Crowd movement simulation is crucial for pedestrian safety management and facility design. Data-driven models offer the potential to improve realism and predictive accuracy, but most are developed for a single scenario, limiting their flexibility. We propose a data-driven crowd simulation model that incorporates refined visual-information extraction and explicit exit cues, aiming to improve flexibility across multiple scenarios by more effectively capturing core navigational features. The model is tested on four fundamental modules (bottleneck, corridor, corner, and T-junction) and further evaluated in a composite scenario using a modular approach. Results show that our model performs well across these scenarios, aligning with pedestrian movement in real-world experiments, and outperforms the classical knowledge-driven model in these scenarios. The research outcomes can provide inspiration for the development of data-driven crowd simulation models and advance the application of data-driven approaches.

翻译：人群运动模拟对行人安全管理和设施设计至关重要。数据驱动模型在提升真实感和预测精度方面潜力显著，但现有模型大多针对单一场景开发，灵活性受限。我们提出了一种融合精细化视觉信息提取与显式出口线索的数据驱动人群模拟模型，旨在通过更有效地捕捉核心导航特征来提升多场景适用性。该模型在四种基础模块（瓶颈、走廊、转角及T型交叉口）上进行了测试，并进一步在组合场景中采用模块化方法进行评估。结果表明，本模型在上述场景中均表现良好，与真实行人运动实验数据吻合，且性能优于经典知识驱动模型。研究成果可为数据驱动人群模拟模型的开发提供启发，并推动数据驱动方法的实际应用。