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The article provides a detailed analysis of the conceptual and practical features of integrating Intelligent Transportation Systems (ITS) into urban environments, with a focus on the use of Internet of Things (IoT), Artificial Intelligence (AI), and edge computing technologies. A conceptual model of ITS has been developed, enabling not only real-time collection and processing of sensor data but also dynamic decision-making based on big data analytics. The multi-level architecture of ITS is examined, employing modern optimization, prediction, and clustering algorithms to enhance traffic management adaptability, minimize congestion, and reduce CO₂ emissions. Examples of successful ITS implementations in leading global cities are presented, showcasing their positive impact on increasing traffic throughput, reducing accident rates, and improving environmental conditions. Particular attention is paid to cybersecurity issues, which are critical for the stable and reliable operation of ITS. Potential threats associated with unauthorized access to system resources are analyzed, and the implementation of advanced encryption mechanisms, multi-factor authentication, and blockchain technologies is proposed to ensure data integrity and confidentiality. The article also highlights the development of effective anomaly detection algorithms capable of promptly responding to non-standard situations, such as traffic accidents or sudden changes in traffic flows. This ensures system resilience and flexibility in dynamic urban environments. The prospects for further ITS development through deeper integration of edge computing, Big Data technologies, and AI are emphasized, contributing to enhanced overall efficiency, safety, and adaptability of urban transportation infrastructure. The proposed ITS model incorporates energy efficiency, enables highly accurate traffic flow prediction, and ensures environmental sustainability in urbanized spaces. Practical recommendations for implementing the developed ITS are provided, emphasizing its ability to evolve and adapt to changes in traffic intensity, infrastructural constraints, and environmental safety requirements.
Довженко et al. (Mon,) studied this question.