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Trend analysis of machine learning techniques for traffic control based on bibliometrics

IAES International Journal of Artificial Intelligence · Vol. 14 No. 3 (2025) · hlm. 2402–2411

DOI
10.11591/ijai.v14.i3.pp2402-2411
ISSN
2252-8938, 2089-4872
Terbit
2025-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.

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  • National Research and Innovation Agency
  • Universitas Gadjah Mada
  • Badan Pengkajian dan Penerapan Teknologi
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Rekan penulisOrganisasiArtikel bersama
Syamsul KamarAfiliasi tidak diketahui5
Meiyanne LestariAfiliasi tidak diketahui5
Sahid BismantokoNational Research and Innovation Agency3
Eko Syamsuddin HasritoAfiliasi tidak diketahui3
Heru BasukiAfiliasi tidak diketahui3
Winarno WinarnoAfiliasi tidak diketahui2
Djoko Wahyu KarmiadjiBadan Pengkajian dan Penerapan Teknologi2
M. RosyidiAfiliasi tidak diketahui2
Harris ZaenalAfiliasi tidak diketahui2
Nofriyadi NurdamAfiliasi tidak diketahui2
Andi M. KadirAfiliasi tidak diketahui2
Nurhadi PramanaAfiliasi tidak diketahui2
Fauzi Dwi SetiawanAfiliasi tidak diketahui2
Respatya Teguh SoewonoAfiliasi tidak diketahui2
Okghi Adam QowiyAfiliasi tidak diketahui2
Mulyadi Sinung HarjonoAfiliasi tidak diketahui2
Tri Wahyu WidodoNational Research and Innovation Agency1
Umi ChasanahNational Research and Innovation Agency1
Gilang Mantara PutraNational Research and Innovation Agency1
Mohammad RosyidiNational Research and Innovation Agency1
Joko WidodoAfiliasi tidak diketahui1
DYP SugihartoAfiliasi tidak diketahui1
Sri Rejeki RetnaningdyastutiAfiliasi tidak diketahui1
Iswardono S. PermonoUniversitas Gadjah Mada1
Muhammad F. AlfarisyAfiliasi tidak diketahui1
Nopirin NopirinAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Trend analysis of machine learning techniques for traffic control based on bibliometrics</dc:title>
	<dc:creator>Luthfiyah, Hilda</dc:creator>
	<dc:creator>Syamsuddin Hasrito, Eko</dc:creator>
	<dc:creator>Widodo, Tri</dc:creator>
	<dc:creator>Hidayat, Sofwan</dc:creator>
	<dc:creator>Adam Qowiy, Okghi</dc:creator>
	<dc:subject xml:lang="en-US">Intelligent transportation system; Traffic control;  Machine learning</dc:subject>
	<dc:subject xml:lang="en-US">Bibliometric analysis; Intelligent transportation system; Machine learning; Scopus database; Traffic control</dc:subject>
	<dc:description xml:lang="en-US">Machine learning in traffic control for intelligent transportation systems (ML-ITSTC) aims to enhance user coordination and safety within transportation networks, ultimately improving overall traffic system performance. ML-ITSTC is achieved by leveraging data to execute machine learning algorithms in intelligent transportation management and optimizing traffic flow to prevent or reduce congestion. This paper conducts bibliometric analysis to explain the research status, development trajectory, and challenges of ML-ITSTC, drawing insights from literature in the Scopus database literature covering 2013 to November 2023. The bibliometric analysis of ML-ITSTC includes: performance analysis, science mapping analysis, and citation analysis. The evaluation of ML algorithm trends over the 10-year span indicates that traffic prediction (TP), neural networks, and deep learning are frequently used keywords. Further, an examination of keywords used over the entire period and in 2023 (up to November) shows that reinforcement learning (RL) is the latest popular approach for traffic control in transportation. The results provide a comprehensive view of the opportunities and challenges in ML-ITSTC, covering data, models, and applications, offering researchers insights into the current and future directions of ML-ITSTC research.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Research Center for Transportation Technology, National Research and Innovation Agency (BRIN)</dc:contributor>
	<dc:date>2025-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/25092</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i3.pp2402-2411</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025; 2402-2411</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/25092/14592</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>