This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics.

翻译：本研究提出了一种交通控制系统的新方法，即使用大型语言模型（LLMs）作为交通控制器。该研究利用其逻辑推理、场景理解和决策能力，以实时优化通行能力并根据交通状况提供反馈。LLMs将传统上分散的交通控制流程集中化，并能整合来自不同来源的交通数据，以提供情境感知的决策。LLMs还可以通过无线信号和视觉等多种方式，向驾驶员、基础设施和自动驾驶车辆提供定制化的输出。为了评估LLMs作为交通控制器的能力，本研究提出了一种四阶段方法论。该方法包括数据创建与环境初始化、提示工程、冲突识别以及微调。我们模拟了多车道四岔路口场景，并生成了详细的数据集，以便使用LLMs和Python仿真作为基准真值进行冲突检测。我们采用思维链提示引导LLMs理解情境、检测冲突、运用交通规则解决冲突，并提供情境敏感的交通管理解决方案。我们评估了GPT-mini、Gemini和Llama作为交通控制器的性能。结果显示，经过微调的GPT-mini达到了83%的准确率和0.84的F1分数。GPT-mini模型在生成可操作的交通管理见解方面表现出色，其在冲突识别、决策制定、优先级分配和等待时间优化方面的ROUGE-L得分分别为0.95、0.91、0.94和0.92。我们证明，LLMs能够根据车辆动态，实时向驾驶员提供精确的建议，包括让行、减速或停车。