Traffic anomaly event detection and auxiliary decision-making based on large language models - LI Yanying; WANG Xinyu; WANG Xiao; SUN Changyin (智能科学与技术学报)
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 policy-making 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.