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@ARTICLE{Subaih:908881,
      author       = {Subaih, Rudina and Maree, Mohammed and Tordeux, Antoine and
                      Chraibi, Mohcine},
      title        = {{Q}uestioning the {A}nisotropy of {P}edestrian {D}ynamics:
                      {A}n {E}mpirical {A}nalysis with {A}rtificial {N}eural
                      {N}etworks},
      journal      = {Applied Sciences},
      volume       = {12},
      number       = {15},
      issn         = {2076-3417},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2022-02894},
      pages        = {7563 -},
      year         = {2022},
      abstract     = {Identifying the factors that control the dynamics of
                      pedestrians is a crucial step towards modeling and building
                      various pedestrian-oriented simulation systems. In this
                      article, we empirically explore the influential factors that
                      control the single-file movement of pedestrians and their
                      impact. Our goal in this context is to apply feed-forward
                      neural networks to predict and understand the individual
                      speeds for different densities of pedestrians. With
                      artificial neural networks, we can approximate the fitting
                      function that describes pedestrians’ movement without
                      having modeling bias. Our analysis is focused on the
                      distances and range of interactions across neighboring
                      pedestrians. As indicated by previous research, we find that
                      the speed of pedestrians depends on the distance to the
                      predecessor. Yet, in contrast to classical purely
                      anisotropic approaches—which are based on vision fields
                      and assume that the interaction mainly depends on the
                      distance in front—our results demonstrate that the
                      distance to the follower also significantly influences
                      movement. Using the distance to the follower combined with
                      the subject pedestrian’s headway distance to predict the
                      speed improves the estimation by $18\%$ compared to the
                      prediction using the space in front alone.},
      cin          = {IAS-7},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Pilotprojekt zur
                      Entwicklung eines palästinensisch-deutschen Forschungs- und
                      Promotionsprogramms 'Palestinian-German Science Bridge'
                      (01DH16027) / DFG project 446168800 -
                      Multi-Agent-Modellierung der Dynamik von dichten
                      Fußgängermengen: Vorhersagen Verstehen (446168800)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(BMBF)01DH16027 /
                      G:(GEPRIS)446168800},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000839091400001},
      doi          = {10.3390/app12157563},
      url          = {https://juser.fz-juelich.de/record/908881},
}