UrbanLLMind: Scalable LLM-Powered Urban Mobility Simulation with Open-Weight Models

Simulating human behavior in real-world environments is important for synthetic data generation, policy analysis, and scenario planning. Agent-based models (ABMs) are widely used for this purpose, but they often rely on hand-crafted rules and simplified assumptions that limit their realism, behavioral diversity, and adaptation to changing conditions. Large language models (LLMs) offer a promising alternative as decision engines for ABMs since they can condition agent behavior on agent state, environmental context, memory, and world knowledge. We present UrbanLLMind, a scalable generative agent-based model for urban mobility simulation. In UrbanLLMind, agents inhabit a synthetic city constructed from geographic and demographic data and act through a daily loop of LLM-powered planning, step-level mobility decisions, and end-of-day reflection. Agents also maintain a long-term memory system that allows them to recall past experiences and preserve consistent personas over time. UrbanLLMind is designed to run with open-weight LLMs on local cluster infrastructure. In our evaluations, UrbanLLMind generates plausible mobility patterns and, when powered by a fine-tuned LLM, aligns closely with observed distributions (surveys). Moreover, the model is sensitive to external behavioral drivers, such as extreme weather. We show that UrbanLLMind is capable of simulating 10,000 agents powered by an open-weight 120B-parameter model using a vLLM architecture on local cluster infrastructure. Together, these results position UrbanLLMind as the first controllable, city-grounded testbed for large-scale urban mobility simulation and scenario analysis