Traffic congestion in freeways poses significant challenges, impacting travel times and environmental sustainability. This paper proposes a novel approach to enhance ramp metering control using predictive traffic insights derived from physics-informed LSTM (Long Short-Term Memory) models. By integrating predictive capabilities with established control strategies like ALINEA, the method dynamically adjusts on-ramp flow rates based on anticipated traffic conditions. Real-world traffic data are used to evaluate the effectiveness of the approach, demonstrating improved performance compared to the adoption of conventional controllers.


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    Title :

    AI-Based Predictive Ramp-Metering Control for Freeway Traffic Systems


    Contributors:
    Binjaku, K. (author) / Mece, E. K. (author) / Pasquale, C. (author) / Siri, S. (author) / Sacone, S. (author)


    Publication date :

    2024-09-24


    Size :

    839469 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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