基于文献计量分析的驾驶风险识别研究综述

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL
Huimin Ge, Yunyu Bo, Wenkai Zang, Lijun Zhou, Lei Dong
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引用次数: 0

摘要

为了了解国内外驾驶风险识别的研究现状和发展方向,选取中国知网(CNKI)和科学网(WOS)近12年(2011-2022)在驾驶风险识别领域的相关文献作为研究样本,并使用文献计量工具VOSviewer和Citespace进行可视化分析。从时间分布、国家合作网络、国内机构分布、期刊表现和关键词综述、文献耦合聚类和研究热点等方面对情况进行了分析。结果显示,发表论文数量逐年波动,中国、美国和德国的发表论文数量最多。美国是国际合作的中心。中国知网显示,长安大学、重庆交通大学等中国高校发表了大量文献。根据WOS的统计;《预防》是世界上出版最广泛的期刊。该期刊的平均水平较高,文章质量较好。结合CNKI和WOS的研究内容,利用VOSviewer中的耦合函数可以将主要研究方向聚类为五个聚类主题,包括考虑驾驶员因素的驾驶风险评估、驾驶环境对驾驶风险的影响、考虑多源特征数据的驾驶风险评估,多方面研究非传统车辆在特定场景下的驾驶风险和风险识别。人机协同驾驶、人工智能、智能驾驶、风险识别和自然驾驶是当前的研究热点和未来的研究趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Literature review of driving risk identification research based on bibliometric analysis

In order to understand the current research status and development direction of driving risk identification at home and abroad, relevant literatures in the field of driving risk identification from the China National Knowledge Infra-structure (CNKI) and Web of Science (WOS) in recent 12 years (2011–2022) were selected as research samples, and literature metrology tools VOSviewer and Citespace were used for visual analysis. The situation was analyzed from the aspects of chronological distribution, national cooperation network, distribution of domestic institutions, journal performance and keywords overview, literature coupling clustering and research hotspots. The results show that the number of published papers fluctuates year by year, and China, the United States and Germany have the largest number of published papers. The United States is at the center of international cooperation. The CNKI shows that universities in China such as Chang'an University and Chongqing Jiaotong University have published a large number of documents. According to the statistics of WOS, Accident Analysis & Prevention is the most widely published journal in the world. The average level of the journal is high and the quality of articles is better. Combining the research contents of CNKI and WOS, the main research directions can be clustered into five cluster themes by using the coupling function in VOSviewer, including driving risk assessment considering driver factors, the influence of driving environment on driving risk, driving risk assessment considering multi-source characteristic data, multi-aspect research on driving risk and risk identification of non-traditional vehicles in specific scenarios. Human-machine co-driving, artificial intelligence, intelligent driving, risk identification and natural driving are the current research hotspots and the future research trends.

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来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
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