Veri madenciliği yöntemleri kullanılarak akıllı ulaşım sistemleri ve uygulamaları konulu çalışmaların bibliometrik analizi

Abstract

Bu çalışma, 2015-2024 yılları arasında Web of Science (WoS), Scopus ve TR Dizin veritabanlarından elde edilen verilerle Akıllı Ulaşım Sistemleri (AUS) araştırmalarının bibliyometrik bir analizini sunmaktadır. Analiz, yıllık yayınlarda %440’lık bir artış olduğunu ortaya koymuş, bu artışın yapay zeka (AI), Nesnelerin İnterneti (IoT) ve sürdürülebilirlik odaklı teknolojilerdeki gelişmelerle desteklendiği görülmüştür. Çalışmada IoT, makine öğrenimi ve akıllı şehir çözümleri gibi konuların başlıca araştırma temaları olduğu, eko-yönlendirme ve yenilenebilir enerji entegrasyonu gibi sürdürülebilirlik temalı konuların ise giderek önem kazandığı belirlenmiştir. Coğrafi analiz, Hindistan, Çin ve Amerika Birleşik Devletleri'nin önde gelen katkı sağlayıcılar olduğunu, Türkiye ve Güney Kore gibi gelişmekte olan ekonomilerin ise araştırma alanında büyüyen etkisini göstermektedir. İş birliği ağları, disiplinler arası ve uluslararası ortaklıkların önemini vurgulamakta olup, önde gelen merkezler arasında MIT, Tsinghua Üniversitesi ve Delft Teknoloji Üniversitesi bulunmaktadır. Makine öğrenimi modelleri, 2026 yılı itibarıyla yıllık yaklaşık 950 yayına ulaşılacağını öngörmektedir. Ancak, otonom araçlarla ilgili etik sorunlar, altyapı entegrasyonu ve kullanıcı merkezli tasarım eksikliği gibi zorluklar devam etmektedir. Bu çalışma, AUS'un küresel ulaşım sorunlarını çözmedeki kritik rolünü vurgulamakta ve sürdürülebilir, verimli ve adil hareketlilik sistemlerini ilerletmek için araştırmacılara, politika yapıcılara ve sektör paydaşlarına uygulanabilir öneriler sunmaktadır.

Keywords

• Akıllı ulaşım sistemleri
• Yapay Zeka (AI)
• Nesnelerin İnterneti (IoT)
• kentsel ulaşım
• Makine Öğrenimi
• Bibliyometrik Analiz
• İş Birliği Ağları

A bibliometric analysis of studies on intelligent transportation systems and applications using data mining methods

Abstract

This study conducts a bibliometric analysis of Intelligent Transportation Systems (ITS) research, using data from Web of Science (WoS), Scopus, and TR Dizin covering 2015–2024. The analysis reveals a 440% increase in annual publications, driven by advancements in artificial intelligence (AI), the Internet of Things (IoT), and sustainability-focused technologies. Dominant research themes include IoT, machine learning, and smart city solutions, with sustainability-related topics such as eco-routing and renewable energy integration gaining prominence. Geographic analysis identifies India, China, and the United States as leading contributors, while emerging economies like Turkey and South Korea are expanding their research footprints. Collaboration networks highlight interdisciplinary and international partnerships, with key hubs including MIT, Tsinghua University, and Delft University of Technology. Machine learning models predict steady growth in ITS publications, projecting approximately 950 annual outputs by 2026. Despite progress, challenges remain, including ethical concerns around autonomous vehicles, infrastructure integration, and a lack of user-centric designs. This study emphasizes the critical role of ITS in addressing global transportation challenges, providing actionable insights for researchers, policymakers, and industry stakeholders to advance sustainable, efficient, and equitable mobility systems.

Keywords

• Intelligent transportation systems
• Artificial Intelligence (AI)
• Internet of Things (IoT)
• urban transportation
• Machine Learning
• Bibliometric Analysis
• Collaboration Networks

References

1. Abraham, A., Hassanien, A.-E., & Snášel, V. (2009). Computational social network analysis: Trends, tools and research advances.
2. Bajdor, P., & Starostka-Patyk, M. (2021). Smart city: A bibliometric analysis of conceptual dimensions and areas. Energies, 14(14), 4288. https://doi.org/10.3390/en14144288
3. Chen, X., Zou, D., Cheng, G., & Xie, H. (2020). Detecting latent topics and trends in educational technology research: A topic modeling and visualization approach. Educational Technology & Society, 23(1), 129–144.
4. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.
5. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
6. Gamboa-Rosales, N. K., Celaya-Padilla, J. M., Hernandez-Gutierrez, A. L., Moreno-Baez, A., Galván-Tejada, C. E., Galván-Tejada, J. I., González-Fernández, E., Gamboa-Rosales, H., & López-Robles, J. R. (2020). Visualizing the Intellectual Structure and Evolution of Intelligent Transportation Systems: A Systematic Analysis of Research Themes and Trends. Sustainability, 12(21). https://doi.org/10.3390/su12218759
7. Guevara, L., & Cheein, F. A. (2020). The role of 5G technologies: Challenges in smart cities and intelligent transportation systems. https://doi.org/10.3390/su12166469
8. Guo, Y. M., Huang, Z. L., Guo, J., Li, H., Guo, X. R., & Nkeli, M. J. (2019). Bibliometric analysis on smart cities research. Sustainability, 11(13), 3606. https://doi.org/10.3390/su11133606

9. Ioachimescu, O. C., & Shaker, R. (2025). Translational science and related disciplines. Journal of Investigative Medicine, 73(1), 3–26.
10. Iqbal, K., Khan, M. A., Abbas, S., & Hasan, Z. (2018). Intelligent transportation system (ITS) for smart-cities using Mamdani fuzzy inference system. https://www.researchgate.net/profile/Muhammad-Khan-121/publication/323536714_Intelligent_Transportation_System_ITS_for_Smart-Cities_using_Mamdani_Fuzzy_Inference_System/links/5c0745aa92851c6ca1ff1fd8/Intelligent-Transportation-System-ITS-for-Smart-Cities-using-Mamdani-Fuzzy-Inference-System.pdf Leahey, E. (2016). From sole investigator to team scientist: Trends in the practice and study of research collaboration. Annual Review of Sociology, 42(1), 81–100.
11. Lifelo, Z., Ding, J., Ning, H., & Dhelim, S. (2024). Artificial Intelligence-Enabled Metaverse for Sustainable Smart Cities: Technologies, Applications, Challenges, and Future Directions. Electronics, 13(24), 4874.
12. Luan, H., & Tsai, C.-C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250–266.
13. Mokhtari, H., Barkhan, S., Haseli, D., & Saberi, M. K. (2020). A bibliometric analysis and visualization of the Journal of Documentation: 1945–2018. Journal of Documentation, 77(1), 69–92.
14. Mora, L., Bolici, R., & Deakin, M. (2017). The first two decades of smart-city research: A bibliometric analysis. Journal of Urban Technology, 24(1), 3–27. https://doi.org/10.1080/10630732.2017.1285123
15. Song, B., Lin, Z., Feng, C., Zhao, X., & Teng, W. (2023). Global research landscape and trends of papillary thyroid cancer therapy: a bibliometric analysis. Frontiers in Endocrinology, 14, 1252389.
16. Szum, K. (2021). IoT-based smart cities: A bibliometric analysis and literature review. Engineering Management in Production and Services, 13(3), 17–25. https://doi.org/10.2478/emj-2021-0017
17. Tomaszewska, E. J., & Florea, A. (2018). Urban smart mobility in the scientific literature — bibliometric analysis. Engineering Management in Production and Services, 10(2), 41–56. https://doi.org/doi:10.2478/emj-2018-0010 Tran, C. N. N., Tat, T. T. H., & Tam, V. W. Y. (2023). Factors affecting intelligent transport systems towards a smart city: A critical review. https://doi.org/10.1080/15623599.2022.2029680
18. Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285–320). Springer.
19. van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. Measuring Scholarly Impact, 285–320. https://doi.org/10.1007/978-3-319-10377-8_13
20. Vujković, P., Ravšelj, D., Umek, L., & Aristovnik, A. (2022). Bibliometric analysis of smart public governance research: Smart city and smart government in comparative perspective. Social Sciences, 11(7), 293. https://doi.org/10.3390/socsci11070293
21. Yang, W., Zhang, J., & Ma, R. (2020). The prediction of infectious diseases: a bibliometric analysis. International Journal of Environmental Research and Public Health, 17(17), 6218.