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@PHDTHESIS{Subaih:1046651,
      author       = {Subaih, Rudina},
      title        = {{S}ingle-file {M}ovement: {L}iterature {R}eview,
                      {E}mpirical {A}nalysis with {A}rtificial {N}eural
                      {N}etworks, and {M}odeling},
      volume       = {73},
      school       = {Wuppertal},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-03883},
      isbn         = {978-3-95806-843-8},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {x, 115},
      year         = {2025},
      note         = {Dissertation, Wuppertal, 2025},
      abstract     = {Several influential factors impact pedestrian movement
                      within crowds, making their analysis complex and
                      challenging. To address this, we employ a simplified system
                      referred to as single-file. In this system, pedestrians walk
                      along a narrow path without overtaking, ensuring that the
                      order of individuals remains constant. This setup reduces
                      the number of variables and allows for a focused examination
                      of the specific factors researchers aim to investigate in
                      pedestrian dynamics. Given the significance of single-file
                      movement in understanding complex movement behaviors, this
                      thesis demonstrates the importance of studying single-file
                      systems. Furthermore, this thesis analyzes the interaction
                      ranges in single-file systems by incorporating into the
                      speed model the influence of both the pedestrian ahead and
                      the one behind, taking into account their respective
                      distances and speeds. This novel approach, detailed in
                      Publications II and III, enhances the accuracy of modeling
                      in single-file movement. This cumulative thesis comprises
                      three publications aimed at investigating pedestrians’
                      single-file movement. Publication I provides a comprehensive
                      review of experiments on single-file pedestrian movement,
                      emphasizing its importance. The review covers the historical
                      background of single-file movement studies and offers
                      insights from human and non-human traffic systems. The
                      publication also elaborates on various experimental setups
                      and data collection methods and discusses factors
                      influencing pedestrian movement. Additionally, the study
                      introduces a new Python-based tool,
                      SingleFileMovementAnalysis, designed to analyze the data of
                      pedestrian movement, particularly head trajectories, which
                      helps prepare and calculate movement quantities such as
                      speed, density, and headway. The publication offers an
                      approach to experimental data analysis and suggests future
                      directions for research in this field. In Publication II,
                      the factors influencing pedestrian movement in single-file
                      experiments are explored. Feed-forward neural networks are
                      utilized to predict individual pedestrians’ speeds, using
                      various combinations of distances and interaction ranges
                      with neighboring pedestrians. Therefore, the influence of
                      introducing the distance behind into the speed model is
                      analyzed, and the predicted individual speeds using
                      different influential factors are evaluated and compared.
                      Inspired by the results from the statistical investigations
                      conducted in PublicationII, Publication III introduces a new
                      microscopic speed model that considers the relative
                      distances to the nearest neighbors both behind and ahead for
                      single-file movement. A fine-tuning of the weighted
                      asymmetry parameters is applied, and the stability of the
                      new model is analyzed. Furthermore, a numerical simulation
                      of one-dimensional movement evaluates the proposed model.},
      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-2025-03883},
      url          = {https://juser.fz-juelich.de/record/1046651},
}