UrbanGPT: Spatio-Temporal Large Language Models

arXiv:2403.00813v1 Announce Type: new
Abstract: 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 …

abstract arxiv crime cs.ai cs.cl cs.cy diverse dynamics environments events forecast future insights language language models large language large language models life patterns population prediction space temporal transportation trends type urban