  Urban environments, characterized by their complex, multi-layered networks
encompassing physical, social, economic, and environmental dimensions, face
significant challenges in the face of rapid urbanization. These challenges,
ranging from traffic congestion and pollution to social inequality, call for
advanced technological interventions. Recent developments in big data,
artificial intelligence, urban computing, and digital twins have laid the
groundwork for sophisticated city modeling and simulation. However, a gap
persists between these technological capabilities and their practical
implementation in addressing urban challenges in an systemic-intelligent way.
This paper proposes Urban Generative Intelligence (UGI), a novel foundational
platform integrating Large Language Models (LLMs) into urban systems to foster
a new paradigm of urban intelligence. UGI leverages CityGPT, a foundation model
trained on city-specific multi-source data, to create embodied agents for
various urban tasks. These agents, operating within a textual urban environment
emulated by city simulator and urban knowledge graph, interact through a
natural language interface, offering an open platform for diverse intelligent
and embodied agent development. This platform not only addresses specific urban
issues but also simulates complex urban systems, providing a multidisciplinary
approach to understand and manage urban complexity. This work signifies a
transformative step in city science and urban intelligence, harnessing the
power of LLMs to unravel and address the intricate dynamics of urban systems.
The code repository with demonstrations will soon be released here
https://github.com/tsinghua-fib-lab/UGI.
