用大模型指导交通信号，零样本适配不同路况，显著降低拥堵和排放。
HiLLTS: Zero-Shot Hierarchical LLM-Guided Traffic Signal Control for Sustainable Transportation
- 分层架构结合大模型决策，实现跨场景零样本交通控制。
- 低拥堵下等待时间降62.07%，高拥堵下减少40.36%，碳排放最多降28.89%。
- 无需重训，适合快速部署于真实城市交通网络。
城市交通拥堵导致燃油消耗、温室气体排放和通勤延误显著上升，造成巨大经济与环境损失。传统信号控制方法如固定时序、感应控制及强化学习（RL）存在适应性差、需大量重训与仿真数据等问题。为此，本文提出HiLLTS，一种基于大语言模型的分层交通信号控制框架，包含中央协调代理、区域层与多集群路口代理。实验表明，相较各场景最强非LLM基线，HiLLTS在低拥堵下平均等待时间减少36.73%，碳排放降低7.87%；高拥堵下分别减少14.71%与8.57%。对比弱基线：低拥堵时，等待时间最多下降62.07%，碳排放降18.00%；高拥堵时，等待时间降40.36%，碳排放降28.89%。消融实验验证了大模型协同优于规则控制。
原文摘要 · Abstract (English)
Urban traffic congestion significantly increases fuel consumption, greenhouse gas emissions, and commuter delays, resulting in substantial economic losses and environmental harm in modern cities. Traditional traffic signal control strategies such as fixed-time scheduling, actuated control, and reinforcement learning (RL)-based methods, offer different degrees of adaptability; however, RL-based methods can require extensive retraining, careful reward design, and substantial simulation data when transferred across networks or demand regimes. To address these challenges, we propose HiLLTS, an LLM-guided traffic signal control framework that employs a hierarchical three-layer architecture consisting of a central coordination agent, a district layer and multiple cluster-level intersection agents. Experimental results demonstrate consistent improvements in both congestion and environmental performance. Compared with the strongest non-LLM baseline in each scenario, HiLLTS reduces average waiting time by 36.73% under the low-congestion scenario and 14.71% under the high-congestion scenario, while reducing average CO2 emissions by 7.87% and 8.57%, respectively. Larger gains are observed against weaker baselines: under low congestion, HiLLTS achieves reductions of up to 18.00% in emissions and 62.07% in waiting time relative to Fixed-Time control; under high congestion, reductions of up to 28.89% in emissions and 40.36% in waiting time are observed relative to Max Pressure. The ablation study further validates the contribution of LLM-guided coordination over rule-based control
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