UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models
Yue Jiang, Qin Chao, Yile Chen, Xiucheng Li, Shuai Liu, Gao Cong
Abstract
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.
BibTeX
@inproceedings{jiang-etal-2024-urbanllm,
title = "{U}rban{LLM}: Autonomous Urban Activity Planning and Management with Large Language Models",
author = "Jiang, Yue and
Chao, Qin and
Chen, Yile and
Li, Xiucheng and
Liu, Shuai and
Cong, Gao",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.98/",
doi = "10.18653/v1/2024.findings-emnlp.98",
pages = "1810--1825"
}