CAMS：基於CityGPT的城市人類移動模擬代理框架

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

人類移動模擬在諸多現實應用中扮演著關鍵角色。近期，為克服傳統數據驅動方法的局限，研究者探索利用大型語言模型（LLMs）的常識知識與推理能力，以加速人類移動模擬。然而，這些方法存在若干關鍵缺陷，包括對城市空間建模不足，以及與個體移動模式和群體移動分佈的整合不佳。針對這些挑戰，我們提出了基於CityGPT的移動模擬代理框架（CAMS），這是一個利用語言基礎模型來模擬城市空間中人類移動的代理框架。CAMS包含三個核心模塊：MobExtractor用於提取模板移動模式並基於用戶檔案合成新模式，GeoGenerator考慮集體知識生成錨點並使用增強版CityGPT生成候選城市地理空間知識，TrajEnhancer基於移動模式檢索空間知識並通過DPO生成與真實軌跡偏好對齊的軌跡。在真實數據集上的實驗表明，CAMS在不依賴外部提供的地理空間信息的情況下，實現了卓越的性能。此外，通過全面建模個體移動模式和群體移動約束，CAMS生成了更為真實且合理的軌跡。總體而言，CAMS確立了一種將代理框架與具備城市知識的LLMs相結合的人類移動模擬新範式。

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.