Prediction of Pedestrian Speed with Artificial Neural Networks View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2019-10-24

AUTHORS

Antoine Tordeux , Mohcine Chraibi , Armin Seyfried , Andreas Schadschneider

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. More... »

PAGES

327-335

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-11440-4_36

DOI

http://dx.doi.org/10.1007/978-3-030-11440-4_36

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1122016269


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