In order to use computational models of crowd movement for evaluating and informing building designs, it is imperative that these models capture the essence of real human crowd interactions, while generalizing to novel environment layouts. Current approaches make simplifying assumptions that prevent the models from accounting for the locomotion affordances and constraints exhibited by humans in high-density interactions. Additionally, these models are manually tuned to replicate specific crowd phenomena with no existing mechanisms for automatically fitting these models to capture the essence of real crowd datasets in a representative and general fashion. We leverage our research in autonomous virtual humans  to serve as a basis for computational  models of human crowd behavior. Our proposed models facilitate the extrapolation of the crowd dynamics in unseen situations (such as those featuring increased density or new environment layouts) while preserving the crowd characteristics observed in reality.