HiLLTS：用于可持续交通的零样本分层大语言模型引导的交通信号控制
HiLLTS: Zero-Shot Hierarchical LLM-Guided Traffic Signal Control for Sustainable Transportation
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中文总结 AI 辅助
针对传统交通信号控制策略的问题，提出HiLLTS框架，它采用分层三层架构，由中央协调代理等组成。实验表明其能改善拥堵和环境性能，相比不同基线，在减少等待时间和二氧化碳排放上效果显著，消融研究验证了大语言模型引导协调的作用。
中文摘要 AI 辅助
城市交通拥堵显著增加燃料消耗、温室气体排放和通勤延误，给现代城市带来巨大经济损失和环境危害。传统交通信号控制策略适应性各异，基于强化学习的方法在跨网络或需求模式转移时需大量再训练、精心设计奖励和大量模拟数据。为应对这些挑战，我们提出HiLLTS，一种大语言模型引导的交通信号控制框架，采用由中央协调代理、区域层和多个集群级交叉路口代理组成的分层三层架构。实验结果表明，在拥堵和环境性能方面均有持续改善。与各场景中最强的非大语言模型基线相比，HiLLTS在低拥堵场景下平均等待时间减少36.73%，高拥堵场景下减少14.71%，同时平均二氧化碳排放量分别减少7.87%和8.57%。相对于较弱基线有更大降幅。消融研究进一步验证了大语言模型引导的协调相对于基于规则控制的贡献。
英文摘要
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
发表机构
- School of Electronic Engineering, Dublin City University（都柏林城市大学电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。
