提出新型通信机制，让交通信号灯协同感知宏观与微观路况。
Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
- 设计双层通信模块，捕捉路口间与车道间的交通关联
- 在真实与仿真场景中提升信号控制效率，平均减少等待时间18%
- 适用于大规模城市交通网络，对复杂变道行为有良好适应性
自适应交通信号控制（ATSC）是智能交通系统中的研究热点。基于多智能体深度强化学习的区域交通信号控制（RTSC）因能在可扩展性与最优性间取得平衡而备受关注。现有方法通常将路网划分为互不重叠的区域，并对每个区域使用集中式强化学习。然而，如何实现各智能体间的有效协作仍是一个开放问题，尚无针对RTSC智能体的通信策略被系统研究。本文提出一种通信策略，用于捕捉车道间的微观交通状态关联以及路口间的宏观交通状态关联。首先通过存储转发队列系统证明了RTSC过程的马尔可夫性。在此基础上，提出两种GAT聚合通信模块——GA2-Naive和GA2-Aug，分别提取区域内及区域间的宏观与微观交通状态相关性。其中，GA2-Naive仅考虑路口内车辆移动，而GA2-Aug还引入了车辆变道行为。这两种通信模块被整合进两种先进RTSC框架——RegionLight与Regional-DRL中。实验结果表明，无论是真实还是合成场景下，两种通信模块均显著提升了原有框架性能；超参数测试进一步验证了其在大规模交通网络中的鲁棒性与潜力。
原文摘要 · Abstract (English)
Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules--GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks--RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks.
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