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Application of Digital Twins and Building Information Modeling in the Digitization of Transportation: A Bibliometric Review

Authors :
Chao Gao
Jianwei Wang
Shi Dong
Zhizhen Liu
Zhiwei Cui
Ningyuan Ma
Xiyang Zhao
Source :
Applied Sciences, Vol 12, Iss 21, p 11203 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
21
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.31a93557ea4d4472a5c1e2bf2d04fd18
Document Type :
article
Full Text :
https://doi.org/10.3390/app122111203