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首页> 外文期刊>ACM Transactions on Modeling and Computer Simulation >A Role-Dependent Data-Driven Approach for High-Density Crowd Behavior Modeling
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A Role-Dependent Data-Driven Approach for High-Density Crowd Behavior Modeling

机译：基于角色的数据驱动方法用于高密度人群行为建模

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摘要

In this article, we propose a role-dependent (RD) data-driven modeling approach to simulate pedestrians' motion in high-density scenes. It is commonly observed that pedestrians behave quite differently when walking in a dense crowd. Some people explore routes toward their destinations. Meanwhile, some people deliberately follow others, leading to lane formation. Based on these observations, two roles are included in the proposed model: leader and follower. The motion behaviors of leader and follower are modeled separately. Leaders' behaviors are learned from real crowd motion data using state-action pairs, while followers' behaviors are calculated based on specific targets that are obtained dynamically during the simulation. The proposed RD model is trained and applied to different real-world datasets to evaluate its generality and effectiveness. The simulation results demonstrate that the RD model is capable of simulating crowd behaviors in crowded scenes realistically and reproducing collective crowd behaviors such as lane formation.
机译：在本文中，我们提出了一种角色依赖（RD）数据驱动的建模方法，以模拟高密度场景中行人的运动。通常观察到，在密集人群中行走时，行人的行为有很大不同。有些人探索前往目的地的路线。同时，一些人故意跟随其他人，导致车道形成。基于这些观察，建议的模型包括两个角色：领导者和跟随者。领导者和跟随者的运动行为是分别建模的。领导者的行为是使用状态-行为对从真实人群运动数据中学习的，而跟随者的行为则是基于在仿真过程中动态获得的特定目标来计算的。所提出的RD模型经过训练并应用于不同的现实世界数据集，以评估其通用性和有效性。仿真结果表明，RD模型能够真实模拟拥挤场景中的人群行为，并能够再现诸如车道形成等集体人群行为。

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