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@INPROCEEDINGS{Tordeux:866251,
      author       = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin
                      and Schadschneider, Andreas},
      title        = {{P}rediction of {P}edestrian {S}peed with {A}rtificial
                      {N}eural {N}etworks},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2019-05417},
      pages        = {327-335},
      year         = {2019},
      comment      = {Traffic and Granular Flow '17 / Hamdar, Samer H. (Editor) ;
                      Cham : Springer International Publishing, 2019, Chapter 36 ;
                      ISBN: 978-3-030-11439-8},
      booktitle     = {Traffic and Granular Flow '17 /
                       Hamdar, Samer H. (Editor) ; Cham :
                       Springer International Publishing,
                       2019, Chapter 36 ; ISBN:
                       978-3-030-11439-8},
      abstract     = {Pedestrian behaviours tend to depend on the type of
                      facility. Accurate predictions of pedestrian movement in
                      complex geometries (including corridor, bottleneck or
                      intersection) are difficult to achieve for models with few
                      parameters. Artificial neural networks have multiple
                      parameters and are able to identify various types of
                      patterns. They could be a suitable alternative for
                      forecasts. We aim in this paper to present first steps
                      testing this approach. We compare estimations of pedestrian
                      speed with a classical model and a neural network for
                      combinations of corridor and bottleneck experiments. The
                      results show that the neural network is able to
                      differentiate the two geometries and to improve the
                      estimation of pedestrian speeds.},
      month         = {Jul},
      date          = {2017-07-19},
      organization  = {Traffic and Granular Flow 2017,
                       Washington (USA), 19 Jul 2017 - 22 Jul
                       2017},
      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},
      UT           = {WOS:000653682700036},
      doi          = {10.1007/978-3-030-11440-4_36},
      url          = {https://juser.fz-juelich.de/record/866251},
}