Research explores the concept of a Digital Control Tower (DCT), defining it as a centralized, data-driven system that integrates real-time information from various sources to monitor, control, and optimize operations. The study delves into different kinds and functions of DCTs, particularly focusing on their applications in transportation systems. Specifically, we explore their role in managing public transport systems, such as city bus routes and networks, where DCTs ensure efficient route management, dynamic scheduling, and real-time decision-making to enhance service reliability. We also investigate the mathematical approaches used to solve the complexities of managing public bus networks. Optimization algorithms, including linear programming, dynamic programming, and machine learning models, are employed to minimize operational costs and improve service performance. Reinforcement learning algorithms are applied to dynamically adjust bus routes and schedules in real-time, while predictive maintenance models are used to reduce vehicle downtime and enhance fleet management. This paper describes a mathematical model for a city public bus transport network that integrates real-time data and predictive analytics. The proposed model demonstrates how a DCT can optimize public transportation services by efficiently solving challenges such as traffic congestion, route planning, and resource allocation. Finally, we suggest potential areas for further research, including advanced AI techniques and the integration of autonomous vehicles into city transport networks, to further improve the efficiency and sustainability of public transportation systems.