2025/04/18 by Xiaotong Ye, Nicolas Bougie, Ye, Xiaotong +5 · 1 citation
Engineering · Social Sciences · #Action (physics) #Artificial Intelligence (cs.AI) #Code (set theory) #Construct (python library) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Key (lock) #Public transport #Scalability #Selection (genetic algorithm) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2504.16946
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/04/18 · openalex created_date 2025/10/14 · openalex updated_date 2026/08/05
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.