001050073 001__ 1050073
001050073 005__ 20251223202202.0
001050073 037__ $$aFZJ-2025-05782
001050073 041__ $$aEnglish
001050073 1001_ $$0P:(DE-Juel1)132266$$aSeyfried, Armin$$b0$$ufzj
001050073 1112_ $$aTransportation Engineering Seminar Series$$cWashington D.C.$$wUSA
001050073 245__ $$aCongestion in crowds – behaviour and risks at high densities and in crowds$$f2025-03-20 - 
001050073 260__ $$c2025
001050073 3367_ $$033$$2EndNote$$aConference Paper
001050073 3367_ $$2DataCite$$aOther
001050073 3367_ $$2BibTeX$$aINPROCEEDINGS
001050073 3367_ $$2ORCID$$aLECTURE_SPEECH
001050073 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1766476746_7056$$xOther
001050073 3367_ $$2DINI$$aOther
001050073 502__ $$cGeorge Washington University
001050073 520__ $$aPredicting congestion in pedestrian flows is useful for planning events, transportation hubs, or escape routes in buildings. According to the state of the art, software solutions are based on agent-based models in which pedestrians are represented as two-dimensional objects (e.g., circles, ellipses, etc.). These models are able to predict congestion in complex path networks, but reach their limits when it comes to describing crowds at high densities.The talk will use witness statements from the Love Parade in Duisburg (an event in which 31 people died) to analyse how people behave in crowds and which dynamics lead to life-threatening situations. In a second part, a methodology will be presented for collecting data that provides a three-dimensional description of the movement and interaction of bodies (torsos and limbs) in crowds. This data is used to develop hybrid AI models in which pedestrians interact as three-dimensional objects. Using methods and concepts from social psychology, we are working on models in which the dynamic of motivation changes is described, or which analyse the spread of behaviour in crowds. Empirical data from laboratory experiments confirm the findings from the analysis of witness statements and show how slowly pushing and shoving behaviour spreads in crowds.
001050073 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001050073 909CO $$ooai:juser.fz-juelich.de:1050073$$pVDB
001050073 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132266$$aForschungszentrum Jülich$$b0$$kFZJ
001050073 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$$x0
001050073 9141_ $$y2025
001050073 920__ $$lyes
001050073 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
001050073 980__ $$atalk
001050073 980__ $$aVDB
001050073 980__ $$aI:(DE-Juel1)IAS-7-20180321
001050073 980__ $$aUNRESTRICTED