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

Junkai Zhang, Jun Wang (), Haoyu Zang, Ning Ma, Martin Skitmore, Ziyi Qu, Greg Skulmoski and Jianli Chen
Additional contact information
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

Sustainability, 2024, vol. 16, issue 14, 1-34

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.

Keywords: machine learning; deep learning; intelligent transportation; scientometric analysis; qualitative review (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/14/5879/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/14/5879/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:14:p:5879-:d:1432435

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().