A Data Assimilation Framework for Discrete Event Simulations

被引：11

作者：

Hu, Xiaolin ^{[1
]}

Wu, Peisheng ^{[1
]}

机构：

[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA

来源：

ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION | 2019年 / 29卷 / 03期

关键词：

Data assimilation framework; discrete event simulations; sequential monte carlo methods; SEQUENTIAL DATA ASSIMILATION; MONTE-CARLO METHODS; KALMAN FILTER; PARTICLE; MODEL;

D O I：

10.1145/3301502

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Discrete event simulation (DES) is traditionally used as an offline tool to help users to carry out analysis for complex systems. As real-time sensor data become more and more available, there is increasing interest of assimilating real-time data into DES to achieve on-line simulation to support real-time decision making. This article presents a data assimilation framework that works with DES models. Solutions are proposed to address unique challenges associated with data assimilation for DES. A tutorial example of discrete event road traffic simulation is developed to demonstrate the data assimilation framework as well as principles of data assimilation in general. This article makes contributions to the DE'S community by presenting a data assimilation framework for DES and a concrete tutorial example that helps readers to grasp the details of data assimilation for DES.

引用

页数：26

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