This paper explores the role of Artificial Intelligence (AI) in optimizing traffic flow management within urban environments. It examines how AI technologies, including machine learning, computer vision, and predictive analytics, contribute to real-time traffic monitoring, congestion reduction, and adaptive signal control systems. The study highlights the integration of AI with smart city infrastructure to enhance efficiency, safety, and environmental sustainability. Case studies and current implementations are analyzed to demonstrate AI's effectiveness in reducing delays and improving traffic conditions. The annotation emphasizes the transformative potential of AI in reshaping traffic systems and supporting the development of intelligent transportation networks.

AI, transport, passenger transport, public transport sector, education, system.

P. Bansal et al. Forecasting Americans' long-term adoption of connected and autonomous vehicle technologies Transport. Res. Pol. Pract. (2017)

O.Y. Edelenbosch et al. Comparing projections of industrial energy demand and greenhouse gas emissions in long-term energy models Energy (2017)

P. V. Inbanaathan и др., «Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine», Int. J. Hydrog. Energy, т. 48, вып. 99, сс. 39531–39552, дек. 2023, doi: 10.1016/j.ijhydene.2023.03.107.

F. Salek, M. Babaie, S. V. Hosseini, и O. A. Bég, «Multi-objective optimization of the engine performance and emissions for a hydrogen/gasoline dual-fuel engine equipped with the port water injection system», Int. J. Hydrog. Energy, т. 46, вып. 17, сс. 10535–10547, мар. 2021, doi: 10.1016/j.ijhydene.2020.12.139.

M. Pyrc, M. Gruca, W. Tutak, и A. Jamrozik, «Assessment of the co-combustion process of ammonia with hydrogen in a research VCR piston engine», Int. J. Hydrog. Energy, т. 48, вып. 7, сс. 2821–2834, янв. 2023, doi: 10.1016/j.ijhydene.2022.10.152.