TY - JOUR
T1 - Intelligent transportation systems (ITS)
T2 - A systematic review using a Natural Language Processing (NLP) approach
AU - Zulkarnain, null
AU - Putri, Tsarina Dwi
N1 - Funding Information:
This work was supported by the Directorate of Research and Development Universitas Indonesia (No: NKB- 1433/UN2.RST/HKP.05.00/2020 ).
Funding Information:
Authors would like to express appreciation and gratitude to Universitas Indonesia for funding this study through PUTI Q1 Research Grants Universitas Indonesia No: NKB- 1433/UN2.RST/HKP.05.00/2020. Authors would also like to thank anonymous reviewers for their valuable comments in improving this article.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/12
Y1 - 2021/12
N2 - Intelligent Transportation Systems (ITS) is not a new concept. Notably, ITS has been cited in various journal articles and proceedings papers around the world, and it has become increasingly popular. Additionally, ITS involves multidisciplinary science. The growing number of journal articles makes ITS reviews complicated, and research gaps can be difficult to identify. The existing software for systematic reviews still relies on highly laborious tasks, manual reading, and a homogeneous dataset of research articles. This study proposes a framework that can address these issues, return a comprehensive systematic review of ITS, and promote efficient systematic reviews. The proposed framework consists of Natural Language Processing (NLP) methods i.e., Named Entity Recognition (NER), Latent Dirichlet Allocation (LDA), and word embedding (continuous skip-gram). It enables this study to explore the context of research articles and their overall interpretation to determine and define the directions of knowledge growth and ITS development. The framework can systematically separate unrelated documents and simplify the review process for large dataset. To our knowledge, compared to prior research regarding systematic review of ITS, this study offers more thorough review.
AB - Intelligent Transportation Systems (ITS) is not a new concept. Notably, ITS has been cited in various journal articles and proceedings papers around the world, and it has become increasingly popular. Additionally, ITS involves multidisciplinary science. The growing number of journal articles makes ITS reviews complicated, and research gaps can be difficult to identify. The existing software for systematic reviews still relies on highly laborious tasks, manual reading, and a homogeneous dataset of research articles. This study proposes a framework that can address these issues, return a comprehensive systematic review of ITS, and promote efficient systematic reviews. The proposed framework consists of Natural Language Processing (NLP) methods i.e., Named Entity Recognition (NER), Latent Dirichlet Allocation (LDA), and word embedding (continuous skip-gram). It enables this study to explore the context of research articles and their overall interpretation to determine and define the directions of knowledge growth and ITS development. The framework can systematically separate unrelated documents and simplify the review process for large dataset. To our knowledge, compared to prior research regarding systematic review of ITS, this study offers more thorough review.
KW - Continuous skip-gram
KW - Custom named entity recognition
KW - Intelligent transportation system
KW - Latent dirichlet allocation
KW - Natural language processing
KW - Systematic review
KW - Word embedding
UR - http://www.scopus.com/inward/record.url?scp=85121444473&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2021.e08615
DO - 10.1016/j.heliyon.2021.e08615
M3 - Article
AN - SCOPUS:85121444473
SN - 2405-8440
VL - 7
JO - Heliyon
JF - Heliyon
IS - 12
M1 - e08615
ER -