Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter
Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter
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DOI：
10.1016/j.simpat.2021.102386
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发表时间：
2021-08-11
影响因子：
4.2
通讯作者：
Malleson, Nick
中科院分区：
文献类型：
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作者：
Clay, Robert;Ward, Jonathan A.;Malleson, Nick
常用的数据同化方法正被用于基于代理的模型，目的是允许实时优化响应新数据。然而，现有的方法在处理基于智能体的模型中常见的分类参数时面临困难。本文提出了一种将Unscented卡尔曼滤波（UKF）数据同化算法与可逆跳跃（RJ）马尔可夫链蒙特卡罗方法的元素相结合的新方法RJUKF。该方法能够同时对连续参数和分类参数进行同化。与类似的混合状态估计技术相比，RJUKF具有在线（即实时）应用程序足够高效的优势。通过对一群人穿过火车站的仿真，证明了这种新方法能够估计他们当前的位置（一个连续的高斯变量）和他们选择的目的地（一个分类参数）。该方法为使用基于主体的模型作为管理繁忙场所（如公共交通枢纽、购物中心或商业街）人群的工具做出了宝贵贡献。
Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time. However, existing methods face difficulties working with categorical parameters, which are common in agent based models. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. real-time) application. The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). This method makes a valuable contribution towards the use of agent-based models as tools for the management of crowds in busy places such as public transport hubs, shopping centres, or high streets.