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.

中文翻译：

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用于实时交通信号控制的可扩展原型学习

尽管已经为交通路口控制开发了各种信号控制系统，但解决拥堵仍然是一个挑战。为了解决这个问题，人们提出了基于强化学习 (RL) 的方法，重点解决相关的数据驱动问题。然而，只有少数方法被开发并应用于双环交通信号控制系统。因此，我们为此类系统开发了一种基于 RL 的交通信号控制模型，以在冲突阶段的不同过饱和状态下有效地分配绿灯间隔。所提出的模型采用深度确定性策略梯度算法来优化连续动作空间中的绿色值。此外，我们开发了一个可扩展的原型学习框架，无需额外的迁移学习即可应用于新的交叉路口。所提出的模型在反映实际交叉路口相位系统和最小绿灯时间限制的模拟环境中基于早高峰时间进行了验证。与固定控制方法相比，所提出的模型平均减少了 20% 的路口延误。