基于圆社会力量模型的行人预测动态
Xiangmin Hu1, Tao Chen1,2, Yushan Song1
1Department of Engineering Physics, Tsinghua University, Beijing 100084, China.
Chaos (Woodbury, N.Y.)
|July 6, 2023
概括
用社会力量模型 (SFM) 建模的行人运动的预测可以稳定人群并促进秩序. 这种预测能力在模拟中增强了集体运动动态.
科学领域:
- 计算社会科学 计算社会科学
- 复杂系统的物理 复杂系统的物理
- 人类流动性的动力学
背景情况:
- 人类的预测能力显著影响人际交互和集体行为.
- 了解预测如何影响行人运动对于人群管理和模拟准确性至关重要.
研究的目的:
- 通过使用增强的社会力量模型 (SFM) 调查预测对行人运动动态的影响.
- 量化预测对人群配置和运动稳定性的影响.
主要方法:
- 利用社会力量模型 (SFM) 的圆规范来模拟行人运动.
- 引入了预测,通过根据相对速度和调整外推时间来塑造圆电位.
- 通过四种场景进行模拟:循环运动,人群聚集,瓶逃脱和自由漫游.
主要成果:
- 发现预期可以通过减少干扰来稳定行人运动.
- 模拟表明,预期促进了更有序的人群配置.
- 这项研究表明,预期促进了行人之间自发的集体运动.
结论:
- 预测是影响行人动态的关键因素,有助于稳定和秩序.
- 这些发现为进一步研究人群中预期动态的研究提供了基础.
- 增强的SFM为建模和理解复杂人群行为提供了有价值的工具.
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