Data-driven crowd simulation models offer advantages in enhancing the accuracy and realism of simulations, and improving their generalizability is essential for promoting application. Current data-driven approaches are primarily designed for a single scenario, with very few models validated across more than two scenarios. It is still an open question to develop data-driven crowd simulation models with strong generalizibility. We notice that the key to addressing this challenge lies in effectively and accurately capturing the core common influential features that govern pedestrians' navigation across diverse scenarios. Particularly, we believe that visual information is one of the most dominant influencing features. In light of this, this paper proposes a data-driven model incorporating a refined visual information extraction method and exit cues to enhance generalizability. The proposed model is examined on four common fundamental modules: bottleneck, corridor, corner and T-junction. The evaluation results demonstrate that our model performs excellently across these scenarios, aligning with pedestrian movement in real-world experiments, and significantly outperforms the classical knowledge-driven model. Furthermore, we introduce a modular approach to apply our proposed model in composite scenarios, and the results regarding trajectories and fundamental diagrams indicate that our simulations closely match real-world patterns in the composite scenario. The research outcomes can provide inspiration for the development of data-driven crowd simulation models with high generalizability and advance the application of data-driven approaches.This work has been submitted to Elsevier for possible publication.

翻译：数据驱动的人群仿真模型在提升仿真精度与真实性方面具有优势，增强其泛化能力对于推动实际应用至关重要。当前数据驱动方法主要针对单一场景设计，鲜有模型能在超过两种场景中得到验证。开发具有强泛化能力的数据驱动人群仿真模型仍是一个开放性问题。我们注意到，解决这一挑战的关键在于有效且准确地捕捉支配行人在不同场景中导航的核心共性影响特征。特别地，我们认为视觉信息是最主要的影响特征之一。鉴于此，本文提出一种数据驱动模型，结合改进的视觉信息提取方法与出口线索以增强泛化能力。该模型在四个基本模块上进行了验证：瓶颈、走廊、拐角与T型路口。评估结果表明，我们的模型在这些场景中均表现优异，与实际实验中的行人运动规律相符，且显著优于经典的知识驱动模型。此外，我们引入了一种模块化方法，将所提模型应用于复合场景中；轨迹与基本图的仿真结果表明，我们的仿真结果与复合场景中的实际模式高度吻合。研究成果可为开发高泛化能力的数据驱动人群仿真模型提供启发，并推动数据驱动方法的应用。本工作已提交至Elsevier待审发表。