用视觉信息提升人群模拟，跨场景更真实。
Improved visual-information-driven model for crowd simulation and its modular application
- 融合视觉信息与出口提示，提升模型泛化能力。
- 在4类基础模块和复合场景中表现优于传统模型。
- 适合需要多场景适应的智能交通与建筑设计者。
人群移动模拟对行人安全管理和设施设计至关重要。数据驱动模型有望提升真实感与预测准确性，但多数仅针对单一场景，灵活性不足。本文提出一种数据驱动的人群模拟模型，结合精细化视觉信息提取与明确出口线索，旨在通过更有效捕捉核心导航特征，提升跨场景灵活性。模型在四个基础模块（瓶颈、走廊、拐角、T型路口）及复合场景中通过模块化方法进行测试。结果表明，该模型在各类场景中表现良好，与真实实验中行人行为高度一致，并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发展提供启示，推动数据驱动方法的实际应用。
原文摘要 · Abstract (English)
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.
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