UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal
  Prediction
Yuan Yuan Jingtao Ding Jie Feng Depeng Jin Yong Li
Urban spatio-temporal prediction is crucial for informed decision-making,
such as transportation management, resource optimization, and urban planning.
Although pretrained foundation models for natural languages have experienced
remarkable breakthroughs, wherein one general-purpose model can tackle multiple
tasks across various domains, urban spatio-temporal modeling lags behind.
Existing approaches for urban prediction are usually tailored for specific
spatio-temporal scenarios, requiring task-specific model designs and extensive
in-domain training data. In this work, we propose a universal model, UniST, for
urban spatio-temporal prediction. Drawing inspiration from large language
models, UniST achieves success through: (i) flexibility towards diverse
spatio-temporal data characteristics, (ii) effective generative pre-training
with elaborated masking strategies to capture complex spatio-temporal
relationships, (iii) spatio-temporal knowledge-guided prompts that align and
leverage intrinsic and shared knowledge across scenarios. These designs
together unlock the potential of a one-for-all model for spatio-temporal
prediction with powerful generalization capability. Extensive experiments on 15
cities and 6 domains demonstrate the universality of UniST in advancing
state-of-the-art prediction performance, especially in few-shot and zero-shot
scenarios.