CAMS：基于CityGPT的城市人类移动性模拟智能体框架

CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation

June 16, 2025

作者: Yuwei Du, Jie Feng, Jian Yuan, Yong Li

cs.AI

摘要

人类移动模拟在众多现实应用中扮演着关键角色。近期，为克服传统数据驱动方法的局限，研究者们探索利用大型语言模型（LLMs）的常识知识与推理能力来加速人类移动模拟。然而，这些方法存在若干关键不足，包括对城市空间建模不足，以及与个体移动模式和集体移动分布融合欠佳。针对这些挑战，我们提出了CityGPT驱动的移动模拟代理框架（CAMS），该框架依托基于语言的城市基础模型，模拟城市空间中的人类移动。CAMS包含三大核心模块：MobExtractor用于提取模板移动模式并根据用户画像合成新模式；GeoGenerator在考虑集体知识的基础上生成锚点，并利用增强版CityGPT生成候选城市地理空间知识；TrajEnhancer则基于移动模式检索空间知识，并通过DPO生成与真实轨迹偏好对齐的轨迹。在真实世界数据集上的实验表明，CAMS在不依赖外部提供的地理空间信息的情况下，实现了卓越的性能。此外，通过全面建模个体移动模式与集体移动约束，CAMS生成了更为真实且合理的轨迹。总体而言，CAMS开创了一种将代理框架与具备城市知识的LLMs相结合的人类移动模拟新范式。

English

Human mobility simulation plays a crucial role in various real-world applications. Recently, to address the limitations of traditional data-driven approaches, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models (LLMs) to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces and poor integration with both individual mobility patterns and collective mobility distributions. To address these challenges, we propose CityGPT-Powered Agentic framework for Mobility Simulation (CAMS), an agentic framework that leverages the language based urban foundation model to simulate human mobility in urban space. CAMS comprises three core modules, including MobExtractor to extract template mobility patterns and synthesize new ones based on user profiles, GeoGenerator to generate anchor points considering collective knowledge and generate candidate urban geospatial knowledge using an enhanced version of CityGPT, TrajEnhancer to retrieve spatial knowledge based on mobility patterns and generate trajectories with real trajectory preference alignment via DPO. Experiments on real-world datasets show that CAMS achieves superior performance without relying on externally provided geospatial information. Moreover, by holistically modeling both individual mobility patterns and collective mobility constraints, CAMS generates more realistic and plausible trajectories. In general, CAMS establishes a new paradigm that integrates the agentic framework with urban-knowledgeable LLMs for human mobility simulation.