论文标题
利用队列长度和注意力机制来增强交通信号控制优化
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization
论文作者
论文摘要
近年来，用于流量信号控制（TSC）的增强学习（RL）技术已越来越受欢迎。但是，大多数现有的基于RL的TSC方法倾向于主要关注RL模型结构，同时忽略了适当的交通状态表示的重要性。此外，一些基于RL的方法在很大程度上依赖于专家设计的流量信号阶段竞争。在本文中，我们提出了一种新的TSC方法，该方法利用队列长度作为有效的状态表示。我们提出了两种新方法：（1）最大队列长度（M-QL），这是一种基于优化的传统方法，基于队列长度的属性； （2）注意力光，一种RL模型，该模型采用自我发项机制来捕获信号相的相关性，而无需人类了解相位关系。在多个现实世界数据集上进行的全面实验证明了我们方法的有效性：（1）M-QL方法的表现优于最新的基于RL的方法； （2）注意力灯会取得新的最先进的表现； （3）我们的结果突出了适当状态表示的重要性，这与TSC方法中的神经网络设计至关重要。我们的发现对推进更有效和有效的TSC方法的发展具有重要意义。我们的代码在github（https：//github。com/liangzhang1996/Goadelight）上发布。
Reinforcement learning (RL) techniques for traffic signal control (TSC) have gained increasing popularity in recent years. However, most existing RL-based TSC methods tend to focus primarily on the RL model structure while neglecting the significance of proper traffic state representation. Furthermore, some RL-based methods heavily rely on expert-designed traffic signal phase competition. In this paper, we present a novel approach to TSC that utilizes queue length as an efficient state representation. We propose two new methods: (1) Max Queue-Length (M-QL), an optimization-based traditional method designed based on the property of queue length; and (2) AttentionLight, an RL model that employs the self-attention mechanism to capture the signal phase correlation without requiring human knowledge of phase relationships. Comprehensive experiments on multiple real-world datasets demonstrate the effectiveness of our approach: (1) the M-QL method outperforms the latest RL-based methods; (2) AttentionLight achieves a new state-of-the-art performance; and (3) our results highlight the significance of proper state representation, which is as crucial as neural network design in TSC methods. Our findings have important implications for advancing the development of more effective and efficient TSC methods. Our code is released on Github (https://github. com/LiangZhang1996/AttentionLight).
