Urban Computing in the Era of Large Language Models

2025-04-02

AI Summary

Traditional urban computing faces limitations in generalizability, scalability, and contextual understanding. To address these challenges, this paper systematically investigates a large language model (LLM)-empowered paradigm for urban computing. We propose the first LLM application framework tailored to urban complexity, grounded in a tripartite methodology integrating *functional roles*, *implementation pathways*, and *evaluation dimensions*. Our approach unifies multi-source heterogeneous data processing, knowledge-augmented reasoning, prompt engineering, and human-AI collaborative interaction. We comprehensively survey over 100 state-of-the-art studies across core domains—including transportation, public safety, and environmental monitoring—and curate specialized datasets and toolkits. We identify critical bottlenecks and distill three key advancement directions: scalability, interpretability, and interactivity. Finally, we articulate six foundational research agendas, delivering the first systematic roadmap for LLM-driven smart city research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and contextual understanding. The advent of Large Language Models (LLMs) offers transformative potential in this domain. This survey explores the intersection of LLMs and urban computing, emphasizing the impact of LLMs in processing and analyzing urban data, enhancing decision-making, and fostering citizen engagement. We provide a concise overview of the evolution and core technologies of LLMs. Additionally, we survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring, summarizing essential tasks and prior works in various urban contexts, while highlighting LLMs' functional roles and implementation patterns. Building on this, we propose potential LLM-based solutions to address unresolved challenges. To facilitate in-depth research, we compile a list of available datasets and tools applicable to diverse urban scenarios. Finally, we discuss the limitations of current approaches and outline future directions for advancing LLMs in urban computing.

Problem

• Enhancing urban data processing with LLMs
• Improving decision-making in urban domains
• Addressing unresolved urban challenges using LLMs

Innovation

• Leveraging LLMs for urban data processing
• Applying LLMs in transportation and safety
• Proposing LLM solutions for urban challenges