC-Crash软件中,通过动画模拟还原事故发生过程,综合判定事故的责任划分。

基于多项式Logit模型的道路交叉口机非事故影响因素分析

为分析道路交叉口处机动车和非机动车交通事故的影响因素,将事故按照严重程度划分为无伤害事故、可能伤害事故、轻伤事故、重伤事故以及死亡事故,并提取了机动车驾驶员年龄及性别、非机动车驾驶员年龄及性别,道路线型、光照条件、限速条件等13个影响因素作为自变量,基于多项式Logit模型对事故进行了模型标定以及边际效应分析.结果表明:非机动车驾驶员的性别及年龄、非机动车所处的位置、事故发生地区域类型、光照条件、道路线型、限速条件7个变量与事故的严重程度存在显着相关.

Workers Survived from Commuting Road Crashes:Who Are They?

Commuting road crashes are road traffic crashes that involve workers while travelling in the course of work.The more worker travels,the higher the probability of the occurrence of commuting road crashes.The aim of this study was to determine baseline sociodemographic,employment and injury and characteristics of injured workers who survived from commuting road crash.Eligible 200 workers who were involved in commuting road crash were identified and invited to be part of this study.Sociodemographic,employment and injury-related questions were distributed to identified and consented injured workers.Majority(79.5%)of the respondents were aged 25 years old or older,male(86.0%),married or divorced(63.5%),and attained secondary and below education level at secondary or below(66.0%).Most of the injured workers consisted of blue-collar workers(69%),had fracture injury(93.0%),and had injury to their lower limbs(48.5%).A higher percentage(63.5%)of injured workers had returned to work compared to those who were still not working(36.5%)after involved in commuting road crash.Commuting road crashes are common to blue collar workers as they are more prone to use motorcycles to commute due to cheaper price compare to other type of transportation such as car.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

基于PC-CRASH的道路交通事故分析及再现研究

在分析研究交通事故案例的基础上,利用PC-CRASH软件对已经发生的车辆碰撞道路交通事故进行事故碰撞前状况、碰撞点、碰撞后形态的再现,从而确定碰撞行驶速度,分析事故发生的原因。根据推定得出的事故发生原因,实现快速确定事故责任方,协助交通事故鉴定部门处理好事故,减少事故处理部门的工作量。