In traffic signal control (TSC), Deep Reinforcement Learning (DRL) has demonstrated its superiority to traditional approaches. However, there remain two challenges in DRL-based traffic signal control. How to cooperatively control decentralized intersection agents and how to apply TSC methods to the real world are still open. To address the aforementioned challenges, we propose a novel DRL-based strategy that optimizes the phase split for each intersection. We prioritize each intersection for signal control and adjust its phase split in accordance with its assigned priority. To be more specific, we start by optimizing the phase split of the intersection with the highest priority and then control the intersection with the following priority based on the outcome from the first intersection. In this manner, the coordinated optimization is taken into account. Additionally, we maintain the pre-defined order and definition of phases, and a fixed cycle time during the optimization process to support practical application. The proposed method yields an improvement in the network efficiency, ranging from 8–16 %, compared to the traditional method when tested in real urban network scenarios. This study also tests the impact of the control priority on the model performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prioritized Phase Split Optimization for Coordinated Traffic Signal Control in Urban Network Using Deep Reinforcement Learning


    Contributors:
    Kim, Hyunsoo (author) / Jin, Zhixiong (author) / Tak, Hyeyoung (author) / Yu, Hwapyeong (author) / Yeo, Hwasoo (author)


    Publication date :

    2023-09-24


    Size :

    1467407 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Incremental Reinforcement Learning with Prioritized Sweeping for Traffic Signal Control

    Rattan, Gurseerat Kaur / Mao, Zhenyu / Li, Jialong et al. | IEEE | 2023


    Urban traffic signal control using reinforcement learning agents

    Balaji, P.G. / German, X. / Srinivasan, D. | IET | 2010



    Deep Reinforcement Learning-based Traffic Signal Control

    Ruan, Junyun / Tang, Jinzhuo / Gao, Ge et al. | IEEE | 2023


    Adaptive Traffic Signal Control System Using Deep Reinforcement Learning

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