# UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models

arXiv: https://arxiv.org/abs/2406.12360 · HTML: https://mlsanity.com/paper/2406.12360/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [BRITS: Bidirectional Recurrent Imputation for Time Series](https://mlsanity.com/paper/1805.10572/) (2018)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models](https://mlsanity.com/paper/2309.06719/) (2023)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)
- [GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT](https://mlsanity.com/paper/2307.07930/) (2023)
- [RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer](https://mlsanity.com/paper/2211.13234/) (2022)
- [TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation](https://mlsanity.com/paper/2402.07233/) (2024)
- [PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval](https://mlsanity.com/paper/2402.19273/) (2024)
- [Large language model empowered participatory urban planning](https://mlsanity.com/paper/2402.01698/) (2024)

## Built on by (most cited first, 13 of 13)

- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning](https://mlsanity.com/paper/2601.03248/) (2026)
- [TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration](https://mlsanity.com/paper/2410.20445/) (2024)
- [A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMs](https://mlsanity.com/paper/2506.20073/) (2025)
- [Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning](https://mlsanity.com/paper/2507.19586/) (2025)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models](https://mlsanity.com/paper/2602.00012/) (2025)
- [GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response](https://mlsanity.com/paper/2509.19354/) (2025)
- [Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents](https://mlsanity.com/paper/2606.21740/) (2026)
- [RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planning](https://mlsanity.com/paper/2607.16745/) (2026)
- [Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit](https://mlsanity.com/paper/2608.01753/) (2026)
- [CityPlanner: A Sandbox Agent for Executable Urban Planning](https://mlsanity.com/paper/2609.09578/) (2026)

## Abstract (arXiv)

Location-based services play an critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal context of location-based services, these models struggle to autonomously tackle problems regarding complex urban planing and management. To bridge this gap, we introduce UrbanLLM, a fine-tuned large language model (LLM) designed to tackle diverse problems in urban scenarios. UrbanLLM functions as a problem-solver by decomposing urban-related queries into manageable sub-tasks, identifying suitable spatio-temporal AI models for each sub-task, and generating comprehensive responses to the given queries. Our experimental results indicate that UrbanLLM significantly outperforms other established LLMs, such as Llama and the GPT series, in handling problems concerning complex urban activity planning and management. UrbanLLM exhibits considerable potential in enhancing the effectiveness of solving problems in urban scenarios, reducing the workload and reliance for human experts.