The application of digital twins (DTs) and artificial intelligence (AI) in public transportation has significantly improved traffic management and efficiency. Techniques such as agent-based modelling, reinforcement learning, and multi-agent systems have been used to dynamically adjust traffic signals and reroute vehicles, reducing congestion and improving traffic flow. Additionally, DT-centric approaches for driver intention prediction and adaptive multi-agent networks have shown potential in managing large-scale IoT systems. This study investigates the integration of DT standards and advanced AI methods, such as multi-agent systems and predictive models, to enhance the decision-making processes in the TransMilenio transportation system. The findings demonstrate that the model proposed can reduce the waiting time of passengers within the system.