Investigating the attitudes of Egyptian drivers toward traffic safety

One of the main pillars for improving road safety in any country is a good understanding of traffic safety culture and the driving behavior of local drivers. The primary aim of this study was to determine whether Egyptian drivers differ in traffic safety attitudes and level of acceptance of risky driving behavior. A questionnaire survey was conducted on the driving cognition of the participants. An exploratory factor analysis was used to assess the number of factors that differentiated the three types of drivers. Then a hierarchical cluster analysis was performed to group the drivers with similar patterns of scores on the factors into clusters. Three driver clusters emerged: The drivers in cluster 1 were “drivers who rigidly followed regulations” (51.7%). The drivers in cluster 2 were “drivers who violated safety precautions” (23.3%). The drivers in cluster 3 were “drivers who had a tendency to violate regulations” (25.0%). A similarity between the social norms and personal attitudes of drivers was found. This can be explained by the high social norm of violating traffic laws, which can lead to more drivers accepting violations. The majority of the older drivers and drivers with no violations or traffic accident on their record in the past 2 years were in cluster 1. Cluster 2 had the highest proportion of young drivers who wore their seat belts and used hands-free phones while driving. Cluster 3 drivers accepted very dangerous violations, such as texting while driving, driving while intoxicated, and driving at very high speeds. They reported significantly more traffic accidents, but no more violations than the other two clusters. The results of this study can be used to improve road safety programs for education and enforcement in Egypt.     

An international comparative study on driving attitudes and behaviors based on questionnaire surveys

Road traffic crashes (RTCs) are influenced by a driver's awareness and attitude toward road safety, as well as the socio-economic status, infrastructure development level, traffic status, social system, and traffic safety culture of the area to which the driver belongs. In this study, based on the results of a questionnaire survey conducted in seven countries, the characteristics of each country concerning tolerance for traffic violations, dangerous driving, and acceptance for road safety measures were studied. It was suggested that a high tolerance for traffic violations and dangerous driving might affect traffic violations and RTCs in each country. Additionally, to reduce the tolerance for traffic violations and dangerous driving, the promotion of road safety education, especially among young and male drivers, and stricter regulations and enforcement were suggested.     

基于轨迹数据的危险驾驶行为识别方法

连续的跟驰行为和换道行为是驾驶行为的主要构成部分,对交通拥挤和交通事故有着重要影响。通过无人机视频拍摄和图像处理方式,提取了曹安公路沿线的2个交叉路口间正常交通流状态下共600条多车高精度轨迹数据。首先,考虑车辆类型对驾驶行为产生直接的影响,分析了大车和小车的车辆轨迹特征变量分布的差异性,包括速度、加速度、碰撞时间倒数、车头时距等,在标记危险驾驶行为的过程中考虑车辆类型的影响。其次,针对不同的车辆类型,利用修正碰撞裕度对跟驰行为和换道行为进行风险性评估,将其划分为安全型和风险型。根据风险型行为发生的顺序以及持续时间,评估驾驶人的整体驾驶状态是否危险,作为危险驾驶行为识别的样本标记。分别利用离散小波变换和统计方法提取车辆轨迹的关键特征参数,为了提高模型识别效率,将关键特征参数进行排序,从而确定最优判别指标;最后,利用轻量梯度提升机（LGBM）算法对危险驾驶行为进行识别,并与随机森林、多层感知器、支持向量机等算法在精度上进行比较。研究结果表明：在上述研究条件下,LGBM算法对危险驾驶行为的理论识别率最高可达93.62%,可以实现基于机器学习算法的危险驾驶行为的高精度自动识别,该结果对于智能驾驶辅助系统的设计、道路交通安全决策的制定具有显著的意义。     

基于OBD数据的危险驾驶行为与建成环境空间关联研究

随着宽带移动通信、物联网等新一代信息技术在交通领域中的应用,面向交通安全的移动互联环境下驾驶行为研究成为热点课题.为弥补现有研究中对车联网数据分析较少或对危险驾驶行为空间分析不足等缺陷,基于车辆自诊断系统(OBD)数据对危险驾驶行为进行了空间识别与提取,并基于交通小区(TAZ)分析了危险驾驶行为的空间分布差异.研究揭示了危险驾驶行为空间分布差异的内在机理,利用百度兴趣点(POI)数据度量了城市建成环境因素,通过最小二乘法(OLS)回归模型识别出建成环境对危险驾驶行为显著影响的变量,在此基础上采用基于地理加权回归(GWR)模型得出了不同建成环境变量对危险驾驶行为的空间影响系数.模型显示,采用GWR模型拟合结果优于OLS一倍,并且可以有效地揭示出空间建成环境对危险驾驶行为影响的时空特征,为交通管理与规划部门制定措施或政策提供了决策支持.      

高速公路车辆典型交通违法行为危险度分级预警方法

高速公路不文明驾驶行为及交通违法行为的频发严重影响了高速公路交通流运行的安全与效率.基于采集的高速公路交通安全相关数据,对高速公路典型交通违法行为的危险度的分级预警方法进行了研究.针对高速公路的交通事故数据,以从恶劣天气、道路线形、违法类型的角度分析了道路交通事故与各类因素的相关性,在此基础上确定进行分级预警的影响因素.综合考虑在气象条件、交通状况、道路线形及各类违法行为,提出了层次分析与模糊评价相结合的方法,建立了针对高速公路违法行为危险性的分级预警模型,并用实际案例数据对模型的适用性进行了验证.结果显示,模型具有良好的适用性,所建立的分级预警模型可以为高速公路的主动交通预警及管理提供参考与支撑.      

International analysis on social and personal determinants of traffic violations and accidents employing logistic regression with elastic net regularization

While the number of road fatalities is declining in developed countries, it is still increasing globally, especially in middle-and low-income countries. In addition to the driver's individual awareness and attitude toward traffic safety, various factors such as the development of road infrastructure and the legal system may have a significant influence on the occurrence of traffic accidents. Thus, it is essential to consider these factors to enhance traffic safety and achieve Sustainable Development Goal (SDG) Targets of 3.6.In this study, we developed an Elastic Net Regression model to evaluate the factors that influence an individual's traffic violations and accidents based on an international questionnaire on traffic safety attitude, country fact survey data on traffic regulations, and other statistical databases. As a result, it was revealed that: i) In addition to country-level factors, the individual's attributes and attitudes toward traffic safety have an influence on the experience of traffic violations and accidents. and ii) While the same variables regarding individual attributes and attitudes are selected for both traffic violations and accidents, the selected variables relating to country factors differ between violations and accidents.     

北京市驾驶行为与影响因素分析

研究北京市驾驶行为的特征及其影响因素。对曼彻斯特驾驶行为问卷进行修订,考虑多种因素对驾驶行为的影响,对135名北京市汽车驾驶者进行问卷调查。经因子分析,得到5个驾驶行为因子分别是＂错误＂、＂失误＂、＂非故意违规＂、＂故意违规＂和＂过激行为＂,符合预期的因子结构。对驾驶行为的各类影响因素进行方差分析,结果表明与驾驶行为紧密相关的影响因素主要是驾驶者的自我评价和态度,如驾驶者对自己是否易受情绪影响的评价和对交通法规的态度等。驾驶培训经历,包括驾驶培训的地点和驾驶培训的时间跨度,也对驾驶行为存在一定影响。性别、年龄等其他因素对驾驶行为的影响相对较小。与西方男家相比,中国汽车化历史较短,驾驶行为具有不同的特点。     

Behavioral aspects of safety culture: Identification of critical safety-related behaviors of motorcyclists in Indonesia's urban areas via the application of behavioral-based safety programs

Many violations and accidents involving motorcyclists occur in the urban areas of Indonesia. It can be said that the failure to develop a good traffic safety culture causes poor motorcyclist behavior, as shaped by existing programs and mechanisms. This study aimed to identify motorcyclists' critical behaviors by conducting investigations using the reciprocal safety culture model as a framework. We tried to identify and clarify the safe behaviors expected by local governments from the existing driving safety program. By applying the antecedent-behavior-consequence model of the behavioral-based safety program, we obtained sixty-three behaviors associated with the six criteria of safe driving. We surveyed motorcyclists (N = 97) to review the sixty-three motorcyclist behaviors in the urban area. The relationship between the behavioral and psychological aspects of the reciprocal safety culture model was investigated to obtain the motorcyclists' critical behaviors. Multiple linear regression model, optimized by the stepwise regression, described the influence motorcyclist behavior on the perception of driving safety. We identified eight critical safety-related behaviors engaged in by motorcyclists. Observation revealed some cultural issues embedded in motorcyclists' eight critical safety-related behaviors that need to be intervened by the local government. The reciprocal safety culture model could be applied in the behavioral-based safety program to approach traffic safety culture issues. In order to develop a good traffic safety culture in the urban area, the local government needs to review the existing driving safety program by understanding drivers' behaviors as they relate to such a program.     

Traffic safety culture of professional drivers in the State of Qatar

Professional drivers play a significant role within the traffic system of the State of Qatar. With developing infrastructure, the need for professional drivers is growing. However, knowledge is lacking about their perception of traffic safety. Therefore, this study investigates the personal acceptance of risky driving and suggested traffic laws among this specific group of drivers, in order to create understanding about their likelihood to commit certain risky driving behaviors and their resistance to the implementation of certain traffic laws. The aim of this study is to establish which personal attributes of professional drivers in Qatar could influence a high likelihood to commit risky driving behaviors, estimating which specific groups of professional drivers impose the highest risk to violate certain traffic laws. Results indicate that transportation mode, origin and years of driving experience are all personal attributes that have a significant impact on the professional driver's risk to commit risky driving behaviors and their opposition to the implementation of related traffic laws. Distressing results have been found for the high likelihood to violate speed in school zones and the high risk to be distracted by any type of phone use while driving, suggesting the need to put emphasize on these safety hazards during the training programs of professional drivers at professional driving schools in the State of Qatar.     

汽车驾驶员模型的研究现状及发展趋势

汽车驾驶员模型是汽车交通安全、智能交通系统、汽车自动驾驶和车辆巡航等技术的基础研究内容和关键环节之一。按照汽车驾驶员模型的研究方向及应用,将驾驶员模型分为基于人—车—环境闭环系统汽车操纵稳定性的驾驶员模型、基于智能交通系统的驾驶员行为模型和基于交通安全的驾驶员疲劳模型等类型,综述了上述各类汽车驾驶员模型的研究现状,对各类驾驶员模型存在的不足进行了分析论述,并展望了汽车驾驶员模型的发展方向及趋势。     

基于simulator+技术的动态驾驶安全教育系统

交通事故频发已经严重影响到了人们的生活,并对社会经济造成了巨大的损失.提升驾驶员的驾驶能力能有效减少驾驶事故发生.聚焦典型违法驾驶行为,基于simulator实验平台开发的沉浸体验式教育系统(DSIES),通过系统教育纠正驾驶行为进而减少事故.通过典型违法驾驶行为致因分析,基于计划行为理论提出“知-教-行”动态教育系统.为验证系统有效性,选取具有代表性的4项违法驾驶行为作为实验对象,42名被试者被随机均分为2组进行传统教育及新型教育.采用描述性统计、显著性方差分析及灰度关联分析方法验证不同教育方式的效果.实验结果表明,新型教育系统能有效提升驾驶员驾驶能力;但随着时间的流逝,教育效果均有所降低.另一方面,该教育系统从短时教育及长时教育效果方面均优于传统教育,通过动态教育系统能有效提高驾驶员危险预测能力、降低交通违法行为.      

基于轨迹数据的交叉口相位切换期间危险驾驶行为实证分析

基于交叉口相位切换期间的车辆轨迹数据,分别根据单车和跟车行驶状态,识别和分析了相位切换期间可能发生的危险驾驶行为。通过视频拍摄和图像处理的方式,提取了曹安公路沿线3个交叉口共312条单车状态和四平路-大连路交叉口共449条跟车状态的高精度车辆轨迹数据。针对交叉口相位切换期间的危险驾驶行为特征,利用速度、加减速度、减速度变化率、潜在碰撞时间（TTC）等指标,研究在此期间车辆发生危险驾驶行为的特点和类型。对于单车状态下行驶的车辆,按停止、通过分类,依据减速度、减速度变化率、减速度变化率的峰值差等指标将停止车辆的危险驾驶行为分为紧急减速型、增强减速型和持续急减型,依据过停车线时间、速度、加速度等指标将通过车辆分为闯红灯型、超速过线型、激进加速型和持续高速型。对于在跟车状态下行驶的车辆,按前、后车不同的停止、通过决策组合分类,依据连续5个时间间隔（0.12 s）的TTC分析前、后车的危险驾驶行为及发生追尾事故的危险程度。针对识别出的危险驾驶行为类型,讨论车辆的关键行为参数与危险驾驶行为之间的内在关联。研究结果表明：单车状态下有17%的车辆存在危险驾驶行为,其中53%为紧急减速行为;跟车状态下有19%的跟车行为是危险的,其中停止车辆的比例是通过车辆的2倍以上。研究成果可进一步应用于驾驶行为模型的参数标定、基于车辆轨迹的交叉口安全评价以及预防危险驾驶行为的主动安全控制策略等。     

基于驾驶行为和信息熵的道路交通安全风险预估

为了精准有效地进行交通事故预防预警，基于车辆OBD驾驶行为数据及信息熵理论，提出了城市道路交通安全风险预估方法。首先，分析异常驾驶行为高发位置与道路交通事故发生位置的关联性；其次，构建以道路交通安全熵为一级指标，急加速率、急减速率、急转弯率、超速率、高速空挡滑行率为二级指标的道路交通安全风险预估指标体系，提出了基于改进熵权法的道路交通安全熵计算方法；然后，基于密度聚类、K-means聚类提出了道路交通安全风险等级数确定方法，并基于K-means聚类建立了风险等级阈值计算方法。研究结果表明：异常驾驶行为高发位置与交通事故发生位置具有一致性；通过对log对数底数选择优化、二级指标零值处理、指标权重分段计算3个步骤改进的熵权法，可弥补log对数函数无法计算零值指标熵值的缺陷，避免指标权重为负、指标熵值与权重反映信息不一致的现象；两步聚类避免了孤立数据点对安全风险等级划分的影响。以重庆市4条城市道路（总长约38 km）进行实例验证后得出，道路交通安全熵与交通事故表征的道路交通安全状态趋势一致；道路交通安全风险等级可划分为高、低风险2级，道路交通安全熵优化阈值为0.042，最后，风险等级划分准确率为87.88%。研究成果可为道路交通安全风险点辨识、交通事故预防预警提供有效的技术支持。     

Car drivers' road safety performance: A benchmark across 32 countries

The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure, or road users' behaviour. The last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology.This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties.Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter.The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.     

基于驾驶特性的超车模型研究

超车行驶作为驾驶人行车过程中重要的行为之一,与行驶安全性有着直接的联系。为建立符合驾驶人操作习惯的超车模型,本文通过实车试验采集不同驾驶人在高速公路的超车行驶数据,并以此采用多项式回归拟合建立基于驾驶人操作特性的超车模型,最后利用prescan软件对提出的超车模型进行了仿真分析,结果表明建立的超车模型能够真实地反映驾驶人超车过程中的操作习惯,为超车行为的研究提供了可靠的理论依据。     

面向安全预警的机动车驾驶意图研究现状与展望

简述当前开展面向安全预警的机动车驾驶意图研究的目的和意义.分析国内外研究现状,得出从驾驶员行为及驾驶动作序列角度开展驾驶意图研究的可行性和有效性,同时介绍了 2种基于概率与数理统计的机动车驾驶意图建模方法.结合驾驶员在直线封闭路段实施驾驶行为特征,阐述使用隐马尔科夫模型(HMM)理论建立驾驶意图模型的步骤以及模型参数学习和系统在线优化算法等内容.对驾驶意图模型网络结构、动态性能方面相关研究方向进行展望.     

基于驾驶模拟技术的道路行车安全性研究综述

对驾驶模拟技术在道路行车安全领域的研究及应用现状和存在的问题进行了分析。在广泛调研国内外相关文献的基础上,对驾驶模拟器进行了分类,并总结了国内外主要代表性科研型驾驶模拟器的发展历程,分析了典型驾驶模拟器的自由度、主要特征和应用领域。以“人-车-路-环境-事故”为主线,从不良驾驶行为特性分析、车辆主动安全技术研究、道路与交通设计、车辆驾驶环境以及道路行车事故研究5个方面,系统地梳理了驾驶模拟技术在国内外道路行车安全领域的应用研究现状、存在问题以及应用展望。在不良驾驶行为特性分析方面,重点研究了运用驾驶行为特性开展分心驾驶行为和疲劳驾驶行为的识别;在车辆主动安全技术研究方面,综述了运用驾驶行为开展车辆底盘一体化控制技术、安全辅助驾驶控制技术和自动驾驶接管行为的评价研究;在道路与交通设计方面,综述了道路几何和标志标线等的设计评价;在车辆驾驶环境方面,综述了不良气象、路侧景观和交通冲突等驾驶环境对驾驶行为的影响;在道路行车事故研究方面,总结了道路行车事故再现和事故影响因素分析等内容。此外,对驾驶模拟技术进行了应用展望,主要包括特殊人群的驾驶行为特性、智能网联汽车系统的测试及验证、混合交通流环境下的行车安全问题。对未来应对驾驶模拟器的有效性评价、不适性以及二次开发等问题进行探讨,以便更好地促进驾驶模拟技术的发展。      

驾驶模拟环境中激进驾驶行为的影响因素分析

为了分析驾驶人在驾驶模拟试验过程中出现的相对实际驾驶的激进驾驶行为的影响因素,采用计划行为理论构建心理认知模型。基于计划行为理论设计问卷调查私家车驾驶人对"在驾驶模拟过程中激进驾驶"行为的信念、态度、主观规范、行为感知控制、意向与行为。采用结构方程模型得到观察变量与基本构念以及基本构念内部的相关关系,并最终分析得到影响驾驶人激进驾驶行为的主要因素。通过先导性调查问卷以及正式调查问卷的投放,最终得到217个有效的样本。研究结果表明：心理认知模型具有良好的适配性,其卡方自由度比为1.802,RMSEA值为0.062;态度、行为感知控制是影响驾驶人行为的主要因素,主观规范对行为的影响相对较小;各信念与对应的态度、主观规范及行为感知控制之间存在显著关联,各信念的测量模型的适配性良好,卡方自由度比、RMSEA等指标基本满足要求。采用完整的计划行为理论结构同时从标准获取构念和自行获取构念的角度解释了驾驶人对"在驾驶模拟过程中激进驾驶"行为的心理认知,研究成果可用于驾驶模拟-自然驾驶行为数据差异性控制,驾驶模拟试验规范化方法构建。     

Traffic safety culture of drivers in Canada: Implications for new traffic law implementation to enhance traffic safety

This study investigates Canada's traffic safety culture (TSC) as part of a global research project. The TSC survey data collected by an online survey is used to predict drivers' perception of changes in traffic problems in the past 3 years, driver's support, opposition towards enforcement of additional traffic laws, and drivers' perceived threat towards risky driving behaviors. A two-step procedure is followed to build models. The first step includes feature selection using the chi-square test of independence. The second step comprises building classification models using the Random Forest technique. Results suggest that drivers' personal attributes like the number of accident records, driving frequency, geographic region of nationality, and religion are top predictor variables for drivers' perception towards changes in traffic problems. In addition, compared to others most drivers perceive distracted driving as a major traffic problem today. There is strong disapproval of drivers against the following driving behaviors and strong support to implement laws against it: speeding in school zones, talking on a hand-held cell phone, tying text messages or e-mails, drowsy driving, driving without wearing their seatbelt, drive with passengers not wearing seatbelts, running through red lights, and impaired driving. In contrast, following risky driving behaviors is less of a perceived threat: speeding over limit on a freeway, on a residential street, and in an urban area and talking on a hands-free cell phone while driving. In addition, a driver's accident record is a significant indicator for a perceived threat towards risky driving behaviors followed by age and accumulated demerit points. The results from this study can be used to guide educational campaigns to transform the traffic safety culture of target groups and make more informed policy decisions.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.