Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks


    Beteiligte:
    Bohra, Asha Rani (Autor:in) / Selvi, Thamarai (Autor:in) / Vishal, V (Autor:in) / Sunkara, Pawan Sai (Autor:in) / Sunku, Balaji (Autor:in) / Jignesh, Bhavsar Nitya (Autor:in)


    Erscheinungsdatum :

    21.02.2025


    Format / Umfang :

    369169 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Adaptive Traffic Signal Control System Using Deep Reinforcement Learning

    Agrawal, Satyam / Sharma, Ritvij / Srivastava, Pankaj et al. | IEEE | 2024


    Adaptive Traffic Control Using Deep Reinforcement Learning

    N, Dhashyanth / R, Hemchand / R, Priyanga et al. | IEEE | 2024


    Deep Learning vs. Discrete Reinforcement Learning for Adaptive Traffic Signal Control

    Shabestary, Soheil Mohamad Alizadeh / Abdulhai, Baher | IEEE | 2018


    RA-TSC: Learning Adaptive Traffic Signal Control Strategy via Deep Reinforcement Learning

    Du, Yu / ShangGuan, Wei / Rong, Dingchao et al. | IEEE | 2019


    Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning

    Jamil, Abu Rafe Md / Ganguly, Kishan Kumar / Nower, Naushin | Wiley | 2020

    Freier Zugriff