Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people’s general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Light Control Using Reinforcement Learning




    Erscheinungsdatum :

    23.02.2024


    Format / Umfang :

    1240778 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep reinforcement learning traffic light control method

    KONG YAN / LI YING / CHIH-CHAO YANG | Europäisches Patentamt | 2024

    Freier Zugriff

    Deep Reinforcement Learning for Autonomous Traffic Light Control

    Garg, Deepeka / Chli, Maria / Vogiatzis, George | IEEE | 2018


    Traffic light control method based on deep reinforcement learning and inverse reinforcement learning

    ZHANG YA / GU SHIYI / CHEN GUOXI | Europäisches Patentamt | 2023

    Freier Zugriff

    Smart Traffic Light Controller using Deep Reinforcement Learning

    Abhishek, A / Nayak, Prathvik / Hegde, Krishna P et al. | IEEE | 2022


    TRAFFIC CONTROL WITH REINFORCEMENT LEARNING

    GANTI RAGHU KIRAN / SRIVASTA MUDHAKAR / RAO VENKATESH ASHOK RAO et al. | Europäisches Patentamt | 2020

    Freier Zugriff