Shen Zefang, Ji Wei, Yu Shengnan, Cheng Gang, Yuan Quan, Han Zhengqi, Liu Hongxia, Yang Tiantong
China University of Political Science and Law, Beijing 100088, China.
Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Beijing 100088, China.
Sci Justice. 2023 Jan;63(1):19-37. doi: 10.1016/j.scijus.2022.10.005. Epub 2022 Nov 2.
Traffic collisions are incidents with high fatality rate which generate billions of US dollars of loss worldwide each year. Post-collision scene reconstruction, which involves knowledge of multiple disciplines, is an important approach to restore the traffic collision and infer the cause of it. This paper uses software CiteSpace, VOSviewer, and SciMAT to conduct a visualization study of knowledge mapping on the literature of traffic collision scene reconstruction from 2001 to 2021 based on the Web of Science database. Knowledge mapping is a cutting-edge research method in scientometric, which has been widely applied in medicine and informatics. Compared with traditional literature review, knowledge mapping with visual techniques identifies hot keywords and key literature in the field more scientifically, and displays them in schematic diagrams intuitively which allows to further predict potential hotspots. A total of 803 original papers are retrieved to analyze and discuss the evolution of the field in the past 20 years, from macro to micro, in term of background information, popular themes, and knowledge structure. Results indicate the number of publications in this field is limited, and collaborations among authors and among institutions are insufficient. In the meantime, mappings imply the top three hot themes being scene reconstruction, computer technology, and injuries. The introduction of AI related technologies, such as neural networks and genetic algorithms, into collision reconstruction would be a potential research direction.
交通事故是死亡率很高的事件，每年在全球造成数十亿美元的损失。碰撞后现场重建涉及多学科知识，是还原交通事故并推断其原因的重要途径。本文基于Web of Science数据库，使用CiteSpace、VOSviewer和SciMAT软件对2001年至2021年交通事故现场重建文献进行知识图谱可视化研究。知识图谱是科学计量学中的一种前沿研究方法，已在医学和信息学中广泛应用。与传统文献综述相比，运用可视化技术的知识图谱能更科学地识别该领域的热点关键词和关键文献，并以示意图直观展示，进而预测潜在热点。共检索到803篇原创论文，从宏观到微观，就背景信息、热门主题和知识结构等方面分析和探讨该领域在过去20年的发展演变。结果表明该领域的出版物数量有限，作者之间以及机构之间的合作不足。同时，图谱显示排名前三的热门主题是现场重建、计算机技术和损伤。将神经网络和遗传算法等人工智能相关技术引入碰撞重建将是一个潜在的研究方向。
