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Huimin Ge, Yunyu Bo, Wenkai Zang, Lijun Zhou, Lei Dong. 2023: Literature review of driving risk identification research based on bibliometric analysis. Journal of Traffic and Transportation Engineering (English Edition), 10(4): 560-577. DOI: 10.1016/j.jtte.2023.04.001
Citation: Huimin Ge, Yunyu Bo, Wenkai Zang, Lijun Zhou, Lei Dong. 2023: Literature review of driving risk identification research based on bibliometric analysis. Journal of Traffic and Transportation Engineering (English Edition), 10(4): 560-577. DOI: 10.1016/j.jtte.2023.04.001

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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