About the authors
| First name, Middle name, Last name, Scientific degree, Scientific rank, Current position. Full and brief name of the organization, The organization address. | Vladimir V. Mokshin, Candidate of Technical Sciences, Associate Professor, Department of Automated Information Processing and Control Systems, Kazan National Research Technical University named after A.N. Tupolev – KAI, Kazan, Russian Federation; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it , ORCID: 0000-0002-7650-3419. Alexander A. Vaniushev, Software Engineer, SMP-Neftegaz JSC, Almetyevsk, Russian Federation; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it . Lenar F. Mavliev, Candidate of Technical Sciences, Associate Professor, Kazan State University of Architecture and Civil Engineering, Kazan, Russian Federation; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it , ORCID: 0000-0001-6301-0941. Regina V. Nikolaeva, Candidate of technical sciences, associate professor, Kazan State University of Architecture and Engineering, Kazan, Russian Federation; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it , ORCID: 0000-0002-5324-432х. |
| Title of the article | Video analytics and big data analysis technologies in the development of intelligent transport systems |
| Abstract. | Problem Statement. Transportation plays a key role in the economy and quality of life. Intelligent transportation systems (ITS), which collect data through video surveillance, are used to optimize transportation networks. Video analytics and artificial intelligence make transportation safer and more efficient. The goal of this study is to summarize the operating principles of ITS with the integration of video analytics and Big Data. Objectives: analyze existing ITS systems using these technologies and determine the technological basis for their development. Results. Successful examples of ITS implementation in various countries are considered, confirming their effectiveness. The integration of Big Data, machine learning, and computer vision significantly improves road infrastructure management. The experiment compared the Faster R-CNN, YOLOv8s, and YOLOv9s detection models in terms of accuracy, recall, speed, and resource consumption. Faster R-CNN is accurate but inferior to YOLO in speed and compactness. YOLOv8s demonstrated the best balance of quality and performance. YOLOv9s is promising for devices with limited resources. The technological foundation of the ITS has been established: high-resolution video surveillance, computer vision algorithms, big data and real-time processing, compatibility with other technologies, participant behavior analysis, decision making, privacy protection, and cybersecurity. Conclusions. The significance of this work lies in optimizing transportation network performance. The results highlight the importance of an integrated approach combining technical solutions, data analysis, and user services to create a resilient, secure, and highly efficient transportation network. |
| Keywords. | transport network, intelligent transport systems, traffic flows, video analytics, big data, computer vision, machine learning, highways, Big Data |
| For citations: | Mokshin V.V., Vaniushev A.A., Mavliev L.F., Nikolaeva R.V. Video analytics and big data analysis technologies in the development of intelligent transport systems // News of KSUAE, 2026, № 1 (75), p. 376-394, DOI: 10.48612/NewsKSUAE/75.31, EDN: UNTRBI |
