  Crowd simulations play a pivotal role in building design, influencing both
user experience and public safety. While traditional knowledge-driven models
have their merits, data-driven crowd simulation models promise to bring a new
dimension of realism to these simulations. However, most of the existing
data-driven models are designed for specific geometries, leading to poor
adaptability and applicability. A promising strategy for enhancing the
adaptability and realism of data-driven crowd simulation models is to
incorporate visual information, including the scenario geometry and pedestrian
locomotion. Consequently, this paper proposes a novel visual-information-driven
(VID) crowd simulation model. The VID model predicts the pedestrian velocity at
the next time step based on the prior social-visual information and motion data
of an individual. A radar-geometry-locomotion method is established to extract
the visual information of pedestrians. Moreover, a temporal convolutional
network (TCN)-based deep learning model, named social-visual TCN, is developed
for velocity prediction. The VID model is tested on three public pedestrian
motion datasets with distinct geometries, i.e., corridor, corner, and
T-junction. Both qualitative and quantitative metrics are employed to evaluate
the VID model, and the results highlight the improved adaptability of the model
across all three geometric scenarios. Overall, the proposed method demonstrates
effectiveness in enhancing the adaptability of data-driven crowd models.
