Jin Ruining, Wang Xiao, Nguyen Minh-Hoang, La Viet-Phuong, Le Tam-Tri, Vuong Quan-Hoang
Civil, Commercial and Economic Law School, China University of Political Science and Law, Bei-jing, 100088, China.
Suzhou Lunhua Education Group, Suzhou, China.
Data Brief. 2023 Jun 22;49:109337. doi: 10.1016/j.dib.2023.109337. eCollection 2023 Aug.
Given the high fatality rate due to road traffic accidents in China, understanding the factors influencing aggressive driving behaviors among Chinese drivers is essential to alleviate the problem. The paper describes a dataset of 1039 Chinese drivers' driving behaviors and the socio-cultural factors associated with the behaviors. The dataset was collected through an online survey. The dataset comprises five main categories: 1) driving information, 2) aggressive driving behaviors, 3) friend/peer influence, 4) family influence, and 5) socio-demographic information. The dataset is valuable for public health and transportation researchers to explore factors influencing drivers' driving behaviors and public safety in China. The dataset's construct validity was confirmed by the Bayesian Mindsponge Framework (BMF) analytics. Specifically, the analysis shows that safe driving behaviors are affected by information promoting safe driving that is passively and actively absorbed from friends/peers (friends/peers being role models and friends'/peers' support, respectively). The result is consistent with the Mindsponge Theory's information-processing mechanism in human minds.
鉴于中国道路交通事故的高死亡率，了解影响中国驾驶员攻击性驾驶行为的因素对于缓解这一问题至关重要。本文描述了一个包含1039名中国驾驶员驾驶行为以及与这些行为相关的社会文化因素的数据集。该数据集是通过在线调查收集的。数据集包括五个主要类别：1）驾驶信息，2）攻击性驾驶行为，3）朋友/同伴影响，4）家庭影响，以及5）社会人口统计学信息。该数据集对于公共卫生和交通研究人员探索影响中国驾驶员驾驶行为和公共安全的因素具有重要价值。该数据集的结构效度通过贝叶斯思维海绵框架（BMF）分析得到了证实。具体而言，分析表明，安全驾驶行为受到从朋友/同伴那里被动和主动吸收的促进安全驾驶的信息的影响（朋友/同伴分别作为榜样和给予支持）。这一结果与人类思维中的思维海绵理论的信息处理机制相一致。
