THEORETICAL FOUNDATIONS AND IMPLEMENTATION OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE AND COMPUTER VISION TECHNOLOGIES IN AUTOMATING METROPOLITAN ESCALATORS.
Keywords:
artificial intelligence, computer vision, deep learning, YOLO, metropolitan, escalator,Abstract
This study examines real-time monitoring and automatic management of passenger flow on metropolitan escalators using artificial intelligence and computer vision technologies. The aim is to develop the conceptual foundations of an automatic hazard detection system based on deep learning algorithms (CNN, YOLO, transformer). Systematic literature analysis based on PRISMA principles, comparati ve analysis, and conceptual modeling were employed, with 25 international sources analyzed.
Downloads
References
1. The number of passengers transported on the Tashkent metro increased by 20 million in 2025 // Gazeta.uz. — 2026.01.13. — URL: https://www.gazeta.uz/oz/2026/01/13/metro/
2. Passenger flow on the Tashkent metro increased by 57% in 2024 // Gazeta.uz. — 2025.01.27.
3. How does the Tashkent Metropolitan plan to solve the congestion problem? // Gazeta.uz. — 2024.10.14.
4. Sari M.I., Ibnugraha P.D. et al. Initial Estimation Method for Public Transport Passenger Density // IEEE Proc. — 2023. — DOI: 10.1109/GCAT59970.2023.10353287.
5. Distributed Deep Learning System for Real-Time Passenger Flow Measurement // Applied Intelligence (Springer). — 2025. — DOI: 10.1007/s10489-025-06954-9.



















