The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models’ parameters in real time and for improving data assimilation results.

中文翻译：

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离散事件模拟中在线模型校准的数据同化

从动态系统收集的实时数据的可用性不断增加，为在线校准仿真模型提供了机会，以提高基于仿真的研究的准确性。需要系统的方法将实时测量数据同化到仿真模型中。本文提出了一种基于粒子滤波器的数据同化方法，以支持离散事件模拟中的在线模型校准。定义了联合状态参数估计问题，并提出了基于粒子滤波器的数据同化算法。所开发的方法应用于单向交通控制系统的离散事件仿真。实验结果证明了所开发方法实时校准模拟模型参数和改善数据同化结果的有效性。