基于大语言模型的交通异常事件检测与辅助决策

Traffic anomaly event detection and auxiliary decision-making based on large language models

大语言模型卓越的数据分析和逻辑推理能力为实时交通管理和辅助决策提供了新思路。ChatGPT能够高效地处理和分析公开的社交媒体数据,检测数据中包含的城市、道路信息及发生的交通事件,可用于辅助交通管理人员进行实时询问、追溯起因以及探讨应对措施。构建了一个融合社交媒体与ChatGPT的智能问答框架TMGPT(traffic management GPT),研究如何基于大语言模型进行交通异常事件的快速检测并辅助交通管理部门进行决策。该框架通过对社交媒体数据的获取、处理与分析,精准检测交通异常事件并生成针对性应对策略。该框架通过反馈机制持续优化系统性能,为交通管理和政策制定部门提供决策依据,从而提升城市交通运行效率和安全性。实验结果表明,相较于传统方法,TMGPT框架显著提高了交通异常事件检测的准确率并缩短了响应时间,展现出大语言模型在复杂城市交通管理中的应用潜力。

The superior data analytics and logical reasoning capabilities of big language models provide new ideas for real-time traffic management and assisted decision-making.ChatGPT efficiently processes and analyzes publicly available social media data to detect city and roadway information and traffic events contained in the data,which can be used to assist traffic managers in making real-time inquiries,tracing causes and exploring countermeasures.This paper constructs an intelligent Q&A framework,TMGPT(traffic management GPT),which integrates social media data with ChatGPT,to explore how large language models can be leveraged to quickly detect traffic anomalies and provide decision support for traffic management departments.Through the acquisition,processing and analysis of social media data,the framework achieves accurate detection of traffic anomalies and the generation of targeted response strategies,and continuously optimizes the system performance through the feedback mechanism,providing a decision basis for traffic management and policymaking departments to improve the efficiency and safety of urban traffic operation.The results show that compared with traditional methods,TMGPT significantly improves the accuracy of detection and reduced response time in the detection and assisted decision-making of abnormal traffic events,which demonstrates the application potential of large language models in complex urban traffic management.