车路协同系统接受度建模及性别差异分析

为了研究公众对车路协同系统（CVIS）的接受度以及在性别上的差异,考虑心理因素,基于拓展的技术接受模型建立车路协同系统接受度模型,通过网络问卷调查方法获取车路协同系统接受度影响因素的主观评价数据,使用偏最小二乘法的结构方程模型（PLS-SEM）检验测量模型的内部一致性和可靠性、收敛效度和判别效度以及模型的假设。研究结果表明：该模型对男性接受意向方差具有70.1%的解释力,对女性接受意向方差具有45.0%的解释力,表明模型对男性接受意向的解释力更强;男性与女性之间的接受意向存在很大的差异,女性比男性更加注重车路协同系统的实用性和安全性,而男性比女性更加注重车路协同系统的便利性,也更容易受到社会因素的影响;对于男性和女性来说,行为态度（ATB）对行为意向（BIU）具有正向显著影响,感知易用性（PEU）可以通过感知有用性（PU）和行为态度间接影响行为意向,感知有用性和可靠性对信任度具有正向显著影响;由于受访者对车路协同系统的实用性感知很高,导致对隐私的关注就越少,因而感知隐私风险（PPR）对信任度的影响并不显著;总体来看,构建的模型对于描述车路协同系统接受度具有一定的合理性与有效性,并且可以针对不同性别的人提高其接受度,可对制定精细化、差异化的策略提供支持。     

基于SEM的自动驾驶汽车接受度模型与风险分析

为了探究社会大众对自动驾驶汽车的接受度及其影响因素,通过对自动驾驶汽车的风险评估,基于TAM模型增加外部变量,改进感知易用性与感知有用性,建立自动驾驶汽车接受度研究模型。将研究模型中假设的外部变量、潜在变量、认知变量与自动驾驶汽车社会接受度的关系进行Pearson相关性与Kendall相关性检验。基于相关性检验剔除无关因素变量,建立线性回归模型,对相关因素与自动驾驶汽车接受度的关系进行了探究,并以此建立接受度结构方程模型,对各个变量与自动驾驶汽车社会接受度之间的因果关系进行了进一步研究论证。结果显示,法律伦理的认知对自动驾驶汽车接受意向产生（E =0.038,P < 0.001）显著正影响。驾龄、驾车频率对接受意向产生（E =-0.044,P < 0.001）显著负影响。学历通过促进公众对自动驾驶汽车正面的认知、抑制负面认知,从而对公众接受意向产生（E =0.172,P < 0.001）显著正影响。功能的认知会促进公众对自动驾驶汽车正面影响的认知,从而对接受意向产生（E =0.444,P < 0.001）显著正影响。      

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。     

城市自行车骑行者疲劳感知特性（双语出版）

为了探究自行车骑行过程中骑行者的疲劳感知变化规律及其影响因素,设计实施了针对不同骑行环境的骑行试验。根据试验收集的骑行者个体属性、骑行强度数据及骑行者生理、心理数据,利用多元线性回归方法建立了骑行者疲劳感知模型,定量描述了骑行者疲劳感知与骑行者个体属性、骑行强度以及骑行者生理、心理负荷之间的变化关系。应用统计检验方法对模型的线性显著性、多重共线性进行检验;并通过实施新的骑行试验验证了模型对于实际骑行活动中骑行者疲劳感知的预测能力。由模型分析可知：骑行者的疲劳感知变化主要受到骑行者个体属性、骑行强度、骑行者生理负荷以及心理负荷等属性的影响,4种属性的建模变量对于出行者疲劳感知的影响因子分别为-0.291,0.353,0.253,0.265。研究结果表明：在骑行过程中,骑行强度、骑行者生理负荷以及心理负荷因素与疲劳感知呈正相关关系,而骑行者个体属性则与疲劳感知呈负相关关系,其中骑行强度因素对于疲劳感知变化影响最大;模型预测的骑行者疲劳程度值与试验实测值无显著性差异;可通过改善现有骑行环境来降低骑行过程中骑行者的生理负荷和心理负荷,从而减小骑行者出行疲劳感知期望值,以此影响自行车出行者骑行选择行为的决策;骑行疲劳感知模型可应用于自行车交通评估与规划。     

城市居民使用共享单车意向研究——以合肥为例

为了探究共享单车使用意向的影响因素和因素间的作用关系,基于计划行为理论(TPB)构建了关于态度、主观规范、感知愉悦、灵活便捷和行为意向等潜变量的计划行为理论扩展模型.通过对问卷调查数据的信度和效度的分析,构建了共享单车使用意向的结构方程模型(SEM),得到各影响因素间的关系路径.结果表明,TPB理论可以很好地解释居民使用共享单车的行为意向,在共享单车的使用意向影响因素中,主观规范的影响最为显著,态度居其次,再次是感知愉悦,而灵活便捷对行为意向的影响作用相对较弱.      

城市自行车骑行者疲劳感知特性

为了探究自行车骑行过程中骑行者的疲劳感知变化规律及其影响因素,设计实施了针对不同骑行环境的骑行试验。根据试验收集的骑行者个体属性、骑行强度数据及骑行者生理、心理数据,利用多元线性回归方法建立了骑行者疲劳感知模型,定量描述了骑行者疲劳感知与骑行者个体属性、骑行强度以及骑行者生理、心理负荷之间的变化关系。应用统计检验方法对模型的线性显著性、多重共线性进行检验;并通过实施新的骑行试验验证了模型对于实际骑行活动中骑行者疲劳感知的预测能力。由模型分析可知:骑行者的疲劳感知变化主要受到骑行者个体属性、骑行强度、骑行者生理负荷以及心理负荷等属性的影响,4种属性的建模变量对于出行者疲劳感知的影响因子分别为-0.291,0.353,0.253,0.265。研究结果表明:在骑行过程中,骑行强度、骑行者生理负荷以及心理负荷因素与疲劳感知呈正相关关系,而骑行者个体属性则与疲劳感知呈负相关关系,其中骑行强度因素对于疲劳感知变化影响最大;模型预测的骑行者疲劳程度值与试验实测值无显著性差异;可通过改善现有骑行环境来降低骑行过程中骑行者的生理负荷和心理负荷,从而减小骑行者出行疲劳感知期望值,以此影响自行车出行者骑行选择行为的决策;骑行疲劳感知模型可应用于自行车交通评估与规划。     

老年人对自动驾驶汽车接受度研究

有限的交通方式选择使得老年群体出行的机动性、便捷性和可达性往往难以得到充分的满足,这会极大影响晚年生活的幸福感,为解决这一问题,针对老年群体的出行行为研究受到了广泛关注。自动驾驶汽车作为一种新兴出行方式,能够提高老年群体出行机动性,这一新兴出行方式得以普及的前提条件之一在于广大老年群体是否能迅速且广泛地接受。因此,在自动驾驶汽车大规模应用之前,研究老年群体对这一新兴技术的接受度对于未来提高老年群体出行质量具有重要意义。从行人和使用者2个角度,同时融合极具中国特色的面子意识等影响因素来探讨老年人（60岁及以上）对自动驾驶汽车的认知和接受度。从行人角度,进行了2种情景的假设,并采用有序Logistic回归分析态度、感知有用性、信任和感知风险等变量对自动驾驶汽车接受度的影响,结果发现信任对接受意向的影响最为显著,而感知有用性的影响并不显著。从使用者角度,采用结构方程模型探索面子意识等心理潜变量对自动驾驶接受意向的行为机理,结果表明态度、感知有用性、感知风险和面子意识等对使用意向都有显著影响,而对行人影响显著的信任因素对使用者影响却并不显著。研究成果能够为自动驾驶汽车企业推出老年群体相关出行服务提供一定的科学依据,同时也为政府相关政策的制定提供理论依据。     

公交动态信息下通勤出发时间选择行为分析

在交通行为分析中,传统效用函数形式不能反映个人属性对信息效用感知的影响。改进效用函数形式,运用多项Logit模型理论,建立了公交动态信息下的公交通勤者出发时间选择行为初始模型。从初始模型中选择t检验显著的解释变量,并分别基于传统效用函数形式、改进的效用函数建立了2个修正模型。对比2个修正模型标定结果发现,改进的效用函数比传统的效用函数更有利于提高模型性能。研究也表明:性别、收入的t检验值分别为2.02、-2.25,说明二者对乘坐时间信息效用感知显著;教育属性的t检验值为2.03,说明其对车内拥挤程度信息效用感知影响显著;乘坐时间取均值,车内拥挤度为0.7时,二者效用比值为1.54∶1,说明乘坐时间信息是影响出发时间选择最重要的因素。      

无人驾驶汽车对中短距离市际出行方式选择行为的影响

研究了无人驾驶汽车对中短距离市际间出行选择行为的影响.基于计划行为理论,通过建立结构方程模型,构建出行者对无人驾驶汽车的感知行为控制、主观规范、行为态度和行为意向心理潜变量.然后将这些心理潜变量纳入到随机系数Logit模型建立混合选择模型.以武汉市为例进行实证研究,结果表明:在效用函数中,车内时间、出入站和候车时间,以...     

基于卷积神经网络的自行车骑行行为识别

为研究适合我国城市交通的自行车骑行行为分类识别方法,实施骑行行为视频调查试验,并提取骑行者骑行特征信息.结合我国交通现状和自行车交通特点,提出一种新的骑行行为分类方法,新的行为分类可基本覆盖我国自行车交通的所有骑行行为场景.采用提出的行为分类方法对试验个体骑行行为进行人工分类标记,得到不同骑行场景下的骑行行为分类数据集.基于卷积神经网络(CNN)建立自行车骑行行为识别模型,考虑模型输入特征特性以及分类任务复杂度对模型结构进行迭代设计选优,调整模型网络组成和卷积参数,得到分类效果显著的模型结构.对比分析多元Logit分类模型、三层全连接层BP神经网络模型,与本文的卷积网络模型在骑行行为分类预测的表现,在20 000次迭代训练之后,3个模型均得到收敛结果.结果 表明,提出的骑行行为分类模型准确率分别高于多元Logit模型20%,高于BP网络模型15%,显著优于对比模型的识别效果.卷积网络模型可有效解析骑行行为与各骑行特征因素之间的关系.      

Clarifying multiple-mode decision making in conventional psychological models: A consideration of the influential mechanism of car use's characteristics on the behavioral use of public transportation

The existence of the multiple-mode decision making process challenges the existing psychological theories those predict travel behaviors because the theories were mainly to explain the relationship between travelers' perceived attributes of a targeted mode, not to make choices between modes. A review of related literatures showed a lack of focus on the mechanism of one mode influencing other mode in psychological models. The present study investigates how car-related factors affect the establishment of bus use intention in a psychological investigation. It is hypothesized that single car use factors do not directly influence bus use intention, rather than that; they indirectly influence bus use intention through an overall factor of car use. Results from a dataset of 270 samples investigating three types of daily trips in the Japanese context showed support to the mediating effect of the overall car use factor. As such, an arbitrary inclusion of car-related factors to psychological models of public transportation may not be recommended.     

Evaluating the correlation between risky riding behaviour and self-reported crashes in India: Minimizing unobserved heterogeneity

Riding Behaviour is found to be the main cause of Powered Two-Wheeler (PTW) crashes in more than90% of the crash events. The high percentage of PTW crashes resulting in fatalities has sought a serious need for research to examine risky riding Behaviour. A widely used instrument for measuring the self-reported riding Behaviour of PTW riders is the Motorcycle Rider Behaviour Questionnaire (MRBQ). In this study, exploratory factor analysis of the MRBQ revealed a four-factor solution viz., traffic errors, control errors, speed violations, and stunts. Despite the popularity of MRBQ, it is capable of covering only a small fraction of the large number of elements that affect the riding Behaviour. Many other elements remain overlooked in the analysis, resulting in unobserved heterogeneity. Therefore, the present study uses a random parameter negative binomial (RPNB) model to minimize the effect of unobserved heterogeneity. It was inferred from the RPNB model that variables like gender, control error, and speed violation have a randomly distributed regression coefficient. Further, it is found that traffic errors are the most significant predictor of crash risk. Additionally, results depict that male riders are positively associated with crashes, and they are more likely to involve in crashes as compared to female riders. The finding of this paper will be valuable for policymakers and decision-makers to improve the rider training program, licensing system, and design road safety campaigns.     

基于轨迹数据的共享电动自行车逆行风险行为影响因素研究

为改善电动自行车带来的交通安全问题,研究逆行风险行为与其影响因素间的相关关系。基于长沙市芙蓉区共享电动自行车GPS轨迹数据,实现逆行行为的精准识别,采用机器学习CatBoost模型与SHAP可解释机器学习框架,从道路条件、交通状态、土地利用性质等方面开展逆行行为影响要素挖掘及作用解析。研究结果表明：CatBoost模型能够有效预测路段逆行频次并提取逆行行为的重要影响因素,主要包括出行时段、公共交通设施、土地利用性质、道路条件及交通状态等;从出行时段来看,工作日、早晚高峰时段更容易发生逆行;从公共交通设施与土地利用性质来看,道路周围公交站地铁站出口数量及餐饮、公司、购物等设施数量与逆行频次呈现非线性影响关系,在一定范围内设施数量与逆行行为存在正影响作用;从道路条件来看,过街通道间距在50~400 m时不易发生逆行,在非机动车道无物理隔离设施或过街通道间距在400~600 m时容易发生逆行,间距大于600 m时作用不稳定;从路段机非分隔形式来看,护栏分隔的逆行概率较低,绿化带分隔的逆行概率较高;从交通状态来看,当骑行速度、加速度较低或较高时与逆行行为负相关,当骑行速度在6~16 km·h^-1及加速度在0.3~1.0 m·s^-2时与逆行行为正相关。研究成果可为共享电动自行车风险骑行行为辨识、非机动车交通安全管理提供有效的技术支持。     

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.     

广州大学城建设合同奖罚体系实践分析

广州大学城3年建成,速度快,质量好,被称为“廉政工程”、“阳光工程”。该文从抑制腐败、“深入设计”各项管理制度、细化施工合同奖罚条例等三方面阐述了大学城工程建设的保障体系,并结合劳动竞赛中的重奖重罚措施,综合论述了建立适合国情的建设组织管理模式及奖罚体系在工程建设中的重要作用。     

Effectiveness evaluation on cross-sector collaborative education programs for traffic safety toward sustainable motorcycle culture in Vietnam

Motorcycle injuries have caused serious implications for public health and national economies in many ASEAN countries. Drivers' lack of road safety awareness and low level of voluntary compliance hinder the promotion of traffic safety. Against this background, the Vietnamese government cooperated with motorcycle manufacturers in a wide range of educational activities. This study evaluates the effectiveness of cross-sector collaborative education programs implemented in Vietnam through a series of statistical analyses. Utilizing a sample of 600 respondents, we focus on the educational effects on riders' attitudes, behaviors, accident prevention, and riders' psychological changes after participating in safety activities. The results show that the effectiveness of rider training differed depending on riders' experience. Motorcyclists' improvement in risk awareness mainly results from the enhancement of safety awareness. The structural model revealed that safety activities have positive effects on motorcyclists' riding confidence, safety awareness, joy and comfort while riding, independence and freedom in daily life, and perspective-taking abilities. Altruistic motivation is suggested as the key factor to encourage motorcyclists' safe riding, highlighting the importance of building up traffic moral and expanding traffic safety culture across the country. With an attempt to find out the insufficient and missing content from the present training programs, this study seeks to inform policy decisions on accident prevention as well as promote motorcyclists' well-being based on the sustainable motorcycle culture in ASEAN countries.     

Modeling effects of driver safety attitudes on traffic violations in China using the theory of planned behavior

Traffic violations are recognized as one of the main causes of traffic accidents and have been found to be closely associated with driver attitudes toward traffic safety. In this study, a modified theory of planned behavior (TPB) was used to model the effects of driver safety attitudes on traffic violations, based on a questionnaire survey of 1505 drivers in China. In light of the strong correlations between the observed items, the items of the TPB components were grouped into several parcels, using an item-parceling method. Parcel-based structural equation modeling was then used to operationalize the modified TPB. The results indicate that the proposed model can accurately predict the occurrence of traffic violations based on the observed items related to driver traffic safety attitudes. It was found that driver attitudes, subjective norm, and perceived behavior control significantly affect traffic violations. For predicting traffic violations, driver attitudes toward traffic safety policies had the greatest influence, followed by driver attitudes toward risky driving behaviors and the attitudes of others toward risky driving behaviors. Finally, suggestions on traffic enforcement and education to reduce traffic violations are proposed based on the results.     

汽车乘坐舒适性主观评价模型的构筑

探讨一种主观评价汽车乘坐舒适性的方法。首先采用心理测定法中的SD法，对汽车乘坐舒适性进行主观评价，然后导入FUZZY（模糊）理论中的模糊测度和模糊积分，构筑了汽车乘坐舒适性主观评价的阶层化模型，使以往很难处理的心理状态变化的定量分析成为可能。