Examining the relationship between driver distraction and driving errors: A discussion of theory, studies and methods

Over the past two decades the concept of driver distraction has been the focus of intense research attention. One aspect of distraction for which there has been limited systematic research, however, is its role in driver error causation. This article presents a review of the distraction literature with a view to elucidating what is currently known about the types of driving errors that distraction contributes to and the mechanisms by which distraction induces these errors. The review revealed a number of fundamental gaps in our knowledge, including the number and nature of errors made by drivers when distracted; the mechanisms by which distraction causes errors; whether and how distraction disrupts drivers’ ability to recover from errors; and how system-wide factors moderate the relationship between distraction and error. In closing, we attempt to identify the most appropriate theoretical and methodological approach to drive the integrated study of distraction and error forward. We conclude that it is only through the adoption of a systems approach that integrated countermeasures can be proposed and implemented to mitigate driver errors caused by distraction.     

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.     

复杂系统中人误原因因素的层次分析法

介绍和评析了人误分析历史上有重要影响的几种人误原因因素分类方法:传统人因分类法、信息处理分类法和认知系统工程分类法.基于认知可靠性及失误分析方法(CREAM)的人误原因因素分类,运用层次分析法(AHP)基本原理,建立了大规模复杂人-机系统人误原因因素层次结构模型及相应的AHP程序,并以JCO公司超临界事故为实例进行了分析.分析和应用结果表明,本文所建立的人误原因因素层次结构模型及AHP程序能够辨识出大规模复杂人-机系统中人误的主要原因因素,进而寻找出最优预防方案,对预防和减少此类人误事故的发生有积极意义.     

Managing error on the open road: The contribution of human error models and methods

Despite the provision of various theoretical models and error management methods, error and error-causing conditions remain omnipresent within road transport. This article presents a review of human error models and selected error management approaches, and their applications in a road transport context. The review indicates that such applications, although extant, are limited, and that, compared to other domains, the impact of the models and methods discussed has been only minimal. Reasons for this are discussed, and potential ways in which the models and methods can contribute to road safety are proposed. In conclusion, it is argued that human error models and management methods, although already well integrated within most safety critical domains, still have much to offer to the enhancement of road safety. Further, it is argued that advances in the area, in terms of theoretical and methodological development and validation, are still to be made, and that applications of the error management methods discussed are required to enable such advances.     

Issues of the Human Reliability Analysis in the Context of Probabilistic Safety Studies

This article addresses methodological issues of the human reliability analysis (HRA) in the context of probabilistic safety studies. Several conventional HRA techniques, more often used for the evaluation of the human error probabilities (HEPs), have been classified. A taxonomy of human actions, failure events, and related factors is outlined in order to distinguish action phases, human behavior types and incorrect outputs (errors of omission or commission), error types (slips, lapses, and mistakes), and performance-shaping factors (PSFs) influencing the human performance. A tree is proposed to facilitate the selection of a specific method for the evaluation of human reliability with regard to attributes of the situation analyzed. A software system based on the expert system technology to facilitate and document PSA and HRA is outlined. At the end of the article some research challenges in the domain are discussed.     

Assessment of older drivers: relationships among on-road errors, medical conditions and test outcome

PROBLEM: It is essential that driver licensing authorities have a valid and reliable system for evaluating older drivers' continuing competency; road tests are usually required as part of such a system. This study sought to find information about the nature of driving errors made during license review tests, and about relationships between error type and test outcome for older drivers. METHOD: Data from licensing authority files from 533 road tests during a 12-month period were analyzed; medical and other referral information was included. Average driver age was 76 years. Performance scores were generated for intersection negotiation, lane changing, low speed manoeuvres, positioning and speed control, safety margin, and car control. RESULTS: Logistic regression analysis showed that test outcome was well predicted by a subset of driving performance scores; adding driver age to the model explained very little variance. Age alone was strongly associated with outcome. Relationships between referral information and test outcome are also reported. IMPACT: Results highlight several factors relevant to the development of more valid and reliable road tests for older drivers.     

基于哈默“人的差错理论”的道路交通安全研究

以道路交通安全系统为背景,人的差错为研究方向,运用"哈默"人的差错分类理论,从机动车驾驶员和非机动车驾驶员、乘客及行人两种人群的差错角度,对2004—2006年3年间我国交通事故发生率和死亡人数进行较有针对性的分析归类和研究,进而分别提出基于主动性和被动性两方面的交通事故人因预防措施。通过"哈默"全新的分类方法,全面分析交通行为中人的差错类型,为道路交通事故预防体系的研究、建立以及进一步的发展,提供了有参考价值的基于人的行为机理的理论依据和合理化建议。     

A systems perspective of managing error recovery and tactical re-planning of operating teams in safety critical domains

Introduction

Research in human error has provided useful tools for designing procedures, training, and intelligent interfaces that trap errors at an early stage. However, this “error prevention” policy may not be entirely successful because human errors will inevitably occur. This requires that the error management process (e.g., detection, diagnosis and correction) must also be supported. Research has focused almost exclusively on error detection; little is known about error recovery, especially in the context of safety critical systems. The aim of this paper is to develop a research framework that integrates error recovery strategies employed by experienced practitioners in handling their own errors.

Method and Results

A control theoretic model of human performance was used to integrate error recovery strategies assembled from reviews of the literature, analyses of near misses from aviation and command & control domains, and observations of abnormal situations training at air traffic control facilities. The method of system dynamics has been used to analyze and compare error recovery strategies in terms of patterns of interaction, system affordances, and types of recovery plans. System dynamics offer a promising basis for studying the nature of error recovery management in the context of team interactions and system characteristics.

Impact on industry

The proposed taxonomy of error recovery strategies can help human factors and safety experts to develop resilient system designs and training solutions for managing human errors in unforeseen situations; it may also help incident investigators to explore why people's actions and assessments were not corrected at the time.     

A review of medical error taxonomies: A human factors perspective

Although a large number of medical error taxonomies have been published, there is little evidence to suggest that these taxonomies have been systematically compared. This paper describes a study comparing 26 medical error taxonomies using a human factors perspective. The taxonomies were examined to determine if they classified systemic factors of medical errors and if they utilized theoretical error concepts in their classifications. Scope of classification was also examined. It was found that two-thirds of the taxonomies classified systemic factors of medical errors and only a third utilized theoretical error concepts. Medical error taxonomies based on theoretical error concepts were more likely to be generic in applicability and also more likely to classify systemic factors and psychological error mechanisms of medical errors. In addition to terminology, the medical error taxonomies also varied in terms of domain-specificity, granularity, and developmental process. Different medical error taxonomies provide different information; how these differences affect medical error management needs to be investigated.     

Cognitive failures, driving errors and driving accidents

INTRODUCTION: The impact of a driver's cognitive capability on traffic safety has not been adequately studied. This study examined the relationship between cognitive failures, driving errors and accident data. METHOD: Professional drivers from Iran (160 males, ages 18-65) participated in this study. The cognitive failures questionnaire (CFQ) and the driver error questionnaire were administered. The participants were also asked other questions about personal driving information. A principal component analysis with varimax rotation was performed to determine the factor structure of the CFQ. Poisson regression models were developed to predict driving errors and accidents from total CFQ scores and the extracted factors. RESULTS: Total CFQ scores were associated with driving error rates, but not with accidents. However, the 2 extracted factors suggested an increased effect on accidents and were strongly associated with driving errors. DISCUSSION: Although the CFQ was not able to predict driving accidents, it could be used to identify drivers susceptible to driving errors. Further development of a driving-oriented cognitive failure scale is recommended to help identify error prone drivers. Such a scale may be beneficial to licensing authorities or for developing driver selection and training procedures for organizations.     

A proactive approach to human error detection and identification in aviation and air traffic control

In recent years, there has been a realization that total elimination of human error may be difficult to achieve. A further reduction of accidents will require a better understanding of how practitioners manage their errors in ways that consequences are contained or mitigated. With this goal in mind, the present study has set out to propose a framework of cognitive strategies in error detection that would make human performance resilient to changes in work demands. The literature regarded error detection as a spontaneous process that occurs either while an action is executed (action-based detection) or after action feedback (outcome-based detection). To help practitioners maintain a state of mindfulness and introspection, this study proposes several cognitive strategies such as, rehearsing tasks for future execution, bringing into conscious attention routine tasks, seeing how trajectories change over time, and cross-checking data for reliability. Two further detection mechanisms are proposed at the situation assessment and planning stages of performance. Awareness-based detection may include revising a model of the situation, finding hidden assumptions, and testing the plausibility of assumptions. Planning-based detection addresses issues such as, identifying uncertainties in a plan, thinking out possible errors, and deciding when and how often to review task progress. Finally, several attitudinal factors and team factors are presented that affect the processes of error detection and identification. The cognitive strategies in error detection together with the attitudinal and team factors constitute a framework for designing the content of error management training.     

Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Introduction: Digital billboards (DBs) are a competing factor for attracting drivers’ attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers’ attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers’ behavior and measure factors responsible for drivers’ distraction in Babol, Iran, as a case study. Method: Corresponding data were collected through a Naturalistic Driving Study (NDS) of 78 participants when facing DBs (1,326 samples). These data were analyzed by applying structural equation modeling (SEM) to concurrently recognize relationships between endogenous and exogenous variables. Human, environmental, and road factors were determined as exogenous latent variables in a model to evaluate their influences on drivers’ distraction as an endogenous variable. Results: The results showed that road, environmental, and human factors reciprocally interact with drivers’ distraction, although the estimated coefficient of human factors was more of a factor than that of the other groups. Furthermore, younger drivers, beginner drivers, and male drivers (as human factors); night and unclear weather like a rainy day (as environmental factors); and installing DBs at complicated traffic positions like near-intersections (as road factors) were determined to be the main factors that increase the possibility of drivers’ distraction. Finally, model assessment was suggested using the goodness-of-fit indices.     

基于MAIB事故报告的人为失误灰色关联分析

人为失误作为海上交通事故的主要原因,受到多种因素的影响。为了识别这些影响因素,避免或减少因人为失误导致的海上交通事故,基于96件英国海事调查委员会(MAIB)事故报告,应用熵加权灰色关联分析,分别按船旗国、船舶类型、事故类型计算人为失误与影响因素之间的关联度。结果表明,能力/技能/知识、团队协作、程序和现行规程、设备、交流(内部和外部)和管理/检验/检查是影响人为失误的主要因素。     

Some Aspects of Vehicle Active Safety

Three selected aspects of vehicle active safety are presented in this article: (a) modeling the driver and the driver–vehicle environment system, (b) the dynamic aspects of vehicle rollover, and (c) an analysis of the process of passing. Sample solutions and results show the need for further research in the field of vehicle safety in order to lower the probability of drivers, passengers, and other road users being involved in road accidents.     

基于人因失误分析的医院电气安全管理研究

医院是重点用电单位,用电的安全性和供电可靠性都比较高,但因不可抗力、供电系统故障、医院管理问题、人为失误等方面的原因,仍存在发生各类电气突发事件的风险。其中,由人的误操作或不安全行为因素而诱发的电气方面的突发事件已成为医院非医疗事故的主要原因。文章针对医院电气安全操作方面的人因失误,从个人和组织两个角度进行了失误原因的分析,认为人的失误既受个体因素的影响,也受环境、制度和管理水平的影响。在此基础上,提出完善相关规章制度建设、加强教育与培训等,从组织制度建设、人员技术素质提高等方面,提出预防与减少人因失误的措施与方法,提高供配电质量,为医院医疗工作提供有效的电气安全后勤保障。     

Traffic climate and driver behaviors: The moderating role of driving skills in Turkey and China

Introduction: While road traffic accidents and fatalities are a worldwide problem, the rates of road traffic accidents and fatalities show differences among countries. Similarly, driver behaviors, traffic climate, and their relationships also show differences among countries. The aim of the current study is to investigate the moderating effect of driving skills on the relationship between traffic climate and driver behaviors by country. (Turkey and China). Method: There were 294 Turkish drivers and 292 Chinese drivers, and they completed the Traffic Climate Scale, the Driving Skills Inventory, and the Driver Behavior Questionnaire. The moderated moderation analyses were conducted with Hayes PROCESS tool on SPSS. Results: The results showed that safety skills moderated the relationship between internal requirements and violations both in Turkey and China. Safety skills also moderated the relationship between internal requirements and errors only in China and the relationship between functionality and violations in Turkey. Perceptual-motor skills moderated the relationships between external affective demands and errors, and also the relationship between internal requirements and positive driver behaviors in Turkey. It can be inferred that driving skills has different influences on traffic climate-driver behaviors relationship in different cultures and there might be cultural differences in the evaluation of drivers’ own driving skills. Practical Applications: Among driving skills, safety skills have a more critical role to increase road safety by decreasing number of violations. Interventions to increase safety skills of drivers might be promising for road safety.     

Analysis of naturalistic driving videos of fleet services drivers to estimate driver error and potentially distracting behaviors as risk factors for rear-end versus angle crashes

Objective: The objective of this study was to estimate the prevalence and odds of fleet driver errors and potentially distracting behaviors just prior to rear-end versus angle crashes.

Methods: Analysis of naturalistic driving videos among fleet services drivers for errors and potentially distracting behaviors occurring in the 6 s before crash impact. Categorical variables were examined using the Pearson's chi-square test, and continuous variables, such as eyes-off-road time, were compared using the Student's t-test. Multivariable logistic regression was used to estimate the odds of a driver error or potentially distracting behavior being present in the seconds before rear-end versus angle crashes.

Results: Of the 229 crashes analyzed, 101 (44%) were rear-end and 128 (56%) were angle crashes. Driver age, gender, and presence of passengers did not differ significantly by crash type. Over 95% of rear-end crashes involved inadequate surveillance compared to only 52% of angle crashes (P < .0001). Almost 65% of rear-end crashes involved a potentially distracting driver behavior, whereas less than 40% of angle crashes involved these behaviors (P < .01). On average, drivers spent 4.4 s with their eyes off the road while operating or manipulating their cell phone. Drivers in rear-end crashes were at 3.06 (95% confidence interval [CI], 1.73–5.44) times adjusted higher odds of being potentially distracted than those in angle crashes.

Conclusions: Fleet driver driving errors and potentially distracting behaviors are frequent. This analysis provides data to inform safe driving interventions for fleet services drivers. Further research is needed in effective interventions to reduce the likelihood of drivers' distracting behaviors and errors that may potentially reducing crashes.     

雾霾时车辆尾气排放对驾驶员能见度的影响

针对北京道路车辆尾气排放对雾霾时驾驶员道路能见度的影响问题,建立了雾霾时车辆尾气排放与驾驶员道路能见度关系模型。该模型以车辆为单元建立车辆尾气排放元胞,考虑了车辆尾气排放后的污染物物理和化学变化,并以车道中的尾气排放位置截面及截面单元、交叉口处的尾气排放位置区间为模型单元。为验证该模型,于2016年10月-2017年2月和2017年10月-2018年2月,在北京地区部分路网检测了驾驶员道路能见度等数据,并统计了各时段驾驶员道路能见度的分布情况。对建立的关系模型进行仿真,结果表明,该关系模型的仿真结果与实际检测数据的误差占比平均值小于4. 98%,验证了该关系模型用于描述北京市道路车辆尾气排放对雾霾时驾驶员道路能见度影响的有效性。     

The impact of changes to the NSW graduated driver licensing system on subsequent crash and offense experience

Introduction: In mid-2007 the State of New South Wales (NSW) in Australia introduced modifications to the existing graduated driver licensing system, lengthening the mandatory number of supervised hours for learner drivers aged under 25 years from 50 to 120 and extending the minimum learner period from 6 to 12 months. Additional driving restrictions were also introduced for young drivers in the two provisional licensed periods, P1, P2. This paper aims to evaluate this change by comparing the crash and offense experiences of young learner drivers before and after it occurred. Method: From driver licensing files supplied by the NSW transport authority two cohorts of persons obtaining their initial learner's permits in the year prior to the changes and in the subsequent year were constructed with demographic data, dates of transition to the driving phases, dates of crashes, and dates and types of traffic offenses. Both cohorts comprised around 100,000 individuals. Crash rates per 100 years of person-time under observation post P1 with their standard errors were calculated. Using a survival-analytic approach the proportion of crashes of all types were graphed in three month periods post P1. Sexes were treated separately as were initial learner ages of 16, 17, 18–21, and 22–24 years. The distribution of traffic offense types during P1 and P2 phases were also compared. With such large numbers formal statistical testing was avoided. Results: No meaningful differences in the crash or offense experiences of the two cohorts in either sex or at any age were observed. Delaying progress to unsupervised driving has road safety benefits. Conclusions: At least in conditions similar to those in NSW, requiring more than 50 h of supervised driving seems to have few road safety benefits. Practical applications: Licensing authorities should be cautious in extending the mandated number of supervised driving hours beyond 50.     

An overview of research advances in road traffic trauma in China

With rapid development of social economies, road traffic accidents (RTAs) have continued to increase, and have become the "primary public hazard" to humans. Road traffic trauma (RTT) is a major cause of death in young people of all motorized countries. This article reviews the current advances in RTT research, in order to find some approaches to improving traffic administration and reducing RTAs and RTT. All available data were collected from government, literature, our own research, and conference proceedings. Statistical analysis from every country showed that human factors were still the main cause of RTAs, accounting for more than 90%. Vehicle and road factors caused 3-5% and less than 2% of the total RTAs, respectively. Approximately 85% of RTAs were caused by 21 to 45-year-olds. About 50% of deaths due to RTAs in the United States were related to drinking. In China, RTAs due to driver drinking accounted for 0.29-1.48%. About 6-8% of drivers were prone to RTA, causing 30-40% of RTAs. Seat belts are an effective way to prevent casualties, reducing mortality and morbidity by 13-50%. In China, about 70% of RTAs were related to bicycles. Prehospital emergency treatment is very important. About 35% of deaths may be avoided if the injured receive early and effective treatment. From 1983 to 1992 the mortality of RTT increased by 13% in 18 developing countries, while it decreased by 18% in 13 developed countries, indicating the importance of comprehensive treatment of traffic administration. In addition many advances have been made in basic scientific research of RTT, such as development of serial bioimpact machines and investigation of biomechanical and biochemical mechanisms of impact injuries. In this century, RTAs and RTT are predicted to continue to increase in many countries, especially in developing ones. Full cooperation and comprehensive treatment should be performed in order to improve traffic safety.