001050071 001__ 1050071
001050071 005__ 20251223202202.0
001050071 037__ $$aFZJ-2025-05781
001050071 041__ $$aEnglish
001050071 1001_ $$0P:(DE-Juel1)132266$$aSeyfried, Armin$$b0$$ufzj
001050071 1112_ $$aPerception and action seminar$$cProvidence$$wUSA
001050071 245__ $$aDynamics of moving crowds - Transport, behaviour and risks at high densities$$f2025-03-12 - 
001050071 260__ $$c2025
001050071 3367_ $$033$$2EndNote$$aConference Paper
001050071 3367_ $$2DataCite$$aOther
001050071 3367_ $$2BibTeX$$aINPROCEEDINGS
001050071 3367_ $$2ORCID$$aLECTURE_SPEECH
001050071 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1766491216_3772$$xInvited
001050071 3367_ $$2DINI$$aOther
001050071 502__ $$cBrown University
001050071 520__ $$aKnowledge about the dynamic in crowds is useful for planning events, transportation hubs, or escape routes in buildings. In this context, the first part of the lecture introduces transport characteristics of crowds.  This includes collective phenomena, the relationship between density, speed and flow as well as congestion at bottlenecks. State of the art models represent pedestrian as two-dimensional objects (e.g., circles, ellipses, etc.) and are thus able to predict congestion in complex path networks. However, they reach their limits when it comes to describing crowds at high densities. An insight into these dynamics is gained by witness statements from the Love Parade in Duisburg, an event in which 31 people died. The analysis shows how people behave in crowds and how the loss of balance lead to life-threatening situations.  The last part of the talk introduces a methodology for collecting data that provides a three-dimensional description of the movement and interaction of bodies (torsos and limbs) in crowds. This data forms the basis to develop hybrid AI models in which pedestrians interact as three-dimensional objects.
001050071 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001050071 909CO $$ooai:juser.fz-juelich.de:1050071$$pVDB
001050071 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132266$$aForschungszentrum Jülich$$b0$$kFZJ
001050071 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001050071 9141_ $$y2025
001050071 920__ $$lyes
001050071 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
001050071 980__ $$atalk
001050071 980__ $$aVDB
001050071 980__ $$aI:(DE-Juel1)IAS-7-20180321
001050071 980__ $$aUNRESTRICTED