标题： 具有 Herald 引导提示的双大型语言模型架构用于并行细粒度交通信号控制 显示英文标题

标题： A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control

摘要： 利用大型语言模型（LLMs）在交通信号控制（TSC）中相比传统的强化学习（RL）方法提高了优化效率和可解释性。 然而，现有的基于LLM的方法受限于固定时间的信号持续时间，并容易出现幻觉错误，而RL方法在信号定时决策中缺乏鲁棒性，并且泛化能力差。 为了解决这些挑战，本文提出了HeraldLight，一种由Herald引导提示增强的双LLM架构。 Herald模块根据实时条件提取上下文信息并预测每个交通阶段的队列长度。 第一个LLM，LLM-Agent，使用这些预测来进行细粒度的交通信号控制，而第二个LLM，LLM-Critic，对LLM-Agent的输出进行细化，纠正错误和幻觉。 这些细化的输出用于基于评分的微调，以提高准确性和鲁棒性。 使用CityFlow在涵盖济南（12个）、杭州（16个）和纽约（196个）共224个交叉口的真实世界数据集上进行的仿真实验表明，HeraldLight优于最先进的基线方法，在所有场景中平均行驶时间减少了20.03%，在济南和杭州场景中平均队列长度减少了10.74%。 源代码可在GitHub上获得：https://github.com/BUPT-ANTlab/HeraldLight. 显示英文摘要

摘要： Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent's outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.