@inproceedings{du-etal-2026-gams,
title = "{GAMS}: Geospatial Knowledge Enhanced Agentic Framework for Urban Human Mobility Behavior Simulation",
author = "Du, Yuwei and
Feng, Jie and
Yuan, Jian and
Li, Yong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1295/",
pages = "27390--27405",
ISBN = "979-8-89176-462-0",
abstract = "Recently, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces, poor integration with individual mobility patterns, and weak alignment with collective mobility distributions. To address these limitations, we propose Geospatial Knowledge Enhanced Agentic framework for Mobility Simulation (GAMS), an agentic framework that leverages the language based geospatial foundation model to simulate human mobility in urban space. GAMS comprises three core modules: MobExtractor, which extracts template-based mobility profiles and synthesizes new ones during simulation; GeoGenerator, which generates trajectory points considering collective mobility knowledge and urban geospatial knowledge; and TrajEnhancer, which captures individual mobility regularities from real-world trajectories and refines to generate style-aligned synthetic trajectories. Experiments on two real-world datasets show that GAMS achieves superior performance over various state-of-the-art methods, with more than a 17{\%} improvement by capturing diverse mobility regularities. Our codes and datasets are open-sourced via https://anonymous.4open.science/r/CAMS-1992/."
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<abstract>Recently, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces, poor integration with individual mobility patterns, and weak alignment with collective mobility distributions. To address these limitations, we propose Geospatial Knowledge Enhanced Agentic framework for Mobility Simulation (GAMS), an agentic framework that leverages the language based geospatial foundation model to simulate human mobility in urban space. GAMS comprises three core modules: MobExtractor, which extracts template-based mobility profiles and synthesizes new ones during simulation; GeoGenerator, which generates trajectory points considering collective mobility knowledge and urban geospatial knowledge; and TrajEnhancer, which captures individual mobility regularities from real-world trajectories and refines to generate style-aligned synthetic trajectories. Experiments on two real-world datasets show that GAMS achieves superior performance over various state-of-the-art methods, with more than a 17% improvement by capturing diverse mobility regularities. Our codes and datasets are open-sourced via https://anonymous.4open.science/r/CAMS-1992/.</abstract>
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%0 Conference Proceedings
%T GAMS: Geospatial Knowledge Enhanced Agentic Framework for Urban Human Mobility Behavior Simulation
%A Du, Yuwei
%A Feng, Jie
%A Yuan, Jian
%A Li, Yong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F du-etal-2026-gams
%X Recently, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces, poor integration with individual mobility patterns, and weak alignment with collective mobility distributions. To address these limitations, we propose Geospatial Knowledge Enhanced Agentic framework for Mobility Simulation (GAMS), an agentic framework that leverages the language based geospatial foundation model to simulate human mobility in urban space. GAMS comprises three core modules: MobExtractor, which extracts template-based mobility profiles and synthesizes new ones during simulation; GeoGenerator, which generates trajectory points considering collective mobility knowledge and urban geospatial knowledge; and TrajEnhancer, which captures individual mobility regularities from real-world trajectories and refines to generate style-aligned synthetic trajectories. Experiments on two real-world datasets show that GAMS achieves superior performance over various state-of-the-art methods, with more than a 17% improvement by capturing diverse mobility regularities. Our codes and datasets are open-sourced via https://anonymous.4open.science/r/CAMS-1992/.
%U https://aclanthology.org/2026.findings-emnlp.1295/
%P 27390-27405
Markdown (Informal)
[GAMS: Geospatial Knowledge Enhanced Agentic Framework for Urban Human Mobility Behavior Simulation](https://aclanthology.org/2026.findings-emnlp.1295/) (Du et al., Findings 2026)
ACL