[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123326-en":3,"doc-seo-123326-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123326,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Trend analysis of machine learning techniques for traffic control based on bibliometrics","The study performs bibliometric analysis on machine learning in traffic control for intelligent transportation systems (ML-ITSTC) using Scopus literature from 2013 to November 2023. It characterizes the research status, development trajectory, and key challenges through performance, science mapping, and citation analyses. Algorithm and keyword trends over the 10-year span highlight traffic prediction, neural networks, and deep learning, while reinforcement learning emerges as the newest popular approach in the 2023 period (to November). Results synthesize opportunities and challenges across data, models, and applications to inform current and future research directions.","Trend analysis of machine learning techniques for traffic control based on bibliometrics  \nHilda Luthfiyah, Eko Syamsuddin Hasrito, Tri Widodo, Sofwan Hidayat, Okghi Adam Qowiy  \nResearch Center for Transportation Technology, National Research and Innovation Agency, Tangerang Selatan, Indonesia  \nArticle history:  \nReceived Jan 29, 2024 Revised Nov 18, 2024 Accepted Nov 24, 2024  \nKeywords:  \nBibliometric analysis Intelligent transportation system Machine learning  \nScopus database Traffic control  \nCorresponding Author:  \nMachine 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.  \nThis is an open access article under the CC BY-SA license.  \nOkghi Adam Qowiy  \nResearch Center for Transportation Technology, National Research and Innovation Agency KST B.J. Habibie BRIN Serpong, Kota Tangerang Selatan, Banten 15314, Indonesia Email: [okghi.adam.qowiy@brin.go.id](okghi.adam.qowiy@brin.go.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIntelligent transportation systems (ITS) are built to increase safety and efficiency in transportation by addressing various traffic-related issues, such as traffic management, accident prevention, toll roads, parking systems, and pollution control [1], [2] . The advantage of ITS is its cost-effectiveness through a method of controlling traffic operations without requiring additions or modifications to existing road facilities [3], [4] . ITS combines advanced technologies such as electronic sensor technology, data transmission technology, and intelligent control technology, resulting in big data [5], [6] . However, problems with big data include data storage, data analysis, and data management. Combining diverse and big data obtained from ITS with machine learning (ML) increases accuracy and expedites data analysis [7] .  \nThe ML algorithm processes data through several stages, including regression, classification, clustering, and determining association rules [8] . It identifies relationships between features and outputs, namely labelling, patterns, and makes decisions based on particular data, involved in the transportation sector. In recent years, many papers worldwide have focused on exploring the research status of ML algorithms applied in ITS. For instance, ML is used in urban transportation to prevent vehicle congestion through predictive vehicle analysis [9], [10] . Chen et al. [11] introduced deep learning (DL) for real-time  \nvehicle counting, which can further predict traffic flow using convolutional neural network (CNN) and graph convolutional neural network (GCNN) [12], [13] . Moreover, ML can be applied on highways for vehicle trajectory prediction while line changing [14] .  \nBased on the multitude of studies about ML-ITS topics, a bib","cbCaid0AskNgXFeW","https://ap.wps.com/l/cbCaid0AskNgXFeW","pdf",890710,1,10,"English","en",105,"# Introduction\n## Intelligent transportation systems and traffic challenges\n## Machine learning methods for traffic control\n## Why bibliometric review is needed\n# Method and Data Source\n# Results: Bibliometric Analyses\n## Statistical performance\n## Bibliographic coupling visualization\n## Publication and research trends\n# Conclusion and Future Recommendations","[{\"question\":\"What is the main goal of the bibliometric study on ML-ITSTC?\",\"answer\":\"To quantitatively and visually analyze the academic landscape, research trends, and knowledge dissemination paths in ML-ITSTC.\"},{\"question\":\"Which data source and time span are used in the analysis?\",\"answer\":\"The study uses Scopus database literature covering 2013 to November 2023.\"},{\"question\":\"What bibliometric analysis types does the paper include?\",\"answer\":\"It includes performance analysis, science mapping analysis, and citation analysis.\"}]","Trend analysis of machine learning techniques for traffic control based on bibliometrics | 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