MobileCity高效模拟城市行为，突破计算瓶颈。
MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation
- 构建多模式交通系统与问卷生成真实代理画像
- 4000个代理仿真中行为更真实且计算高效
- 适合城市规划、交通分析等实际应用
生成式代理在模拟真实城市行为方面具有潜力，但现有方法简化了交通选择，依赖静态代理画像导致行为同质化，并伴随高昂的计算成本。为解决这些问题，我们提出MobileCity——一个轻量级仿真平台，旨在高效建模真实城市移动行为。平台引入涵盖多种交通方式的综合交通系统，并通过问卷数据构建代理画像。为实现可扩展仿真，代理在预生成的动作空间中进行决策，并使用本地模型高效生成代理记忆。在4000个代理的微观与宏观评估中，MobileCity生成的行为比基线更真实，同时保持计算效率。我们进一步探索其在预测移动模式和分析交通偏好人口趋势中的实际应用。代码已公开于https://github.com/Tony-Yip/MobileCity。
原文摘要 · Abstract (English)
Generative agents offer promising capabilities for simulating realistic urban behaviors. However, existing methods oversimplify transportation choices, rely heavily on static agent profiles leading to behavioral homogenization, and inherit prohibitive computational costs. To address these limitations, we present MobileCity, a lightweight simulation platform designed to model realistic urban mobility with high computational efficiency. We introduce a comprehensive transportation system with multiple transport modes, and collect questionnaire data from respondents to construct agent profiles. To enable scalable simulation, agents perform action selection within a pre-generated action space and uses local models for efficient agent memory generation. Through extensive micro and macro-level evaluations on 4,000 agents, we demonstrate that MobileCity generates more realistic urban behaviors than baselines while maintaining computational efficiency. We further explore practical applications such as predicting movement patterns and analyzing demographic trends in transportation preferences. Our code is publicly available at https://github.com/Tony-Yip/MobileCity.
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