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International Conference on Traffic and Granular Flow

TGF 2017: Traffic and Granular Flow '17 pp 327–335Cite as

Prediction of Pedestrian Speed with Artificial Neural Networks

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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.

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Acknowledgements

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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Authors and Affiliations

  1. Forschungszentrum Jülich, Jülich, Germany

    Antoine Tordeux, Mohcine Chraibi & Armin Seyfried

  2. University of Wuppertal, Wuppertal, Germany

    Antoine Tordeux & Armin Seyfried

  3. University of Cologne, Cologne, Germany

    Andreas Schadschneider

Authors
  1. Antoine Tordeux
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  2. Mohcine Chraibi
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  3. Armin Seyfried
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  4. Andreas Schadschneider
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Corresponding author

Correspondence to Antoine Tordeux .

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Editors and Affiliations

  1. Dept. of Civil & Environ. Engineering, George Washington University, Washington, DC, USA

    Samer H. Hamdar

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Tordeux, A., Chraibi, M., Seyfried, A., Schadschneider, A. (2019). Prediction of Pedestrian Speed with Artificial Neural Networks. In: Hamdar, S. (eds) Traffic and Granular Flow '17. TGF 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-11440-4_36

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