Prediction of Pedestrian Speed with Artificial Neural Networks

Antoine Tordeux (), Mohcine Chraibi (), Armin Seyfried () and Andreas Schadschneider ()
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Antoine Tordeux: Forschungszentrum Jülich
Mohcine Chraibi: Forschungszentrum Jülich
Armin Seyfried: Forschungszentrum Jülich
Andreas Schadschneider: University of Cologne

A chapter in Traffic and Granular Flow '17, 2019, pp 327-335 from Springer

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-11440-4_36

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DOI: 10.1007/978-3-030-11440-4_36

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