Data Assimilation in Discrete Event Simulations: A Rollback Based Sequential Monte Carlo Approach

Xu Xie, Alexander Verbraeck, Feng Gu

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
18 Downloads (Pure)


Data assimilation is an analysis technique which aims to incorporate measured observations into a dynamic system model in order to produce accurate estimates of the current state variables of the system. Although data assimilation is conventionally applied in continuous system models, it is also a desired ability for its discrete event counterpart. However, data assimilation has not been well studied in discrete event simulations yet. This paper researches data assimilation problems in discrete event simulations, and proposes a rollback based implementation of the Sequential Monte Carlo (SMC) method – the rollback based SMC method. To evaluate the accuracy of the proposed method, an identical-twin experiment in a discrete event traffic case is carried out and the results are presented and analyzed.
Original languageEnglish
Title of host publicationProceedings of the Symposium on Theory of Modeling Simulation (TMS-DEVS)
Place of PublicationSan Diego, CA, USA
PublisherSociety for Computer Simulation International (SCS)
Number of pages8
ISBN (Print)978-1-5108-2321-1
Publication statusPublished - 2016
EventSpringSim-TMS/DEVS 2016 - Pasadena, United States
Duration: 3 Apr 20166 Apr 2016

Publication series

NameTMS-DEVS '16
PublisherSociety for Computer Simulation International


ConferenceSpringSim-TMS/DEVS 2016
Abbreviated titleTMS/DEVS 2016
Country/TerritoryUnited States

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Data Assimilation
  • Discrete event simulations
  • Sequential Monte Carlo methods
  • Rollback


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