Journal Article

FZJ-2019-06354

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Prediction of pedestrian dynamics in complex architectures with artificial neural networks

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

2020
Taylor and Francis, Inc. Philadelphia, Pa.

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Please use a persistent id in citations: http://hdl.handle.net/2128/26024  doi:10.1080/15472450.2019.1621756

Abstract: Pedestrian behavior tends to depend on the type of facility. The flow at bottlenecks, for instance, can exceed the maximal rates observed in straight corridors. Consequently, accurate predictions of pedestrians movements in complex buildings including corridors, corners, bottlenecks, or intersections are difficult tasks for minimal models with a single setting of the parameters. Artificial neural networks are robust algorithms able to identify various types of patterns. In this paper, we will investigate their suitability for forecasting of pedestrian dynamics in complex architectures. Therefore, we develop, train, and test several artificial neural networks for predictions of pedestrian speeds in corridor and bottleneck experiments. The estimations are compared with those of a classical speed-based model. The results show that the neural networks can distinguish the two facilities and significantly improve the prediction of pedestrian speeds.

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Contributing Institute(s):

1. Zivile Sicherheitsforschung (IAS-7)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)

Appears in the scientific report 2020

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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