Ternes Patricia, Ward Jonathan A, Heppenstall Alison, Kumar Vijay, Kieu Le-Minh, Malleson Nick
Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
School of Geography, University of Leeds, Leeds, UK.
Open Res Eur. 2022 Jul 20;1:131. doi: 10.12688/openreseurope.14144.2. eCollection 2021.
This paper explores the use of a particle filter-a data assimilation method-to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents' choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.
本文探讨了使用粒子滤波器（一种数据同化方法）将实时数据纳入基于智能体的模型。我们将该方法应用于对美国纽约市中央大车站大厅内真实行人移动的模拟。结果表明，由于以下原因，粒子滤波器表现不佳：（i）一些行人的行为不可预测；（ii）滤波器未优化此类模型所特有的分类智能体参数。这个问题仅因实验使用的是真实世界的行人移动数据，而非更常见的模拟假设数据而出现。我们指出了一个潜在的解决方案，即对粒子中的一些变量进行重采样，比如智能体在空间中的位置，但保留其他变量，比如智能体对目的地的选择。本研究说明了纳入真实世界数据的重要性，并为将改进的粒子滤波器应用于基于智能体的模型提供了概念验证。所讨论的障碍和解决方案对未来致力于构建大规模基于智能体的实时模型的工作具有重要意义。
