Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications

TL;DR

This paper introduces the concept of urban LLM agents, surveys their current research landscape and applications in urban domains, and discusses challenges and future directions for deploying LLMs in smart city environments.

Contribution

It defines the concept of urban LLM agents, categorizes their applications, and provides a comprehensive survey of current research and open problems in this emerging field.

Findings

Urban LLM agents enable autonomous city decision-making.

Applications span urban planning, transportation, environment, safety, and society.

Trustworthiness and evaluation are critical for real-world deployment.

Abstract

The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, which are LLM-powered agents that are semi-embodied within the hybrid cyber-physical-social space of cities and used for system-level urban decision-making. First, we introduce the concept of urban LLM agents, discussing their unique capabilities and features. Second, we survey the current research landscape from the perspective of agent workflows, encompassing urban sensing, memory management, reasoning, execution, and learning. Third, we categorize the application domains of urban LLM agents into five groups: urban planning, transportation, environment, public safety, and urban society, presenting representative works in each group. Finally, we discuss trustworthiness and evaluation issues that are critical for real-world deployment, and identify several open problems for future research. This survey aims to establish a foundation for the emerging field of urban LLM agents and to provide a roadmap for advancing the intersection of LLMs and urban intelligence. A curated list of relevant papers and open-source resources is maintained and continuously updated at https://github.com/usail-hkust/Awesome-Urban-LLM-Agents.