用轻量生成器+大模型混合框架，高效模拟5.3万人城市出行。
MobiVerse: Scaling Urban Mobility Simulation with Hybrid Lightweight Domain-Specific Generator and Large Language Models
- 结合轻量域生成器与大模型，实现基础行程生成与情境自适应调整。
- 在普通PC上完成约5.3万人群体动态调度，响应道路封闭等环境变化。
- 模块化设计适合测试交通算法，兼顾效率与行为真实感。
理解与建模人类出行模式对交通规划和城市发展至关重要。尽管出行研究进展显著，但尚缺乏可支持算法开发、政策实施与全面评估的大规模仿真平台。传统基于活动的模型需大量数据采集与人工校准，机器学习方法难以适应动态变化，现有基于代理的大型语言模型（LLMs）实现受限于计算开销。为此，我们提出MobiVerse，一种混合框架：利用轻量级领域特定生成器生成基础活动链，结合大模型实现情境感知的动态修正。以洛杉矶西木区为例，我们在标准PC上高效生成并动态调整约5.3万名代理的出行计划。实验表明，该框架能有效响应道路封闭、足球赛等大型集会及交通拥堵等外部反馈。其模块化设计支持在系统与个体层面测试多种出行算法。结果表明，该方法在保持计算效率的同时提升了行为真实性。MobiVerse填补了出行仿真的空白，为交通系统规划与运营提供可定制平台及基准算法。代码与演示视频见https://github.com/ucla-mobility/MobiVerse。
原文摘要 · Abstract (English)
Understanding and modeling human mobility patterns is crucial for effective transportation planning and urban development. Despite significant advances in mobility research, there remains a critical gap in simulation platforms that allow for algorithm development, policy implementation, and comprehensive evaluation at scale. Traditional activity-based models require extensive data collection and manual calibration, machine learning approaches struggle with adaptation to dynamic conditions, and treding agent-based Large Language Models (LLMs) implementations face computational constraints with large-scale simulations. To address these challenges, we propose MobiVerse, a hybrid framework leverages the efficiency of lightweight domain-specific generator for generating base activity chains with the adaptability of LLMs for context-aware modifications. A case study was conducted in Westwood, Los Angeles, where we efficiently generated and dynamically adjusted schedules for the whole population of approximately 53,000 agents on a standard PC. Our experiments demonstrate that MobiVerse successfully enables agents to respond to environmental feedback, including road closures, large gathering events like football games, and congestion, through our hybrid framework. Its modular design facilitates testing various mobility algorithms at both transportation system and agent levels. Results show our approach maintains computational efficiency while enhancing behavioral realism. MobiVerse bridges the gap in mobility simulation by providing a customizable platform for mobility systems planning and operations with benchmark algorithms. Code and videos are available at https://github.com/ucla-mobility/MobiVerse.
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