UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models.

Yue Jiang, Qin Chao, Yile Chen, Xiucheng Li, Shuai Liu, Gao Cong.

Location-based services play a critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal context of location-based services, these models struggle to autonomously tackle problems regarding complex urban planing and management. To bridge this gap, we introduce UrbanLLM, a fine-tuned large language model (LLM) designed to tackle diverse problems in urban scenarios. UrbanLLM functions as a problemsolver by decomposing urban-related queries into manageable sub-tasks, identifying suitable spatio-temporal AI models for each sub-task, and generating comprehensive responses to the given queries.