用大模型辅助信号灯控制，提升动态路况应对能力。
LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support
- 结合LSTM预测交通状态，再用大模型分析并推荐信号相位调整。
- 在多种交通场景下，通行效率显著提升且零安全违规。
- 适合智能交通系统研发者与城市交通管理者参考。
交通信号控制是智能交通系统中的关键任务，传统固定时序和规则方法难以适应动态交通需求，且决策过程缺乏可解释性。本文提出一种大语言模型（LLM）增强的交通信号控制框架，融合基于LSTM的短期交通状态预测、预测性相位选择、结构化大语言模型推理以及安全约束动作过滤机制。LSTM模块基于近期路口级观测数据，预测未来队列长度、等待时间、车辆数和车道占用率。预测控制器生成候选信号动作，大模型模块则利用结构化交通状态输入评估这些动作，输出拥堵诊断、相位调整建议及自然语言解释。为保障运行可靠性，所有大模型生成的建议经安全过滤器验证后才执行。基于SUMO的仿真实验在均衡需求、单向高峰和突发激增等场景下对比了该方法与固定时序控制、规则控制及基于LSTM的预测基线。结果表明，所提框架在动态和非重复性交通条件下显著提升交通效率，且经安全过滤后实现零约束违规。研究证明，将大模型作为受约束的推理与决策支持模块，而非直接低层控制器，能有效增强交通信号控制性能。
原文摘要 · Abstract (English)
Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to adapt to dynamic traffic demand and provide limited decision interpretability. This study proposes an LLM-augmented traffic signal control framework that integrates LSTM-based short-term traffic state prediction, predictive phase selection, structured large language model reasoning, and safety-constrained action filtering. The LSTM module forecasts future queue length, waiting time, vehicle count, and lane occupancy based on recent intersection-level observations. A predictive controller then generates candidate signal actions, while the LLM module evaluates these actions using structured traffic-state inputs and produces congestion diagnoses, phase adjustment recommendations, and natural-language explanations. To ensure operational reliability, all LLM-generated recommendations are validated by a safety filter before execution. Simulation-based experiments in SUMO compare the proposed method with fixed-time control, rule-based control, and an LSTM-based predictive baseline under balanced demand, directional peak demand, and sudden surge scenarios. The results indicate that the proposed framework improves traffic efficiency, especially under dynamic and non-recurrent traffic conditions, while maintaining zero constraint violations after safety filtering. Overall, this study demonstrates that LLMs can enhance traffic signal control when used as constrained reasoning and decision-support modules rather than direct low-level controllers. Keywords: Intelligent Transportation Systems; Traffic Signal Control; Large Language Models; LSTM; Traffic State Prediction; Decision Support; Safety-Constrained Control; SUMO Simulation.
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