Mapping the knowledge of traffic collision Reconstruction: A scientometric analysis in CiteSpace, VOSviewer, and SciMAT

• Published in: Science & justice Vol. 63; no. 1; pp. 19 - 37
• Main Authors: Shen, Zefang, Ji, Wei, Yu, Shengnan, Cheng, Gang, Yuan, Quan, Han, Zhengqi, Liu, Hongxia, Yang, Tiantong
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.01.2023
• Subjects: Accidents, Traffic; CiteSpace; Collision reconstruction; Humans; Research Design; Science mapping; SciMAT; Software; Technology; Traffic accidents; VOSviewer; SciMAT; CiteSpace; Science mapping; Traffic accidents; Collision reconstruction; VOSviewer
• ISSN: 1355-0306; 1876-4452
• DOI: 10.1016/j.scijus.2022.10.005

[Display omitted] •Review of the knowledge structure using CiteSpace, VOSviewer, and SciMAT.•Visualizing the evolution of traffic collision reconstruction: the field is developing.•Analyzing co-word, co-citation, and major themes in traffic collision reconstruction.•Authors and institutions need more collaboration.•Head injury and uncertainty are hotspots: interdisciplinary research is the future. 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.