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.


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    Title :

    AI-Powered Digital Twins for Public Transportation: A Multi-agent Model for Transmilenio in Bogota


    Additional title:

    Studies Comp.Intelligence



    Conference:

    International Workshop on Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future ; 2024 ; Augsburg, Germany September 25, 2024 - September 26, 2024



    Publication date :

    2025-07-03


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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