Prediction of Pedestrian Speed with Artificial Neural Networks

  • Antoine TordeuxEmail author
  • Mohcine Chraibi
  • Armin Seyfried
  • Andreas Schadschneider
Conference paper


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.



Financial supports by the German Science Foundation (DFG) under grants SCHA 636/9-1 and SE 1789/4-1 are gratefully acknowledged.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antoine Tordeux
    • 1
    • 2
    Email author
  • Mohcine Chraibi
    • 1
  • Armin Seyfried
    • 1
    • 2
  • Andreas Schadschneider
    • 3
  1. 1.Forschungszentrum JülichJülichGermany
  2. 2.University of WuppertalWuppertalGermany
  3. 3.University of CologneCologneGermany

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