Rapid population growth, urbanization and increasing mobility demands continue to pose serious challenges for urban transportation systems. Traffic congestion, high costs of transportation, and increased CO2 emission levels contribute to climate change impacts, not only that but also lower user satisfaction.

    This paper introduces a mathematical model (TCES). The model leverages Pareto optimization and linear programming to simultaneously minimize two major conflicting objectives: travel time, and cost emissions (Pollution) while maximizing user satisfaction. The model takes into account the factors which influence the transportation sector, such as passenger demand, real-time traffic data and environmental considerations. This makes the model a scalable and resilient solution to modern transportation, that can be applied in carpooling, ridesharing and city planning.


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    Title :

    A Green Intelligent Transport Model for Urban Mobility


    Additional title:

    Communic.Comp.Inf.Science


    Contributors:

    Conference:

    International Symposium on Intelligent Technology for Future Transportation ; 2024 ; Helsinki, Finland October 20, 2024 - October 22, 2024



    Publication date :

    2025-03-12


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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