数据驱动的基于物理的行人动态建模
Caspar A S Pouw1,2, Geert G M van der Vleuten1, Alessandro Corbetta1,3
1Eindhoven University of Technology, Department of Applied Physics and Science Education, 5600 MB Eindhoven, The Netherlands.
Physical review. E
|February 7, 2025
概括
这项研究引入了一种新的两次级朗格温动力学模型,以准确模拟复杂环境中的行人运动. 该模型从真实世界的数据中学习有效的潜力,捕捉在稀疏和密集的人群中个别行人动态.
科学领域:
- 物理 物理学 物理
- 复杂的系统复杂的系统.
- 数据科学数据科学数据科学
背景情况:
- 以前使用朗格温方程的模型有效地捕捉了简单的行人动态.
- 模拟复杂的行人运动与多个路线和目的地仍然是一个挑战.
- 现有的方法在故意运动,可变性和环境相互作用的相互作用中扎.
研究的目的:
- 开发一种新的,通用的框架来描述任何几何设置中的行人动态.
- 通过结合两个时间尺度来扩展之前的工作,以实现更现实的运动模拟.
- 创建一个基于数据的模型,能够从现实世界的行人轨迹中学习复杂的潜力.
主要方法:
- 开发了一个具有快速 (随机波动) 和缓慢 (计划路径) 时间尺度的朗格温动力学模型.
- 采用数据驱动的方法,灵感来自统计领域理论,从轨迹数据中学习潜力.
- 通过使用现实行人轨迹在各种环境中的高统计数据库验证了该模型.
主要成果:
- 该模型成功地捕获了个人行人动态中的波动统计数据.
- 它准确地模拟了在稀释和密集的人群条件下行人的行为.
- 该框架在五个互补的越来越复杂的环境中表现出有效性,包括火车平台.
结论:
- 新的两次尺度朗格温模型为行人动态提供了一种通用和基于物理的方法.
- 数据驱动的方法允许学习有效的潜力,增强预测能力.
- 这个框架提供了基本的见解,并且在交通,人群管理和集体动物行为的潜在应用.
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