基于信息加工模型的飞行员差错分析与分类

建立了飞行员信息加工模型。基于该模型,飞行员人为差错可按照认知行为过程分为信息感知差错、信息处理差错、决断差错、行为操作差错,并结合差错模式分类框架进行更详细地分类;影响飞行员认知和行为的情境条件可分为外部差错影响因素和内部差错影响因素。对以往飞行人为差错的分析表明,在信息加工层面分类的人为差错更宜于确定差错的心理机制。基于该理论模型的差错分类系统可以提高人为差错分析的有效性和一致性,从而提高差错管理的有效性。     

驾驶中使用手机对驾驶员行为安全绩效影响综述

驾驶中使用手机与交通事故之间存在着高度相关性。为揭示使用手机对驾驶行为安全绩效的影响,探索影响驾驶安全的理论机制,采取更有效的干预措施,结合近10 a来相关研究,综述了与驾驶安全密切相关的驾驶分心问题,主要包括:驾驶员分心的定义及其分类;使用手机对驾驶行为安全绩效的影响,如反应时(RT)、行车速度、路线保持和跟车距离;手机使用对驾驶员分心影响的理论机制,如信息加工理论和计划行为理论(TPB)。分析表明,使用手机会导致驾驶员的反应时延长15%～40%,驾驶路线发生明显偏移,对于行车速度减缓和跟车距离延长的假设需结合驾驶员主客观数据进行比较做进一步验证;驾驶过程中使用手机会增加驾驶员的认知负荷,TPB能够对使用手机行为进行有效的解释和预测,但对该理论中基于信念测量的研究还很少;除手机操作任务,影响驾驶员分心的其他操作任务还需做进一步的研究。     

认知过程中交通标志视认有效性影响因素分析

应用认知心理学的理论及方法,对驾驶员感知交通标志的心理过程进行分析,提出一种将驾驶员的直觉、分析和推理三者相结合的驾驶员信息处理模型。从标志和驾驶员特征出发对影响标志视认性的因素给出基于心理感知的理论分析。该分析结果为驾驶员认知过程研究、标志视认有效性的评价指标选取和相关系统开发设计提供了依据。     

CREAM追溯法在交通事故人因分析中的应用研究

人因失效是道路交通事故发生的主要原因.认知可靠性和失误分析方法(CREAM)可以追溯事故发生的根原因,并对事故隐患进行预测.它强调情景环境对人的行为的重要影响,较符合驾驶员可靠性分析的需求.研究了CREAM中的追溯分析方法在道路交通人因失效事件根原因分析中的应用,建立了道路交通事故的人因失效模式,对人因失效基本原因的分类和后果-前因关系进行归纳、整理和补充,提出了适用于道路交通人因失效事件的“后果-前因”追溯表和具体的追溯分析步骤.进而对其定量计算进行了探索,并通过实例探讨了其应用,得到事故发生的根原因.     

低驾龄驾驶员路怒情绪对行为决策过程的影响

为研究路怒情绪如何影响低驾龄驾驶员驾驶行为决策过程,在Wickens的信息加工模型基础上,构建路怒情绪影响下的驾驶员注意、感觉、思维决策的信息加工决策模型;设计问卷对驾龄在0～6年的低驾龄驾驶员进行调查,利用结构方程模型(SEM)验证调查结果。研究结果表明:驾驶员路怒情绪对注意影响的路径系数为0. 57,对感知编码影响的路径系数为0. 60,对思维决策影响的路径系数为0. 40;路怒情绪对低驾龄驾驶员的驾驶行为决策过程的注意、感觉、思维决策有直接的显著影响。     

区域道路交通事故驾驶员原因的灰色关联分析

本文应用灰色系统理论中的灰色关联分析研究驾驶员行为对道路交通事故的影响.就驾驶员行为对道路交通事故的影响来说,灰色关联序的汁算结果表明,导致某省道路交通事故最主要的原因是驾驶员操作不当,此外依次为不按规定让行,未保持安全距离、违章转弯等.通过规范驾驶员培训、考试制度和加强道路交通安伞宣传、教育,可以减少道路事故的发生.     

船舶驾驶员操作可靠性量化的CREAM改进模型

为解决不确定信息条件下船舶值班驾驶员(OOW)操作可靠性量化问题,以认知可靠性和失误分析方法 (CREAM)为基础,采用模糊集合(FS)、贝叶斯网络(BN)和证据推理(ER)算法对多源粗糙观测数据进行处理,构建一个改进的CREAM模型。用该模型对长江特定场景下驾驶员操作可靠性进行量化。结果显示,驾驶员认知行为控制模式为战术型,失误概率为3.40×10-3。数据对比验证和模型灵敏度分析表明,结果合理,模型稳定。     

ATIS环境下驾驶员认知负荷研究进展

为构建更加符合驾驶员认知特性的出行信息环境,提高驾驶员出行信息服务水平,对ATIS环境下驾驶员认知负荷研究进展进行系统分析与评述.首先阐述了认知负荷的理论基础,随后分析了ATIS环境下驾驶员认知负荷的影响因素,同时分类解析了驾驶员内在认知负荷、外在认知负荷与相关认知负荷的产生根源,并对认知负荷的形成机理进行了分析;系统评述了驾驶员认知负荷的测量方法,最后基于驾驶员信息认知负荷提出了ATIS优化对策.结果表明:年龄、性别、受教育程度、月收入、出行经验是影响驾驶员认知负荷的主要因素;道路交通标志的版面设计与驾驶员的标志视认时间关联密切;驾驶过程中拨打手机及使用车载终端均会分散驾驶员注意力,增加驾驶员的信息认知负荷;较高的车速与音频播放等环境会对驾驶员的信息认知能力提出更高要求.     

私家车驾驶员驾驶行为与人格特质的关系研究

为研究私家车驾驶员不安全驾驶行为与人格特质的关系,选用大五人格理论作为人格特质理论,将不安全驾驶行为划分为3类(违规行为、疏忽失误、认知错误),并以此构架私家车驾驶员不安全驾驶行为与人格特质的关系模型,通过调查问卷收集数据,应用结构方程模型(SEM)验证所建模型。研究得出:外倾性对违规行为有正向影响(路径系数0.180);宜人性和尽责性对违规行为有负向影响(路径系数-0.962和-0.544);神经质对疏忽失误和认知错误均有正向影响(路径系数0.417和0.409);开放性对违规行为无显著影响。     

认知心理学视角下矿工的信息加工模型研

矿工在煤矿井下的生产过程是对外界信息的加工处理过程。矿工能否正确处理外界刺激,将直接产生安全或不安全行为,影响煤矿安全生产,甚至引发重大事故。在认知心理学视角下,应用感觉、注意和记忆理论对矿工行为的形成机制进行分析,建立矿工信息加工模型,并对模型的特点和作用进行分析,同时指出该模型存在的不足。信息加工模型的建立与分析可为安全管理措施的制定及安全培训内容及环节的设计提供理论依据。     

基于认知心理学的驾驶员交通标志视认性理论分析

从认知心理的角度研究了驾驶员对交通标志的视认性,建立了基于认知心理学的驾驶员信息处理过程的概念模型,总结了影响交通标志视认性的两类因素———驾驶员因素和交通标志物理因素。提出了用知觉理论解释不同驾驶经历的驾驶员对标志识别存在的差异现象;用特征说理论解释了标志的独特性有利于驾驶员的识别;标志的信息量不宜超出驾驶员有限的注意力资源以及驾驶员的短时记忆容量限定了交通标志应该具有易读性。为改进交通标志的设计设置,探讨交通标志视认性的指标测度手段,进而为建立起客观实用的认知心理学实验方法及综合评价指标体系提供了理论基础。     

大货车驾驶员交通心理与交通安全的研究

笔者从交通心理学的角度,对大货车驾驶员在行驶过程中的心理状况、因攻击性驾驶行为、强烈的冒险动机的驱使、对道路期望心理的失衡及交通安全感偏差等不良心理因素而导致恶性交通事故进行分析。通过研究得出以下结论大货车驾驶员是一特殊的群体,应重视对该群体的教育、培训与管理,从而提高驾驶员的职业道德水平、性格品质、交通安全感及驾驶技能,以改善道路安全环境和交通安全状况、降低道路交通事故发生率。     

Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data

IntroductionUnderstanding driver behavior is important for traffic safety and operation, especially at intersections where different traffic movements conflict. While most driver-behavior studies are based on simulation, this paper documents the analysis of driver-behavior at signalized intersections with the SHRP 2 Naturalistic Driving Study (NDS) data. This study analyzes the different influencing factors on the operation (speed control) and observation of right-turn drivers.MethodA total of 300 NDS trips at six signalized intersections were used, including the NDS time-series sensor data, the forward videos and driver face videos. Different factors of drivers, vehicles, roads and environments were studied for their influence on driver behavior. An influencing index function was developed and the index was calculated for each influencing factor to quantitatively describe its influencing level. The influencing index was applied to prioritize the factors, which facilitates development and selection of safety countermeasures to improve intersection safety. Drivers' speed control was analyzed under different conditions with consideration of the prioritized influencing factors.ResultsVehicle type, traffic signal status, conflicting traffic, conflicting pedestrian and driver age group were identified as the five major influencing factors on driver observation.ConclusionsThis research revealed that drivers have high acceleration and low observation frequency under Right-Turn-On-Red (RTOR), which constituted potential danger for other roadway users, especially for pedestrians.Practical applicationsAs speed has a direct influence on crash rates and severities, the revealed speed patterns of the different situations also benefit selection of safety countermeasures at signalized intersections.     

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.     

基于多元子代理模块系统(MAS)的单司机值乘驾驶安全可靠性研究

介绍并分析影响单司机值乘驾驶安全可靠性的因素,强调单司机值乘人-机-环境系统安全性中人行为的重要性,提出利用MAS模型对单司机值乘的安全可靠性进行分析。介绍该模型的标定过程,给出其驾驶行为的安全可靠度和Agent模型结合在一起来确定司机决策行为的过程和方法。为提高我国单司机值乘驾驶安全性提供了一种研究方法。     

驾驶行为模型的研究进展

驾驶员行为模型的研究对于预测和干预驾驶员的风险行为、设计相关的道路安全设施与车内设备,以及制定交通法律法规等具有重要的意义。为了解和掌握学术界关于驾驶行为模型的研究进展,搜集、筛选和归纳了1960—2010年被SCI数据库索引的相关文章,将驾驶行为模型分类为描述性模型、信息处理模型、动机模型、计划行为理论(TPB)和躯体标识假设,并对每种模型进行评述和总结,理清这些模型间的内在联系。研究发现,现有各模型只是从某个角度研究驾驶员行为的部分特征,而不能解释驾驶员的全部行为。今后应不断完善和整合各类模型,并借鉴心理学、生理学和行为科学等相关领域的理论、知识,使驾驶行为模型变得更为实用、有效。     

Investigating the self-reported behavior of drivers and their attitudes to traffic violations

IntroductionDriving behavior theoretical models consider attitudes as an important determinant of driver behavior. Moreover, the association between the self-reported tendency to commit violations and accident involvement is widely recognized. This research investigates drivers’ self-reported behavior and attitudes to risky behaviors related to the traffic violations of speeding, drink-driving, and cell phone use using cluster analysis.MethodA sample of 601 Greek drivers participating at the SARTRE 4 pan-European survey is utilized. The analysis identified three clusters of drivers. Drivers in Cluster 1 commit traffic violations more often; drivers in Cluster 2 favor traffic violation countermeasures while having moderate views toward compliance with traffic rules; and drivers in Cluster 3 strongly support traffic violation countermeasures and also have strong views toward compliance with traffic rules. Risky behaviors and related attitudes that differentiate the three distinct groups of drivers (clusters) were determined.ResultsThe findings indicate that differences in attitudes and behaviors may be attributed to factors such as age, gender, and area of residence. The research findings also provided some insight about the current level of drivers’ attitudes to traffic violations, especially those that negatively affect traffic safety. The pattern of their views on violations may form the basis of risk behavior-related interventions tailored to the identified groups, aiming at informing, educating, and raising the awareness of the public.Impact on IndustryAgencies focused on safety interventions could exploit this information in designing and implementing education campaigns, enforcement programs and in defining relevant priorities.     

中美两国汽车驾驶安全影响因素研究

影响汽车驾驶安全的各国道路交通系统以及驾驶员的驾驶行为等存在着差异性 ,许多差异可以通过统计数据的分析 ,或者其他定量研究方法获取 ,但仍然有许多影响驾驶安全的因素难以发现。笔者通过焦点团体座谈会的方法 ,从驾驶者的角度 ,搜集并分析了中美两国驾驶安全的主要影响因素 ,以及具有美国驾驶经历会对我国驾驶员有何影响。通过中美两国定性数据的对比分析 ,得出了主要影响因素 ,与此同时 ,通过问卷调查定量地分析了这些因素的严重程度。     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.