UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction

arXiv:2402.11838v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.lg decision domains foundation general languages making management modeling multiple natural optimization planning prediction prompt resource optimization tasks temporal transportation type universal model urban urban planning