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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30720
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dc.contributor.authorAppel, Jan-
dc.contributor.authorWeiler, Andreas-
dc.date.accessioned2024-05-23T09:15:12Z-
dc.date.available2024-05-23T09:15:12Z-
dc.date.issued2024-03-25-
dc.identifier.issn1613-0073de_CH
dc.identifier.urihttps://ceur-ws.org/Vol-3651/BMDA-9.pdfde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30720-
dc.description.abstractThis demo paper introduces XCrowd, a solution aiming to bridge the gap between theoretical crowd movement optimisation and real world operations. By simulating crowd movements on graph networks, XCrowd provides an efficient tool to evaluate crowd behaviour under specific conditions and, for example, validate the efficiency of a certain environment layout. We discuss the methodology, implementation, and potential implications for crowd movement management, as well as outline future directions, including scalability, integration with scheduling models, and addressing real-world dynamic factors.de_CH
dc.language.isoende_CH
dc.publisherRWTH Aachen Universityde_CH
dc.rightshttps://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectReal-time crowd simulationde_CH
dc.subjectCrowd movement optimizationde_CH
dc.subjectGraph networkde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleXCrowd : a realistic crowd simulation tool for efficient movement managementde_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeAachende_CH
dc.identifier.doi10.21256/zhaw-30720-
zhaw.conference.details6th International Workshop on Big Mobility Data Analytics (BMDA) during EDBT/ICDT Joint Conference, Paestum, Italy, 25 March - 28 March 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conferencede_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIntelligent Information Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Appel, J., & Weiler, A. (2024, March 25). XCrowd : a realistic crowd simulation tool for efficient movement management. Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference Co-Located with the EDBT/ICDT 2024 Joint Conference. https://doi.org/10.21256/zhaw-30720
Appel, J. and Weiler, A. (2024) ‘XCrowd : a realistic crowd simulation tool for efficient movement management’, in Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference. Aachen: RWTH Aachen University. Available at: https://doi.org/10.21256/zhaw-30720.
J. Appel and A. Weiler, “XCrowd : a realistic crowd simulation tool for efficient movement management,” in Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference, Mar. 2024. doi: 10.21256/zhaw-30720.
APPEL, Jan und Andreas WEILER, 2024. XCrowd : a realistic crowd simulation tool for efficient movement management. In: Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference [online]. Conference presentation. Aachen: RWTH Aachen University. 25 März 2024. Verfügbar unter: https://ceur-ws.org/Vol-3651/BMDA-9.pdf
Appel, Jan, and Andreas Weiler. 2024. “XCrowd : A Realistic Crowd Simulation Tool for Efficient Movement Management.” Conference presentation. In Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference Co-Located with the EDBT/ICDT 2024 Joint Conference. Aachen: RWTH Aachen University. https://doi.org/10.21256/zhaw-30720.
Appel, Jan, and Andreas Weiler. “XCrowd : A Realistic Crowd Simulation Tool for Efficient Movement Management.” Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference Co-Located with the EDBT/ICDT 2024 Joint Conference, RWTH Aachen University, 2024, https://doi.org/10.21256/zhaw-30720.


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