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.

常用的数据同化方法正在适应与基于代理的模型一起使用，目的是允许实时优化以响应新数据. 然而，现有方法在处理分类参数时面临困难，这在基于代理的模型中很常见。本文提出了一种新方法 RJUKF，它将无迹卡尔曼滤波器 (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。所提出的方法能够同时对连续和分类参数进行数据同化。与用于混合状态估计的类似技术相比，RJUKF 具有足够高效的在线（即实时）应用的优势。新方法在一群人穿过火车站的模拟中得到证明，并且能够估计他们的当前位置（连续的高斯变量）和他们选择的目的地（分类参数）。