数据同化和基于代理的建模:朝着纳入类别性代理参数的方向
Patricia Ternes1,2, Jonathan A Ward3, Alison Heppenstall4,5,6
1Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Open research Europe
|August 30, 2023
概括
颗粒过器由于不可预测的行为和参数优化挑战,在基于代理的模型中与真实世界的行人数据作斗争. 改进过器显示了大规模实时模拟的前景.
科学领域:
- 计算社会科学 计算社会科学
- 复杂系统建模 复杂系统建模
背景情况:
- 基于代理的模型 (ABM) 使用自主代理来模拟复杂的系统.
- 像颗粒过器这样的数据同化方法将实时数据集成到模型中.
研究的目的:
- 为了评估颗粒过器在行人动态的基于代理模型中的实时数据同化效果.
- 确定挑战,并提出解决方案,以在ABM中使用现实世界的数据.
主要方法:
- 将颗粒过器应用于基于代理的模型,模拟大中央航站楼的行人流动.
- 分析颗粒过器性能与现实世界的行人移动数据.
主要成果:
- 颗粒过器表现出低于最佳的性能.
- 确定了关键问题:不可预测的代理行为和过器无法优化分类代理参数.
- 由于使用真实世界的数据,这一挑战更加严重.
结论:
- 标准颗粒过器在将现实数据纳入ABM时受到限制,特别是在异质代理行为时.
- 修改的颗粒过器方法,涉及选择性重新采样剂变量,提供了一个潜在的解决方案.
- 这项研究强调了对大规模实时ABM进行强大的数据同化技术的需求.
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