dc.contributor.author |
Suchak, Keiran |
|
dc.contributor.author |
Kieu, Minh |
|
dc.contributor.author |
Oswald, Yannick |
|
dc.contributor.author |
Ward, Jonathan A |
|
dc.contributor.author |
Malleson, Nick |
|
dc.coverage.spatial |
England |
|
dc.date.accessioned |
2024-07-12T01:50:34Z |
|
dc.date.available |
2024-07-12T01:50:34Z |
|
dc.date.issued |
2024-04 |
|
dc.identifier.citation |
(2024). Royal Society Open Science, 11(4), 231553-. |
|
dc.identifier.issn |
2054-5703 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/69207 |
|
dc.description.abstract |
Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under 'normality' to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications. |
|
dc.format.medium |
Electronic-eCollection |
|
dc.language |
eng |
|
dc.publisher |
The Royal Society |
|
dc.relation.ispartofseries |
Royal Society open science |
|
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/4.0/ |
|
dc.subject |
agent-based model |
|
dc.subject |
crowd simulation |
|
dc.subject |
data assimilation |
|
dc.subject |
data-driven agent-based modelling |
|
dc.subject |
ensemble Kalman filter |
|
dc.subject |
46 Information and Computing Sciences |
|
dc.subject |
4602 Artificial Intelligence |
|
dc.subject |
Science & Technology |
|
dc.subject |
Multidisciplinary Sciences |
|
dc.subject |
Science & Technology - Other Topics |
|
dc.subject |
EARTH SYSTEM MODEL |
|
dc.subject |
SIMULATION |
|
dc.subject |
ASSIMILATION |
|
dc.title |
Coupling an agent-based model and an ensemble Kalman filter for real-time crowd modelling |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1098/rsos.231553 |
|
pubs.issue |
4 |
|
pubs.begin-page |
231553 |
|
pubs.volume |
11 |
|
dc.date.updated |
2024-06-05T04:59:07Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
38623082 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/38623082 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
1022804 |
|
pubs.org-id |
Engineering |
|
pubs.org-id |
Civil and Environmental Eng |
|
dc.identifier.eissn |
2054-5703 |
|
dc.identifier.pii |
rsos231553 |
|
pubs.number |
ARTN 231553 |
|
pubs.record-created-at-source-date |
2024-06-05 |
|
pubs.online-publication-date |
2024-04-10 |
|