.png){kind=icon}
返回
Extensible prototype learning for real-time traffic signal control
.png){kind=icon}
PRE
AI
DOI：10.1111/mice.12955.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Congestion resolution continues to remain a challenge even though various signal control systems have been developed for traffic-intersection control. To address this issue, reinforcement learning (RL)-based approaches that focus on solving the associated data-driven problems have been proposed. However, only a few methods have been developed and applied to dual-ring traffic signal control systems. Therefore, we develop an RL-based traffic signal control model for such a system to efficiently allocate the green interval in different oversaturation states of the conflicting phases. The proposed model employs a deep deterministic policy gradient algorithm to optimize the green value in the continuous action space. Further, we develop an extensible prototype learning framework for application to new intersections without additional transfer learning. The proposed model is validated based on morning peak hours in a simulation environment that reflects the actual intersection phase system and minimum green time constraints. The proposed model achieves an average 20% intersection delay reduction, compared with the fixed control method.
Keyword:
NETWORK
METHODOLOGY
期刊
C
IF:
9.1
论文数:
2.0K
被引数:
10.0K
机构
引用论文
Defect-related photoluminescence properties of as-synthesized and annealed NiO nanostructures via hydrothermal method通过水热法合成和退火的NiO纳米结构的缺陷相关的光致发光特性
.png){kind=icon}
PREAI
分享收藏
A Deep Reinforcement Learning Approach to Traffic Signal Control With Temporal Traffic Pattern Mining基于时序交通模式挖掘的交通信号控制深度强化学习方法
PREAI
分享收藏分享收藏Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning基于协作组的多agent强化学习的大规模场景自适应交通信号控制
PREAI
分享收藏
查看更多内容