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@INPROCEEDINGS{Seyfried:1050073,
      author       = {Seyfried, Armin},
      title        = {{C}ongestion in crowds – behaviour and risks at high
                      densities and in crowds},
      school       = {George Washington University},
      reportid     = {FZJ-2025-05782},
      year         = {2025},
      abstract     = {Predicting 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.},
      organization  = {Transportation Engineering Seminar
                       Series, Washington D.C. (USA)},
      subtyp        = {Other},
      cin          = {IAS-7},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1050073},
}