RoadEventGPT : A Data‐Driven Autonomous Framework for Detection and Geo‐Visualization of Urban Road Event Using Natural Chinese Text Data

The rapid detection and geolocation of road events are critical for urban traffic management and public safety. With the growing variety of traffic information sources, efficiently processing textual data from news report and social media and delivering GIS/LBS services remains challenging due to delayed information retrieval, imprecise geographic localization, and insufficient integration between textual and spatial information in existing methods. To address these limitations, this study proposes RoadEventGPT, a framework for the automatic extraction and geospatial localization of road events. By integrating large language models (LLMs) with geospatial tools via prompt engineering and few‐shot learning, RoadEventGPT enables complex geospatial reasoning and tool invocation, forming an end‐to‐end pipeline that converts unstructured text into structured, georeferenced road event data. The framework decomposes the task into text preprocessing, event extraction, and geolocation. Experimental results demonstrate that RoadEventGPT significantly outperforms traditional rule‐based methods, with high F1‐scores for geographic entity recognition and spatial scene classification. It also exhibits strong robustness and adaptability across diverse road‐related spatial scenarios, enabling effective handling of different types of spatial contexts in road event analysis.