  Location-based services play an 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
problem-solver 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. Our experimental
results indicate that UrbanLLM significantly outperforms other established
LLMs, such as Llama and the GPT series, in handling problems concerning complex
urban activity planning and management. UrbanLLM exhibits considerable
potential in enhancing the effectiveness of solving problems in urban
scenarios, reducing the workload and reliance for human experts.
