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@ARTICLE{Tordeux:867739,
      author       = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin
                      and Schadschneider, Andreas},
      title        = {{P}rediction of pedestrian dynamics in complex
                      architectures with artificial neural networks},
      journal      = {Journal of intelligent transportation systems},
      volume       = {24},
      number       = {6},
      issn         = {1024-8072},
      address      = {Philadelphia, Pa.},
      publisher    = {Taylor and Francis, Inc.},
      reportid     = {FZJ-2019-06354},
      pages        = {556-568},
      year         = {2020},
      abstract     = {Pedestrian behavior tends to depend on the type of
                      facility. The flow at bottlenecks, for instance, can exceed
                      the maximal rates observed in straight corridors.
                      Consequently, accurate predictions of pedestrians movements
                      in complex buildings including corridors, corners,
                      bottlenecks, or intersections are difficult tasks for
                      minimal models with a single setting of the parameters.
                      Artificial neural networks are robust algorithms able to
                      identify various types of patterns. In this paper, we will
                      investigate their suitability for forecasting of pedestrian
                      dynamics in complex architectures. Therefore, we develop,
                      train, and test several artificial neural networks for
                      predictions of pedestrian speeds in corridor and bottleneck
                      experiments. The estimations are compared with those of a
                      classical speed-based model. The results show that the
                      neural networks can distinguish the two facilities and
                      significantly improve the prediction of pedestrian speeds.},
      cin          = {IAS-7},
      ddc          = {380},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511)},
      pid          = {G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000542811600001},
      doi          = {10.1080/15472450.2019.1621756},
      url          = {https://juser.fz-juelich.de/record/867739},
}