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.

翻译：人群仿真在建筑设计中发挥着关键作用，直接影响用户体验与公共安全。传统知识驱动模型虽具优势，但数据驱动的人群仿真模型有望为这些仿真带来全新的真实感维度。然而，现有大多数数据驱动模型专为特定几何结构设计，导致其适应性与适用性较差。增强数据驱动人群仿真模型适应性与真实感的一个有效策略是融入视觉信息，包括场景几何结构与行人运动特征。为此，本文提出一种新颖的视觉信息驱动（VID）人群仿真模型。该模型基于个体的先验社会视觉信息与运动数据，预测下一时间步的行人速度。研究构建了雷达-几何-运动方法提取行人的视觉信息，并开发了基于时序卷积网络（TCN）的深度学习模型——社会视觉TCN，用于速度预测。VID模型在三个具有不同几何结构（走廊、拐角和T型路口）的公开行人运动数据集上进行了测试。通过定性与定量指标评估，结果表明该模型在所有三种几何场景中均展现出更强的适应性。总体而言，所提方法有效提升了数据驱动人群模型的适应性。