% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Cordes:1032289,
      author       = {Cordes, Jakob},
      title        = {{C}lassification of {P}edestrian {S}treams: {F}rom
                      {E}mpirics to {M}odelling},
      volume       = {66},
      school       = {Köln University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2024-06128},
      isbn         = {978-3-95806-780-6},
      series       = {IAS Series},
      pages        = {vii, 176},
      year         = {2024},
      note         = {Dissertation, Köln University, 2024},
      abstract     = {Pedestrian streams are ubiquitous, but very diverse.
                      Classifying them is critical in practice for crowd
                      management but also for the organization and validation of
                      models. As far as an empirical classification is concerned,
                      a robust method is still lacking. But also in terms of a
                      theoretical description, a large number of models coexist
                      with an ill-defined range of applicability. In this thesis,
                      these problems are addressed in two ways. First, by studying
                      crowds in their one-dimensional limit, namely Single-File
                      motion, which allows for a better understanding of
                      conceptual problems in models. Second, by drawing
                      inspiration from fluid dynamics, where dimensionless numbers
                      such as the Reynolds number help to classify flows.
                      Single-File motion exhibits interesting collective effects,
                      such as stop-and-go waves, which are validation benchmarks
                      for any agent-based modeling approach of traffic systems. We
                      investigate different classes of models by examining the
                      influence of different parameters, including time-gap,
                      anticipation time, and reaction time - sometimes revealing
                      surprising connections between well-known modeling
                      approaches. Then the wide range of phenomena encountered in
                      crowds is organized by introducing two dimensionless numbers
                      rooted in psychological and biomechanical considerations:
                      the Intrusion number based on the preservation of personal
                      space and the Avoidance number based on the anticipation of
                      collisions. Using an extensive data set we show that these
                      two numbers delineate regimes in which different variables
                      characterize the crowd’s arrangement, namely, Euclidean
                      distances at low Avoidance number and times-to-collision at
                      low Intrusion number. Based on these results, a fairly
                      general perturbative expansion of the individual pedestrian
                      dynamics around the non-interacting state is performed.
                      Simulations confirm that this expansion performs well in its
                      expected regime of applicability. This is also relevant for
                      the larger class of agent-based crowd models as their
                      equations of motion typically depend on variants of the
                      Intrusion number or the Avoidance number. Simulations show
                      that the occurrence of the Intrusion number and Avoidance
                      number in these models limits their range of applicability
                      to specific regimes of crowd motion.},
      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)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2024-06128},
      url          = {https://juser.fz-juelich.de/record/1032289},
}