MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation
This work addresses the need for efficient and realistic large-scale urban mobility simulation for urban planners and researchers, though it appears incremental as it builds on existing generative agent methods with specific improvements.
The authors tackled the problem of oversimplified transportation choices and high computational costs in urban behavior simulation by introducing MobileCity, a lightweight platform that uses comprehensive transportation modes and questionnaire data to model realistic urban mobility, achieving more realistic behaviors than baselines while maintaining computational efficiency across 4,000 agents.
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.