dc.contributor.author |
Clay, Robert |
|
dc.contributor.author |
Ward, Jonathan A |
|
dc.contributor.author |
Ternes, Patricia |
|
dc.contributor.author |
Kieu, Le-Minh |
|
dc.contributor.author |
Malleson, Nick |
|
dc.date.accessioned |
2021-09-21T22:11:34Z |
|
dc.date.available |
2021-09-21T22:11:34Z |
|
dc.date.issued |
2021-8 |
|
dc.identifier.citation |
Simulation Modelling Practice and Theory 113:102386 01 Dec 2021 |
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dc.identifier.issn |
1569-190X |
|
dc.identifier.uri |
https://hdl.handle.net/2292/56612 |
|
dc.description.abstract |
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. |
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dc.language |
en |
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dc.publisher |
Elsevier BV |
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dc.relation.ispartofseries |
Simulation Modelling Practice and Theory |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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dc.subject |
0102 Applied Mathematics |
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dc.subject |
0802 Computation Theory and Mathematics |
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dc.subject |
0913 Mechanical Engineering |
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dc.title |
Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter |
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dc.type |
Journal Article |
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dc.identifier.doi |
10.1016/j.simpat.2021.102386 |
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pubs.begin-page |
102386 |
|
dc.date.updated |
2021-08-08T22:53:46Z |
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dc.rights.holder |
Copyright: Elsevier BV |
en |
pubs.end-page |
102386 |
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pubs.publication-status |
Published |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.elements-id |
862245 |
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pubs.number |
102386 |
|