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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Binjaku, K. (Autor:in) / Mece, E. K. (Autor:in) / Pasquale, C. (Autor:in) / Siri, S. (Autor:in) / Sacone, S. (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    839469 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Freeway Traffic Flow Control with Anticipative Ramp Metering

    National Research Council (U.S.) | British Library Conference Proceedings | 2005


    DHP Method for Ramp Metering of Freeway Traffic

    Dongbin Zhao, / Xuerui Bai, / Fei-Yue Wang, et al. | IEEE | 2011


    Deep Koopman Traffic Modeling for Freeway Ramp Metering

    Gu, Chuanye / Zhou, Tao / Wu, Changzhi | IEEE | 2023


    DHP Method for Ramp Metering of Freeway Traffic

    Zhao, D | Online Contents | 2011


    Freeway Optimization Utilizing Ramp Metering

    Bieberitz, J. A. / Institute of Transportation Engineers | British Library Conference Proceedings | 1994