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In many local festivals and in events such as pop concerts or sporting events, crowds might generate dangerous situations like the case of panic stampedes. How to anticipate these situations is challenging. Despite there are simulation models of people motion, it is very difficult to predict real people behavior. In this paper, we advocate the application of digital twins to monitor and analyze real people motion. We implement the Artax framework, which stores people movements as traces through time. We describe the current state of our framework and explain how we aim to apply it for anticipating dangerous situations caused by crowds and warning people.

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Towards a Digital Twin System for Human Crowd Motion Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Ignacio Alba,
  • Javier Troya,
  • Carlos Canal

摘要

In many local festivals and in events such as pop concerts or sporting events, crowds might generate dangerous situations like the case of panic stampedes. How to anticipate these situations is challenging. Despite there are simulation models of people motion, it is very difficult to predict real people behavior. In this paper, we advocate the application of digital twins to monitor and analyze real people motion. We implement the Artax framework, which stores people movements as traces through time. We describe the current state of our framework and explain how we aim to apply it for anticipating dangerous situations caused by crowds and warning people.