School of Traffic and Transportation Engineering, Central South University, Changsha, 410000, China.
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, LE113TU, United Kingdom.
Accid Anal Prev. 2021 Mar;151:105934. doi: 10.1016/j.aap.2020.105934. Epub 2021 Jan 11.
With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior. At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating. The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.
随着车联网（CV）技术的出现，人们怀疑 CV 技术是否能够通过提供实时信息来改善驾驶员的意图和行为，从而避免事故。为了了解 CV 技术提供的实时信息对驾驶员可能产生的影响，本文旨在借助扩展的计划行为理论，开发一个考虑驾驶员异质性的模型。在无信号控制的交叉口，进行了一项基于陈述偏好（SP）的问卷调查，以建立一个包含 1001 名驾驶员的数据集。基于收集到的数据集，提出的模型检验了主观规范、态度、风险感知、感知行为控制和驾驶意图之间的关系，并研究了这些驾驶意图如何与 CV 环境中的驾驶员特征和经验同时相关。此外，形成了具有相似人格特质的同质驾驶员群体，然后分析了对驾驶意图的异质性响应。在分析驾驶员对 CV 技术提供的实时信息的响应时，得出了四个关键发现：1）所提出的 H-ETPB 模型具有良好的拟合度；2）无论驾驶员的人格特质如何，态度和感知行为控制都与加速的驾驶意图直接和间接相关；3）高神经质驾驶员的加速驾驶意图与主观规范显著相关，而低神经质驾驶员的加速驾驶意图则不相关；4）高神经质的老年驾驶员，以及收入不稳定或曾加入过网约车服务的低神经质驾驶员，加速的意图较强。本研究的结果可为优化交通性能和信息设计提供理论框架，并为 CV 和 CAV 环境中的车内个性化信息服务提供支持，协助交通管理部门为无信号控制交叉口的不同驾驶员设计最可接受的交通规则。
