Prediction of Pedestrian Speed with Artificial Neural Networks

Tordeux, Antoine; Schadschneider, Andreas; Seyfried, Armin; Chraibi, Mohcine

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

Contribution to a conference proceedings/Contribution to a book

FZJ-2019-05417

Prediction of Pedestrian Speed with Artificial Neural Networks

Tordeux, A.FZJ* ; Chraibi, M. (Corresponding author)FZJ* ; Seyfried, A.FZJ* ; Schadschneider, A.

2019
Springer International Publishing Cham

Traffic and Granular Flow '17 / Hamdar, Samer H. (Editor) ; Cham : Springer International Publishing, 2019, Chapter 36 ; ISBN: 978-3-030-11439-8
Traffic and Granular Flow 2017, TGF'17, Washington, USA, 19 Jul 2017 - 22 Jul 2017 Cham : Springer International Publishing 327-335 (2019) [10.1007/978-3-030-11440-4_36]

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Please use a persistent id in citations: http://hdl.handle.net/2128/23263 doi:10.1007/978-3-030-11440-4_36

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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