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Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

Author

Listed:
  • Wei Ji

    (Key Laboratory of Evidence Science, Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Ministry of Education, Beijing 100088, China)

  • Shengnan Yu

    (Key Laboratory of Evidence Science, Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Ministry of Education, Beijing 100088, China)

  • Zefang Shen

    (Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing 100192, China)

  • Min Wang

    (Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing 100192, China)

  • Gang Cheng

    (Key Laboratory of Evidence Science, Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Ministry of Education, Beijing 100088, China)

  • Tiantong Yang

    (Key Laboratory of Evidence Science, Fada Institute of Forensic Medicine & Science, China University of Political Science and Law, Ministry of Education, Beijing 100088, China)

  • Quan Yuan

    (School of Vehicle & Mobility, Tsinghua University, Beijing 100084, China)

Abstract

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Suggested Citation

  • Wei Ji & Shengnan Yu & Zefang Shen & Min Wang & Gang Cheng & Tiantong Yang & Quan Yuan, 2023. "Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue," Sustainability, MDPI, vol. 15(15), pages 1-32, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:12003-:d:1210689
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    References listed on IDEAS

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    1. Tian Guan & Yi Han & Nan Kang & Ningye Tang & Xu Chen & Shu Wang, 2022. "An Overview of Vehicular Cybersecurity for Intelligent Connected Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    2. Biyao Wang & Yi Han & Siyu Wang & Di Tian & Mengjiao Cai & Ming Liu & Lujia Wang, 2022. "A Review of Intelligent Connected Vehicle Cooperative Driving Development," Mathematics, MDPI, vol. 10(19), pages 1-31, October.
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    Cited by:

    1. Aleksandra Mach & Daniel Wałach, 2024. "Implementation of Integrated Life Cycle Design Principles in Ground Improvement and Piling Methods—A Review," Sustainability, MDPI, vol. 16(2), pages 1-24, January.

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