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The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

Author

Listed:
  • Junkai Zhang

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Jun Wang

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Haoyu Zang

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Ning Ma

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Martin Skitmore

    (Faculty of Society and Design, Bond University, Robina, QLD 4226, Australia)

  • Ziyi Qu

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Greg Skulmoski

    (Faculty of Society and Design, Bond University, Robina, QLD 4226, Australia)

  • Jianli Chen

    (Department of Civil Engineering, University of Utah, Salt Lake City, UT 84112, USA)

Abstract

Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This paper conducts a systematic review of the intelligent transportation literature using a scientometric analysis, aiming to summarize what is already known, identify current research trends, evaluate academic impacts, and suggest future research directions. The study provides a detailed review by analyzing 113 journal articles from the Web of Science (WoS) database. It examines the growth of publications over time, explores the collaboration patterns of key contributors, such as researchers, countries, and organizations, and employs techniques such as co-authorship analysis and keyword co-occurrence analysis to delve into the publication clusters and identify emerging research topics. Nine emerging sub-topics are identified and qualitatively discussed. The outcomes include recognizing pioneering researchers in intelligent transportation for potential collaboration opportunities, identifying reliable sources of information for publishing new work, and aiding researchers in selecting the best solutions for specific problems. These findings help researchers better understand the application of ML and DL in the intelligent transportation literature and guide research policymakers and editorial boards in selecting promising research topics for further research and development.

Suggested Citation

  • Junkai Zhang & Jun Wang & Haoyu Zang & Ning Ma & Martin Skitmore & Ziyi Qu & Greg Skulmoski & Jianli Chen, 2024. "The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(14), pages 1-34, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5879-:d:1432435
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    References listed on IDEAS

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    Cited by:

    1. Shuyu Si & Yeduozi Yao & Jing Wu, 2025. "Artificial Intelligence in Urban Planning: A Bibliometric Analysis and Hotspot Prediction," Land, MDPI, vol. 14(11), pages 1-39, October.

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