Impact of driving style,behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers.     

Validation of the Driver's Angry Thoughts Questionnaire (DATQ) in a sample of professional drivers in Iran

Aggressive driving has emerged as one of the most studied behaviors in the traffic safety field, due to its association with the odds of motor vehicle crashes and especially fatal crashes. Previous research has investigated the situations which provoke anger while driving, as well as the emotional (anger) and behavioral (aggression) aspects of aggressive driving. However, surprisingly the cognitive aspects of aggressive driving have largely been neglected. This study investigated the psychometric properties of the short-forms of the Driver's Angry Thoughts Questionnaire (DATQ) and the Driving Anger Expression Inventory (DAX) in a sample of professional drivers. Furthermore, the study aimed to investigate the mediation effects of aggressive thoughts, as the cognitive aspect of aggressive driving, on the relationship between traffic congestion and driving aggression. To this end, 613 public transport bus drivers completed the DATQ and DAX and were also asked to report the level of traffic congestion they normally faced in their daily driving, using six pictures. Confirmatory factor analysis (CFA) supported the four factor DAX and the five factor DATQ, which largely replicated the original factors. The four forms of maladaptive thoughts on the road were positively associated with aggressive driving, while the positive factor (coping self-instruction) was negatively associated with aggressive driving and traffic violations. Moreover, the results indicated that traffic congestion does not contribute directly to anger expression on the road, but rather through aggressive thoughts. This study suggests that cognitive interventions may help to eliminate aggressive driving and its adverse outcomes on traffic safety.     

自然驾驶状态下重型工程车驾驶人驾驶稳定性分析

重型工程车行驶过程中事故风险大,发生恶性事故的概率高,易造成重大生命和经济损失,其运输安全管理问题面临挑战.为探究重型工程车驾驶人驾驶稳定性与相关影响因素之间的关系,开展重型工程车自然驾驶试验,提取车辆运动学、道路条件、驾驶人状态和工作时间等数据;采用速度均值和速度标准差表征驾驶人驾驶稳定性,以睡眠模式、道路线形、道路...     

Modeling phone use prevalence and risk assessment among long-haul truck drivers in India

Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.     

Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.     

Factors affecting lane change crashes

This study aims to investigate the contributing factors affecting the occurrence of crashes while lane-changing maneuvers of drivers. Two different data sets were used from the same drivers' population. The first data set was collected from the traffic police crash reports and the second data set was collected through a questionnaire survey that was conducted among 429 drivers. Two different logistic regression models were developed by employing the two sets of the collected data. The results of the crash occurrence model showed that the drivers' factors (gender, nationality and years of experience in driving), location and surrounding condition factors (non-junction locations, light and road surface conditions) and roads feature (road type, number of lanes and speed limit value) are the significant variables that affected the occurrence of lane-change crashes. About 57.2% of the survey responders committed that different sources of distractions were the main reason for their sudden or unsafe lane change including 21.2% was due to mobile usage. The drivers' behavior model results showed that drivers who did sudden lane change are more likely to be involved in traffic crashes with 2.53 times than others. The drivers who look towards the side mirrors and who look out the windows before lane-change intention have less probability to be involved in crashes by 4.61 and 3.85 times than others, respectively. Another interesting finding is that drivers who reported that they received enough training about safe lane change maneuvering during issuing the driving licenses are less likely to be involved in crashes by 2.06 times than other drivers.     

An experimental study on errors regarding the driving behavior of young males caused by temporal urgency on open roads: A Bayesian estimation

Efforts made to prevent road traffic crashes have reduced the number of crashes involving young drivers, however, overall, they are still the most common age group reported in these incidents. In this study, the driving behaviors of ten young male drivers were compared based on lane change—under time pressure and in normal conditions—on open roads where the surroundings constantly change. The study also chronologically analyzed the motions within five seconds from when the driver started steering. The results of the analysis revealed that, compared to normal conditions, drivers under time pressure responded by steering first and viewing their surroundings second. It was also estimated that, regardless of the conditions, the driver tended to use the indicator two to zero seconds before adjusting the steering. This finding indicates that the delay in viewing their surroundings resulted in delayed signaling, and the series of driving behaviors to change lanes may have been overcrowded in time. The findings of this study suggest that it is possible to prevent human error by focusing on the balance and connection between cognition and the behaviors required for a series of driving behaviors.     

Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes.     

重复驾驶条件下驾驶员记忆增长模型研究

驾驶员的记忆影响视觉搜索及路径规划等驾驶行为,进而影响道路通行效率与交通安全.为了描述重复驾驶条件下驾驶员记忆变化的特征,设计模拟驾驶实验,研究同一场景下重复驾驶对驾驶员记忆的累积刺激.通过场景记忆量表衡量驾驶员的记忆程度,分析了驾驶员记忆增长与重复驾驶次数的动态变化关系,分别采用单分子式、修正Weibull方程及Richards方程建立累积刺激作用下驾驶员记忆增长模型,并以误差平方和、均方根误差和调整 R2为评价指标对模型精度进行对比分析.结果表明,3种模型均能对驾驶员记忆增长特性进行描述,其中 Richards模型精度最高,其平均调整R2为0.9884.Richards模型揭示了记忆的同化与异化作用的本质,更适合建立重复驾驶条件下驾驶员对场景的记忆增长模型.      

Assessment of the effectiveness of connected vehicle weather and work zone warnings in improving truck driver safety

Improving work zone safety remains a prime challenge for the transportation sector in the United States. In particular, the frequency and severity of work zone crashes involving large trucks in rural freeways are alarming. Lack of compliance with the instructions provided at work zones results in increased crash risk. In-vehicle advanced warning systems enabled by Connected Vehicle (CV) technology have the potential to prompt appropriate driver response, make navigation more predictable, and improve overall work zone safety. This study falls under the umbrella of the WYDOT Connected Vehicle Pilot Program and seeks to investigate the impacts of the Pilot's real-time weather and work zone notifications on the behavior of truck drivers in rural freeway work zone settings under poor visibility. Twenty professional truck drivers participated in this simulator study. The driving scenarios were designed to mimic the driving conditions experienced on Wyoming Interstate 80. Findings suggest that exposure to the CV notifications has promising safety benefits manifested in improved driver behavior and response. Furthermore, both the weather and work zone notifications acquired high approval from the participants in terms of usefulness and ease of understanding. Nonetheless, the display of multiple work zone warnings on the Human Machine Interface may had introduced little to moderate distraction for some participants. Overall, this study brings forth valuable lessons that are being funneled to support informed decision making to enhance the Pilot's existing Human Machine Interface design.     

Crash risk factors associated with injury severity of teen drivers

This paper focuses on identifying crash risk factors associated with injury severity of teen drivers. Crash data obtained from the Highway Safety and Information System (HSIS) for the entire state of North Carolina, for years 2011 to 2013, was used for analysis and modeling. Among all the crashes during the study period, a total of 62,990 crashes involving teen drivers (15 to 19?years) were analyzed. A partial proportionality odds model was developed to identify factors contributing to injury severity of teen drivers. The results obtained indicate that teen drivers driving sports utility vehicles and pickup trucks are more likely to be severely injured when compared to teen drivers driving passenger cars. Teen drivers are more likely to be severely injured on weekdays, particularly during peak hours. The chances of teen drivers getting involved in severe injury crashes on Tuesdays and Fridays is higher when compared to Sundays. Age, gender, road configuration, terrain, adverse weather condition, and access control are observed to have a significant effect on teen driver's injury severity.     

考虑驾驶人风格的换道轨迹规划与控制（双语出版）

基于自动换道控制技术中融合个性化驾驶人风格的研究，建立考虑驾驶人风格的车辆换道轨迹规划及控制模型以提高换道规划控制模型对不同风格驾驶人的适用性，在保证安全性的基础上进一步满足驾驶人的个性化需求。首先通过问卷调查的方式采集得到了212份驾驶人风格量表数据，采用主成分分析法和K均值（K-means）聚类分析法将驾驶人按驾驶风格分为激进型、普通型和谨慎型，并通过驾驶模拟器试验采集不同风格驾驶人分别在自车道前车、目标车道前车和目标车道后车影响下的换道行为数据。然后对椭圆车辆模型进行改进，以描述不同风格驾驶人的行车安全区域，并据此构建3种典型工况下不同风格驾驶人的换道最小安全距离模型，结合驾驶舒适性约束、车辆几何位置约束以及不同风格驾驶人的换道行为数据，以换道纵向位移最短为目标，实现适应驾驶人风格的换道轨迹规划。最后以基于预瞄的路径跟踪模型作为前馈量，设计基于动力学的线性二次型最优（LQR）反馈控制器，通过调节控制权重矩阵实现3种工况下不同驾驶人风格的换道轨迹跟踪。PreScan和MATLAB/Simulink联合仿真结果表明：所设计的考虑驾驶人风格的换道轨迹规划及跟踪控制模型能够实现不同驾驶风格的自动换道轨迹规划及跟踪控制，可满足驾驶人个性化换道需求。     

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.     

What predicts the number of attempts to pass the driving test? A case from Norwegian driving education model

This study focuses on examining the relationships between the variables within the driving education process in Norway by aiming to answer three research questions: 1) Is there a difference between learner drivers above and below 25 years old in time spent at different steps and between theory and practical tests? 2) Do the time spent during different steps of the driving education, the number of attempts in the theory and practical tests differ by demographic variables? 3) What variables predict the number of attempts to pass the practical test? Data were extracted from two registry systems provided by the Norwegian Public Roads Administration. It included information from a randomized sample of 452 learner drivers who took their driving license in 2017. The age mean of the learner drivers when they got their license was 24.3 and most of them (54.6%) were males. Independent samples t-test results showed that compared to the learner drivers below 25 years, those above 25 years old spent significantly more time during steps 3 and 4, and had significantly more attempts to pass both theory and practical tests. In terms of the demographic variables, age was significantly and positively correlated with the time spent during the whole driving education and the number of attempts both in the theory and practical tests indicating that time spent for driving education and the number of attempts in the tests tend to increase with the increasing age. Also, the average time spent between taking the theory test and completing step 4 was significantly more among males than females. Test location had no significant influence on the number of attempts to pass the practical test. Finally, two separate regression analyses were conducted to examine the predictors of the number of attempts to pass the practical test for learner drivers both below and above 25 years old. For both groups, the strongest predictor of the number of attempts in the practical test was time spent between the theory test and the practical test, which indicates that as the time gap between the two tests increases learner drivers are more likely to fail at the practical test. Results are discussed for their implications which could be useful to improve the driving education process both in Norway and in other countries with similar driving education models.     

CA141汽车半轴的可靠性设计

在工作载荷等基本随机变量的概率特性已知的情况下，使用二阶矩方法对ＣＡ１４１汽车半轴的可靠性进行设计，并编制实用的计算机程序，可准确地得到ＣＡ１４１汽车半轴的可靠性设计参数，此方法是一种较为实用和有效的方法，对机械零部件的可靠性设计具有通用性。     

应急车辆延误情况调查及应对措施研究

因交通拥堵而造成的应急车辆救援延误导致悲剧事件频发。为了解应急车辆救援延误的情况并研究应对措施,以普通机动车驾驶者为对象进行问卷调查,针对驾驶者对应急车辆的认识和对应急车道的占用情况,驾驶者在驾驶过程中对应急车辆是否避让,以及避让方法进行调查。然后利用SPSS数据统计软件筛选出对驾驶者驾驶行为影响权重较大的特征变量,并基于Logistic模型建立了驾驶者特征与占用应急车道和避让应急车辆行为的模型。在此基础上提出相应的应对方案,利用Vissim仿真软件对解决方案进行仿真。结果显示:对有一定驾驶年龄并有本科以上学历的青壮年的驾驶行为对应急车辆延误有较大影响,且正确的避让方法能明显地减少应急车辆的行程时间。      

A meta-analysis of the impacts of operating in-vehicle information systems on road safety

This study aims to estimate the overall impact of distraction due to operating in-vehicle information systems (IVIS) and similar devices while driving on road crashes. While similar research has been undertaken investigating the issue, varying results have been reported so far. Therefore a two-step approach was adopted: initially a review of the literature was conducted to identify key high quality studies and the parameters that they examined. Afterwards, meta-analyses were applied in order to estimate the overall effects of operating IVIS while driving on the absolute proportion of crashes (i.e. the proportion of total crashes due to IVIS). After applying a random effects meta-analysis to the findings of existing studies, it was found that 1.66% of crashes occur due to operating devices in total. In addition, it is indicated that about 0.6% of safety-critical incidents for professional drivers are due to in-vehicle device operation. The odds of crashes influenced by IVIS operation were also estimated and were found to be very low. From the findings of the present review and the meta-analysis, it is suggested that device operation as a risk factor while driving is a less researched aspect of driver distraction than others, and more studies would improve result estimates and transferability, especially for professional drivers. This study summarizes concisely the current effect of driver interaction with in-vehicle information systems on crashes, which might become considerably pertinent in view of the increasing deployment of vehicles with increasing levels of automation.     

老年驾驶人的驾驶自我调节研究述评

随着社会人口老龄化的发展，老年驾驶人的占比逐年增加，提升老年人的驾驶安全性对于其安全自主出行和公共交通安全均具有重要意义。驾驶自我调节是老年驾驶人为适应身体、认知功能变化而对驾驶行为做出的主动调整，是其提升驾驶安全性、延长驾驶生命和维持自主行动能力的有效补偿策略。通过对已有关于老年驾驶人的驾驶自我调节研究进行系统回顾，介绍了老年驾驶人的驾驶自我调节行为的定义及其表现，归纳分析了其驾驶自我调节行为的影响因素及产生机制，在此基础上总结了现有研究的局限，并指出了未来进一步研究的主要思路和方向。对文献的梳理和分析表明：老年驾驶人的驾驶自我调节包括减少驾驶频率和回避具有挑战性的驾驶情境2种主要形式，并可分为策略性、战术性和生活目标性自我调节3种不同的层次水平；驾驶自我调节是一个复杂的过程，社会人口因素、生理健康和功能状况、心理因素等均可对其产生影响；驾驶自我调节的产生机制可以被概括为是个体从认知到态度改变，再到形成调节行为意向，直至最终发生驾驶行为改变的动态决策过程。未来对老年驾驶人的驾驶自我调节行为研究应更进一步将客观驾驶行为数据、医疗机构数据与驾驶人主观自我报告相结合，适当开展追踪研究，考察驾驶自我调节行为随年龄的发展变化趋势，深入探究驾驶自我调节的产生机制及其在降低事故发生和提升驾驶安全性方面的作用。     

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.     

Modeling injury severity of crashes involving trucks: Capturing and exploring risk factors associated with land use and demographic in addition to crash,driver, and on-network characteristics

Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. The focus of this research, therefore, is on developing a methodology, capturing and integrating data, exploring, and identifying risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics by modeling injury severity of crashes involving trucks. Crash data for Mecklenburg County in North Carolina from 2013 to 2017 was used to develop partial proportional odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit >40 mph and curves, and driver fatigue, impairment, and inattention have a significant influence on injury severity of crashes involving trucks. These outcomes indicate the need for effective geometric design and improved visibility to reduce the injury severity of crashes involving trucks. The likelihood of a severe or moderate injury crash involving a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to identify potential risk areas, proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas.