People spend the majority of their lives within built environments, whose design can profoundly influence human- and community-centered outcomes such as social capital formation, access to opportunity, public health, and resilience to disruption. Just as the built environment shapes human behavior and well-being, its design, operation, and performance can be substantially improved by better understanding how people actually use and experience space. Yet both of these goals — enhancing human benefits from built environments and improving system performance through human-aware design — are constrained by a fundamental limitation: existing computational models oversimplify human agents, equipping them with static or assumed behavioral rules that fail to reflect the dynamic, adaptive, and context-sensitive nature of real-world behavior. These simplifications undermine generalizability, limiting the ability of such models to transfer insights across scenarios or support the design of responsive, human-centered spaces. To overcome these limitations, we introduce EMPIRE ( Empirical Modeling of People in Responsive Environments ) — a data-driven, hierarchical model for predicting human spatio-temporal behavior in dynamic physical environments, with a focus on scenario-based generalizability. Driven by in-situ data, EMPIRE integrates Imitation Learning for strategic activity planning and Reinforcement Learning for generating adaptive execution policies based on interpretation of the environment and preferences. This multi-layered decomposition mirrors the cognitive structure of human decision making, enabling modularity, interpretability, and adaptability across unseen spatial configurations. To illustrate EMPIRE’s generalizability, we simulate human behavior in a social infrastructure setting (i.e., a park) by generating synthetic ground-truth trajectories that incorporate heterogeneous agent preferences, environmental dynamics, and social constraints. We conduct a systematic evaluation across six distinct park layouts using a leave-one-layout-out strategy, where models are trained on five configurations and tested on the sixth. This setup allows assessment of EMPIRE’s capacity to generalize to various unseen spatial scenarios. Experimental results demonstrate that EMPIRE successfully transfers learned behavioral patterns to new environments. • Data-driven agent-based model learns activities and preferences from in-situ data. • Hierarchical IL-GNN-RL structure mirrors human cognition for behavior simulation. • GNN learns preference-based rewards from physical, environmental, and social features. • Modular, data-driven foundation for rapid what-if built environment analysis.
Doctorarastoo et al. (Thu,) studied this question.