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

• URL: http://arxiv.org/abs/2507.00914v1
• Date: Tue, 01 Jul 2025 16:18:29 GMT
• Title: Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
• Authors: Jindong Han, Yansong Ning, Zirui Yuan, Hang Ni, Fan Liu, Tengfei Lyu, Hao Liu,
• Abstract summary: Large Language Models (LLMs) have opened new ways toward realizing the vision of intelligent cities.<n>In this article, we focus on Urban LLM Agents, which are semi-embodied within the hybrid cyber-physical-social space of cities.
• Score: 11.994794218481122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• USTBench: Benchmarking and Dissecting Spatiotemporal Reasoning of LLMs as Urban Agents [6.054990893127997]
  Large language models (LLMs) have shown emerging potential intemporal, reasoning making them promising candidates for building urban agents that support diverse urban downstream applications.<n>Existing studies on evaluating urban agents on outcome-level studies offer limited insight into their underlying reasoning processes.<n>As a result, strengths and limitations of urban agents intemporal reasoning remain poorly understood.<n>USTBench is the first benchmark to evaluate LLMs'temporal reasoning abilities as urban agents across four dimensions:temporal understanding, forecasting, planning, and reflection with feedback.
  arXiv  Detail & Related papers  (2025-05-23T07:30:57Z)
• Urban Computing in the Era of Large Language Models [41.50492781046065]
  This survey explores the intersection of Large Language Models (LLMs) and urban computing.<n>We provide a concise overview of the evolution and core technologies of LLMs.<n>We survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring.
  arXiv  Detail & Related papers  (2025-04-02T05:12:13Z)
• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
  Large Language Models (LLMs) offer transformative potential to address transportation challenges.<n>This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation.<n>For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
  arXiv  Detail & Related papers  (2025-03-27T11:56:27Z)
• NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews [65.35458530702442]
  We focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN. LLMs are significantly less likely than human interviewers to use acknowledgements and to pivot to higher-level questions.
  arXiv  Detail & Related papers  (2024-11-21T01:37:38Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence.<n>WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• CityGPT: Empowering Urban Spatial Cognition of Large Language Models [7.40606412920065]
  Large language models often fall short when tackling real-life geospatial tasks within urban environments.<n>We propose textitCityGPT, a framework designed to enhance LLMs' understanding of urban space and improve their ability to solve related urban tasks.<n>To validate the effectiveness of our proposed framework, we develop a comprehensive text-based spatial benchmark textitCityEval for evaluating the performance of LLMs.
  arXiv  Detail & Related papers  (2024-06-20T02:32:16Z)
• CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks [10.22654338686634]
  Large language models (LLMs) and vision-language models (VLMs) have become essential to ensure their real-world effectiveness and reliability.<n>The challenge in constructing a systematic evaluation benchmark for urban research lies in the diversity of urban data.<n>In this paper, we design textitCityBench, an interactive simulator based evaluation platform.
  arXiv  Detail & Related papers  (2024-06-20T02:25:07Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment [32.53845672285722]
  Urban environments, characterized by their complex, multi-layered networks, face significant challenges in the face of rapid urbanization. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems.
  arXiv  Detail & Related papers  (2023-12-19T03:12:13Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.