000867739 001__ 867739
000867739 005__ 20210130003849.0
000867739 0247_ $$2doi$$a10.1080/15472450.2019.1621756
000867739 0247_ $$2ISSN$$a1547-2442
000867739 0247_ $$2ISSN$$a1547-2450
000867739 0247_ $$2Handle$$a2128/26024
000867739 0247_ $$2altmetric$$aaltmetric:92186582
000867739 0247_ $$2WOS$$aWOS:000542811600001
000867739 037__ $$aFZJ-2019-06354
000867739 082__ $$a380
000867739 1001_ $$0P:(DE-HGF)0$$aTordeux, Antoine$$b0
000867739 245__ $$aPrediction of pedestrian dynamics in complex architectures with artificial neural networks
000867739 260__ $$aPhiladelphia, Pa.$$bTaylor and Francis, Inc.$$c2020
000867739 3367_ $$2DRIVER$$aarticle
000867739 3367_ $$2DataCite$$aOutput Types/Journal article
000867739 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1604326420_30548
000867739 3367_ $$2BibTeX$$aARTICLE
000867739 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000867739 3367_ $$00$$2EndNote$$aJournal Article
000867739 520__ $$aPedestrian behavior tends to depend on the type of facility. The flow at bottlenecks, for instance, can exceed the maximal rates observed in straight corridors. Consequently, accurate predictions of pedestrians movements in complex buildings including corridors, corners, bottlenecks, or intersections are difficult tasks for minimal models with a single setting of the parameters. Artificial neural networks are robust algorithms able to identify various types of patterns. In this paper, we will investigate their suitability for forecasting of pedestrian dynamics in complex architectures. Therefore, we develop, train, and test several artificial neural networks for predictions of pedestrian speeds in corridor and bottleneck experiments. The estimations are compared with those of a classical speed-based model. The results show that the neural networks can distinguish the two facilities and significantly improve the prediction of pedestrian speeds.
000867739 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000867739 588__ $$aDataset connected to CrossRef
000867739 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b1$$eCorresponding author
000867739 7001_ $$0P:(DE-Juel1)132266$$aSeyfried, Armin$$b2
000867739 7001_ $$00000-0002-2054-7973$$aSchadschneider, Andreas$$b3
000867739 773__ $$0PERI:(DE-600)2156104-7$$a10.1080/15472450.2019.1621756$$gp. 1 - 13$$n6$$p556-568$$tJournal of intelligent transportation systems$$v24$$x1024-8072$$y2020
000867739 8564_ $$uhttps://juser.fz-juelich.de/record/867739/files/ArticleNN_JITS_REV2.pdf$$yPublished on 2019-06-04. Available in OpenAccess from 2020-06-04.$$zStatID:(DE-HGF)0510
000867739 8564_ $$uhttps://juser.fz-juelich.de/record/867739/files/ArticleNN_JITS_REV2.pdf?subformat=pdfa$$xpdfa$$yPublished on 2019-06-04. Available in OpenAccess from 2020-06-04.$$zStatID:(DE-HGF)0510
000867739 909CO $$ooai:juser.fz-juelich.de:867739$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000867739 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132077$$aForschungszentrum Jülich$$b1$$kFZJ
000867739 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132266$$aForschungszentrum Jülich$$b2$$kFZJ
000867739 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000867739 9141_ $$y2020
000867739 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000867739 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000867739 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000867739 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000867739 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ INTELL TRANSPORT S : 2017
000867739 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000867739 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000867739 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000867739 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000867739 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000867739 920__ $$lyes
000867739 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
000867739 980__ $$ajournal
000867739 980__ $$aVDB
000867739 980__ $$aUNRESTRICTED
000867739 980__ $$aI:(DE-Juel1)IAS-7-20180321
000867739 9801_ $$aFullTexts