In urban environments, traffic congestion remains a pervasive challenge, significantly impacting both the economy and quality of life. This issue is addressed in this paper by leveraging Reinforcement Learning (RL) techniques to optimize traffic signal timings at intersections. The objective is to minimize congestion and enhance overall traffic flow. This involves the creation of a simulation environment modelling real-world traffic scenarios. Within this framework, an RL agent is trained to learn optimal signal timings by iteratively interacting with simulated traffic conditions. This proposed work evaluates the trained model across various scenarios, assessing its adaptability to changing traffic conditions and its effectiveness in minimizing congestion. Results from simulations will demonstrate the potential of RL-based traffic signal optimization in significantly improving traffic flow, reducing congestion-related delays, and enhancing the overall efficiency of urban transportation systems. The findings contribute to the ongoing discourse on intelligent traffic management and underscore the applicability of RL techniques in addressing complex real- world challenges.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Smart Traffic Control: Leveraging DQN for Improved Urban Mobility


    Beteiligte:


    Erscheinungsdatum :

    02.08.2024


    Format / Umfang :

    576546 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AI Based Traffic Flow Prediction for Smart Urban Mobility

    Bhosale, Ashwini / Shaikh, Arshiya / Kamble, Arpita et al. | IEEE | 2024



    Automated Traffic Control for Sustainable Urban Mobility

    Sudhakaran, Pradeep / C R, Koushik / George, Josh G | IEEE | 2024


    Smart Urban Mobility: When Mobility Systems Meet Smart Data

    Mahrez, Zineb / Sabir, Essaid / Badidi, Elarbi et al. | IEEE | 2022


    Traffic Management for Urban Air Mobility

    Suda Bharadwaj / Steven Carr / Natasha Neogi et al. | NTRS