用数字孪生与智能体AI实现交通灯实时自适应调控
Autonomous Traffic Signal Optimization Using Digital Twin and Agentic AI for Real-Time Decision-Making
- 构建交通数字孪生，通过传感器与边缘计算实时感知路况
- 相比固定时序和强化学习方案，平均等待时间显著降低
- 适合智慧交通、城市治理领域研究者参考
本文提出一种基于交通基础设施数字孪生的交通灯优化框架，由智能体AI驱动实现实时自主决策。系统采用三层架构：感知层采集物理系统数据；概念层利用LangChain处理信息；执行层通过模型上下文协议（MCP）和交通管理API部署优化算法。依托实时传感器与边缘计算，持续更新数字孪生并模拟交通流。实验表明，该方法有效降低路口等待时间，提升整体交通运行效率，优于固定时序与强化学习基线方案。
原文摘要 · Abstract (English)
This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed by agentic AI to ensure real-time autonomous decisions. The framework relies on physical sensors and edge computing to measure real-time traffic information and simulate traffic flow in a constantly updated digital twin. The traffic light is automatically controlled through the digital twin according to traffic congestion, travel delay and traffic patterns. This approach is implemented as a three-layer system: perception, conceptualization and action. The perception layer receives data on physical systems; the conceptualization layer uses LangChain to process the data; and the action layer links to the Model Context Protocol (MCP) and traffic management APIs to implement optimised traffic signal control algorithms. The results show that the framework minimizes waiting time at traffic lights and positively affects the effectiveness of the entire traffic flow, which is better than the fixed-time and reinforcement learning-based baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。