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@INPROCEEDINGS{Tordeux:866394,
      author       = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin
                      and Schadschneider, Andreas},
      title        = {{A}rtificial {N}eural {N}etworks {P}redicting {P}edestrian
                      {D}ynamics in {C}omplex {B}uildings},
      volume       = {294},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2019-05548},
      isbn         = {978-3-030-28664-4 (print)},
      series       = {Springer Proceedings in Mathematics $\&$ Statistics},
      pages        = {363 - 372},
      year         = {2019},
      comment      = {Steland, Ansgar (Editor) Chapter 27 ; ISSN:
                      2194-1009=2194-1017 ; ISBN:
                      978-3-030-28664-4=978-3-030-28665-1},
      booktitle     = {Steland, Ansgar (Editor) Chapter 27 ;
                       ISSN: 2194-1009=2194-1017 ; ISBN:
                       978-3-030-28664-4=978-3-030-28665-1},
      abstract     = {The prediction of pedestrian movements in complex buildings
                      is a difficulttask. Recent experiments have shown that the
                      behaviour of pedestrians tends todepend on the type of
                      facility. For instance, flows at bottlenecks often exceed
                      themaximal rates observed in straight corridors. This makes
                      pedestrian behavioursgeometry-dependent. Yet the types of
                      geometries are various, and their systematicidentification
                      in complex buildings is not straightforward. Artificial
                      neural networksare able to identify various types of
                      patterns without supervision. They could be asuitable
                      alternative for forecasts of pedestrian dynamics in complex
                      architectures. Inthis paper, we test this assertion.We
                      develop, train and test artificial neural networksfor the
                      prediction of pedestrian speeds in corridor and bottleneck
                      experiments. Theestimations are compared to those of an
                      elementary speed-based model. The resultsshow that neural
                      networks distinguish the flow characteristics for the two
                      differenttypes of facilities and significantly improve the
                      prediction of pedestrian speeds.},
      month         = {Mar},
      date          = {2019-03-06},
      organization  = {Stochastic Models, Statistics and
                       Their Applications, Dresden (Germany),
                       6 Mar 2019 - 8 Mar 2019},
      cin          = {IAS-7},
      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)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-030-28665-1_27},
      url          = {https://juser.fz-juelich.de/record/866394},
}