  Spatio-temporal prediction aims to forecast and gain insights into the
ever-changing dynamics of urban environments across both time and space. Its
purpose is to anticipate future patterns, trends, and events in diverse facets
of urban life, including transportation, population movement, and crime rates.
Although numerous efforts have been dedicated to developing neural network
techniques for accurate predictions on spatio-temporal data, it is important to
note that many of these methods heavily depend on having sufficient labeled
data to generate precise spatio-temporal representations. Unfortunately, the
issue of data scarcity is pervasive in practical urban sensing scenarios.
Consequently, it becomes necessary to build a spatio-temporal model with strong
generalization capabilities across diverse spatio-temporal learning scenarios.
Taking inspiration from the remarkable achievements of large language models
(LLMs), our objective is to create a spatio-temporal LLM that can exhibit
exceptional generalization capabilities across a wide range of downstream urban
tasks. To achieve this objective, we present the UrbanGPT, which seamlessly
integrates a spatio-temporal dependency encoder with the instruction-tuning
paradigm. This integration enables LLMs to comprehend the complex
inter-dependencies across time and space, facilitating more comprehensive and
accurate predictions under data scarcity. To validate the effectiveness of our
approach, we conduct extensive experiments on various public datasets, covering
different spatio-temporal prediction tasks. The results consistently
demonstrate that our UrbanGPT, with its carefully designed architecture,
consistently outperforms state-of-the-art baselines. These findings highlight
the potential of building large language models for spatio-temporal learning,
particularly in zero-shot scenarios where labeled data is scarce.
