Agent-Based Human Mobility Simulation with Personalized Cognitive Maps

Large language models enable agents to simulate human mobility through sequential decisions about daily activities and destinations, without requiring task-specific training on large-scale trajectory data. However, existing methods often overlook how each individual’s limited and evolving knowledge of the city constrains their mobility decisions. To address this limitation, we introduce CogMob, which equips each agent with a personalized, partial, and evolving cognitive map and incorporates it into mobility decision-making, where intent-aware retrieval activates relevant spatial knowledge and destination selection uses this knowledge to compare candidates in light of the agent’s accumulated experience and current context. Experiments on two public datasets show that CogMob consistently improves mobility simulation across a wide range of metrics. We further assess its downstream utility through segregation analysis, observing patterns qualitatively consistent with prior findings. Our code is available at https://anonymous.4open.science/r/CogMob-17B2.