research-article

A Data Assimilation Framework for Discrete Event Simulations

Authors:

Peisheng WuAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 29, Issue 3

Article No.: 17, Pages 1 - 26

https://doi.org/10.1145/3301502

Published: 18 June 2019 Publication History

Abstract

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 DES 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.

References

[1]

F. Bouttier and P. Courtier. 1999. Data Assimilation Concepts and Methods. Meteorological Training Course Lecture Series. European Centre for Medium-Range Weather Forecasts, Reading, England.

[2]

R. H. Reichle. 2008. Data assimilation methods in the earth sciences. Adv. Water Res. 31, 11 (2008), 1411--1418.

[3]

F. Bai, S. Guo, and X. Hu. 2011. Towards parameter estimation in wildfire spread simulation based on sequential monte carlo methods. In Proceedings of 44th Annual Simulation Symposium (ANSS’11) and 2011 Spring Simulation Multiconference (SpringSim'11), 159—166.

Digital Library

[4]

Y. Long and X. Hu. 2017. Spatial partition-based particle filtering for data assimilation in wildfire spread simulation. ACM Trans. Spatial Algor. Syst. 3, 2, Article 5 (2017).

Digital Library

[5]

B. P. Zeigler, H. Praehofer, and Tag G. Kim. 2000. Theory of Modeling and Simulation. Academic Press.

Digital Library

[6]

H. Xue, F. Gu, and X. Hu. 2012. Data assimilation using sequential monte carlo methods in wildfire spread simulation. ACM Trans. Model. Comput. Simul. 22, 4, Article 23 (2012).

Digital Library

[7]

F. Gu and X. Hu. 2008. Towards applications of particle filter in wildfire spread simulation. In Proceedings of the 2008 Winter Simulation Conference (WSC’08).

[8]

D. Arnaud, N. D. Freitas, and N. Gordon. 2001. An introduction to sequential monte carlo methods. In Sequential Monte Carlo Methods in Practice. Springer, New York, NY, 3--14.

[9]

Arulampalam, M. Sanjeev, S. Maskell, N. Gordon, and T. Clapp. 2002. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Sign. Process. 50, 2 (2002), 174--188.

Digital Library

[10]

X. Hu. 2011. Dynamic data driven simulation. SCS M8S Magazine. January.

[11]

R. Fujimoto, D. Lunceford, E. Page, and A. M. Uhrmacher (Eds). 2002. Grand challenges for modeling and simulation: Dagstuhl report. Technical Report 350, Schloss Dagstuhl. Seminar No. 02351.

[12]

H. Aydt, S. J. Turner, W. Cai, and M. Y. H. Low. 2009. Research issues in symbiotic simulation. In Proceedings of the 2009 Winter Simulation Conference (WSC’09). 1213--1222.

[13]

H. Aydt, W. Cai, S. J. Turner, and B. P. Gan. 2011. Symbiotic simulation for optimisation of tool operations in semiconductor manufacturing. In Proceedings of the 2011 Winter Simulation Conference (WSC’11). 2088--2099.

Digital Library

[14]

E. Kalnay. 2003. Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press.

[15]

J. A. Carton and B. S. Giese. 2008. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Month. Weather Rev. 136, 8 (2008), 2999--3017.

[16]

M. P. Clark, D. E. Rupp, R. A. Woods, X. Zheng, R. P. Ibbitt, A. G. Slater, J. Schmidt, and M. J. Uddstrom. 2008. Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Adv. Water Res. 31, 10 (2008), 1309--1324.

[17]

H. E. Emara-Shabaik, Y. A. Khulief, and I. Hussaini, 2002. A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation. In Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 216, Vol. 6. 497--512. https://journals.sagepub.com/home/pii.

[18]

J. Mandel, Lynn S. Bennethum, Jonathan D. Beezley, Janice L. Coen, Craig C. Douglas, M. Kim, and A. Vodacek. 2008. A Wildfire Model with Data Assimilation. CCM Report 233.

[19]

Y. Wang and M. Papageorgiou. 2005. Real-time freeway traffic state estimation based on extended kalman filter: A general approach. Transport. Res. B 39, 2 (2005), 141--167

[20]

A. H. Jazwinski. 2007. Stochastic Processes and Filtering Theory. Dover Publications, London, 376.

[21]

S. J. Julier and J. K. Uhlmann. 2004. Unscented filtering and nonlinear estimation. Proc. IEEE 92, 3 (2004), 401--422.

[22]

G. Evensen. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res. 99, C5 (1994), 10143--10162.

[23]

O. Talagrand and P. Courtier. 1987. Variational assimilation of meteorological observations with the adjoint vorticity equation. Theory Quart. J. R. Meteorol. Soc. I 113, 478 (1987), 1311--1328.

[24]

S. E. Cohn, A. d. Silva, J. Guo, M. Sienkiewicz, and D. Lamich. 1998. Assessing the effects of data selection with the DAO physical-space statistical analysis system. Month. Weather Rev. 126, 11 (1998), 2913--2926.

[25]

S. Noh, Y. Tachikawa, M. Shiiba, and S. Kim. 2011. Applying sequential Monte Carlo methods into a distributed hydrologic model: Lagged particle filtering approach with regularization. Hydrol. Earth Syst. Sci 15, 10 (2011), 3237--3251.

[26]

J. Gonzalez, J. Blanco, C. Galindo, A. Ortiz-de-Galisteo, Juan A. Fernandez-Madrigal, Francisco A. Moreno, and Jorge L. Martinez. 2009. Mobile robot localization based on ultra-wide-band ranging: A Particle filter approach. Robot. Auton. Syst. 57, 5 (2009), 496--507.

Digital Library

[27]

N. Ahmed, M. Rutten, T. Bessell, Salil S. Kanhere, N. Gordon, and S. Jha. 2010. Detection and tracking using particle filter-based wireless sensor networks. IEEE Trans. Mobile Comput. 9, 9 (2010), 1332--1345.

Digital Library

[28]

M. Wang and X. Hu. 2015. Data assimilation in agent based simulation of smart environment using particle filters. Simul. Model. Pract. Theory 56 (2015), 36--54.

[29]

C. M. J. Tampere and L. H. Immers. 2007. An extended kalman filter application for traffic state estimation using CTM with implicit mode switching and dynamic parameters. In Proceedings of the Intelligent Transportation Systems Conference (ITSC'07). IEEE, 209--216.

[30]

H. Chen, Hesham A. Rakha, and S. Sadek. 2011. Real-time freeway traffic state prediction: A particle filter approach. In Proceedings of the 14th International IEEE Conference Intelligent Transportation Systems (ITSC’11). 626--631.

[31]

X. Yan, F. Gu, X. Hu, and C. Engstrom. 2013. Dynamic data driven event reconstruction for traffic simulation using sequential monte carlo methods. In Proceeding of the Winter Simulation Conference.

Digital Library

[32]

M. Treiber, and A. Kesting. 2010. An open-source microscopic traffic simulator. IEEE Intell. Transport. Syst. Mag. 2, 3 (2010), 6--13.

[33]

J. Liu and R. Chen. 1998. Sequential monte carlo methods for dynamic systems. J. Am. Stat. Assoc. 93, 443 (1998), 1032--1044.

[34]

N. J. Gordon, D. J. Salmond, and A. F. M. Smith. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc. Radar Sign. Process. 140, 2 (1993), 107--113.

[35]

R. Douc and O. Cappé. 2005. Comparison of resampling schemes for particle filtering. In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA’05).

[36]

M. Xue, M. Tong, and Kelvin K. Droegemeier. 2006. An OSSE framework based on the ensemble square-root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecast. J. Atmos. Ocean. Technol. 23 (2006), 46--66.

[37]

DEVS-Java Reference Guide. Retrieved from www.acims.arizona.edu.

[38]

L. Mihaylova, R. Boel, and A. Hegyi. 2007. Freeway traffic estimation within particle filtering framework. Automatica 43, 2 (2007), 290--300.

Digital Library

[39]

X. Xie and A. Verbraeck. 2018. A particle filter-based data assimilation framework for discrete event simulations. Simulation.

[40]

X. Xie. 2018. Data Assimilation in Discrete Event Simulations. Ph.D. Dissertation, Delft University of Technology.

[41]

Y. Cheng and S. Reich. 2015. Assimilating data into scientific models: An optimal coupling perspective. In Frontiers in Applied Dynamical Systems: Reviews and Tutorials, Volume 2: Nonlinear Data Assimilation. Springer-Verlag, New York, 75--118.

[42]

Dinh T. Pham. 2001. Stochastic methods for sequential data assimilation in strongly nonlinear systems. Month. Weather Rev. 129, 5 (2001), 1194--1207.

[43]

S. Rai and X. Hu. 2018. Building occupancy simulation and data assimilation using a graph-based agent-oriented model. Physica A 502 (2018), 270--287.

[44]

C. Synder, T. Bengtsson, P. Bickel, and J. Anderson. 2008. Obstacles to high-dimensional particle filtering. Month. Weather Rev. 136, 12 (2008), 4629--4640.

[45]

R. Boel and L. Mihaylova. 2006. A compositional stochastic model for real-time freeway traffic simulation. Transport. Res. B 40, 4 (2006), 319--334.

[46]

X. Xie, H. van Lint, and A. Verbraeck. 2018. A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters. Transportation Research Part C: Emerging Technologies 92 (2018), 364--391.

Cited By

Hu XYan M(2024)Data assimilation for online model calibration in discrete event simulationSimulation10.1177/00375497231221578100:6(529-544)Online publication date: 1-Jun-2024
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NAGAHARA SKAIHARA TFUJII NKOKURYO D(2023)Data-driven and multi-scale modeling approach for production system simulation (Fundamental study on model identification for single process systems)生産システムシミュレーションにおけるデータ駆動型マルチスケールモデリングアプローチの提案（単工程システムを対象としたモデル同定に関する基礎検討）Transactions of the JSME (in Japanese)10.1299/transjsme.23-0020589:928(23-00205-23-00205)Online publication date: 2023
https://doi.org/10.1299/transjsme.23-00205
Tan WSeok MCai W(2023)Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production SystemsProceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3573900.3591112(73-83)Online publication date: 21-Jun-2023
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Index Terms

A Data Assimilation Framework for Discrete Event Simulations
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Data assimilation
      2. Discrete-event simulation

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cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 29, Issue 3

July 2019

151 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/3341298

Editor:
Adelinde M. Uhrmacher
University of Rostock, Germany

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 18 June 2019

Accepted: 01 December 2018

Revised: 01 September 2018

Received: 01 January 2018

Published in TOMACS Volume 29, Issue 3

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Cited By

Hu XYan M(2024)Data assimilation for online model calibration in discrete event simulationSimulation10.1177/00375497231221578100:6(529-544)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1177/00375497231221578
NAGAHARA SKAIHARA TFUJII NKOKURYO D(2023)Data-driven and multi-scale modeling approach for production system simulation (Fundamental study on model identification for single process systems)生産システムシミュレーションにおけるデータ駆動型マルチスケールモデリングアプローチの提案（単工程システムを対象としたモデル同定に関する基礎検討）Transactions of the JSME (in Japanese)10.1299/transjsme.23-0020589:928(23-00205-23-00205)Online publication date: 2023
https://doi.org/10.1299/transjsme.23-00205
Tan WSeok MCai W(2023)Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production SystemsProceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3573900.3591112(73-83)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3573900.3591112
Tolk A(2022)Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep UncertaintyInformation10.3390/info1310046913:10(469)Online publication date: 30-Sep-2022
https://doi.org/10.3390/info13100469
Zhu JYao YTang WZhang H(2022)Dynamic Parameter Calibration Framework for Opinion Dynamics ModelsEntropy10.3390/e2408111224:8(1112)Online publication date: 12-Aug-2022
https://doi.org/10.3390/e24081112
Hu X(2022)Data Assimilation For Simulation-Based Real-Time Prediction/Analysis2022 Annual Modeling and Simulation Conference (ANNSIM)10.23919/ANNSIM55834.2022.9859329(404-415)Online publication date: 18-Jul-2022
https://doi.org/10.23919/ANNSIM55834.2022.9859329
Huang YXie XCho YVerbraeck A(2022)Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditionsSIMULATION10.1177/0037549722114398899:4(403-415)Online publication date: 26-Dec-2022
https://doi.org/10.1177/00375497221143988
Naing HCai WNan HTiantian WLiang Y(2022)Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term MemoryACM Transactions on Modeling and Computer Simulation10.1145/355855532:4(1-27)Online publication date: 5-Nov-2022
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Wang CLi Y(2022)Digital-Twin-Aided Product Design Framework For IoT PlatformsIEEE Internet of Things Journal10.1109/JIOT.2021.31007969:12(9290-9300)Online publication date: 15-Jun-2022
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Zhang NBahsoon RTziritas NTheodoropoulos G(2022)Explainable Human-in-the-Loop Dynamic Data-Driven Digital TwinsDynamic Data Driven Applications Systems10.1007/978-3-031-52670-1_23(233-243)Online publication date: 6-Oct-2022
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