论文标题
动态光：两阶段动态流量信号正时
DynamicLight: Two-Stage Dynamic Traffic Signal Timing
论文作者
论文摘要
增强学习（RL）正在流行，作为交通信号控制（TSC）的有效方法，并越来越多地应用于该领域。但是，大多数现有的RL方法都局限于单阶段的TSC框架，主要集中于在固定的动作间隔下选择适当的交通信号阶段，从而导致不灵活且适应性较低的相位持续时间。为了解决此类局限性，我们介绍了一个名为Dynamiclight的新颖的两阶段TSC框架。该框架以负责确定最佳交通阶段的相位控制策略的启动，然后是确定相应相位持续时间的持续时间控制策略。实验结果表明，动态光优于最先进的TSC模型，并且展示了出色的模型泛化功能。此外，通过各种Dynamiclight变体进一步证明和验证了动态光的现实实现的鲁棒性和潜力。该代码在https://github.com/liangzhang1996/dynamiclight上发布。
Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain. However, most existing RL methodologies are confined to a single-stage TSC framework, primarily focusing on selecting an appropriate traffic signal phase at fixed action intervals, leading to inflexible and less adaptable phase durations. To address such limitations, we introduce a novel two-stage TSC framework named DynamicLight. This framework initiates with a phase control strategy responsible for determining the optimal traffic phase, followed by a duration control strategy tasked with determining the corresponding phase duration. Experimental results show that DynamicLight outperforms state-of-the-art TSC models and exhibits exceptional model generalization capabilities. Additionally, the robustness and potential for real-world implementation of DynamicLight are further demonstrated and validated through various DynamicLight variants. The code is released at https://github.com/LiangZhang1996/DynamicLight.
