Modern society increasingly depends on accurate, controllable simulations of human crowds for safety-critical planning, architectural design, and immersive media. Yet existing crowd simulators face fundamental challenges: they are difficult to calibrate from real-world trajectory data, too computationally expensive for large-scale design exploration, often lack learned control policies that are both general and richly heterogeneous, and provide no principled metrics to determine which parameters meaningfully drive emergent behaviors. This thesis presents a unified, learning-based framework that addresses these interconnected challenges through four parametric components. First, we introduce a trajectory-to-configuration inference framework that regresses latent motion patterns to simulator parameters, enabling interpretable calibration of classical models such as Social Force and ORCA. Second, we develop neural surrogates trained on simulated egress data that predict crowd performance metrics orders of magnitude faster than full simulation, enabling crowd-aware optimization of complex architectural layouts. Third, we propose a parametric multi-agent reinforcement learning method (HOP-RL) that yields heterogeneous, directable agent behaviors with reciprocal predictive collision avoidance, generalizing across scenarios without retraining. Fourth, we introduce variance decomposition analysis that separates parameter effects from simulation noise, providing sample-size-invariant measures of parameter importance and revealing how population structure reshapes parameter sensitivity. Across synthetic validations and real-world case studies, including evacuation analyses of complex built environments, we demonstrate that this integrated approach improves calibration fidelity, optimization efficiency, and behavioral realism while substantially reducing the effective parameter space that practitioners must manage. The result is a principled, end-to-end methodology for learning, directing, and validating synthetic crowds that is simultaneously data-informed, design-oriented, and evaluation-driven, enabling a coherent workflow from observed data to informed design decisions.
Kaidong Hu (Thu,) studied this question.