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001     866394
005     20210130003435.0
020 _ _ |a 978-3-030-28664-4 (print)
020 _ _ |a 978-3-030-28665-1 (electronic)
024 7 _ |a 10.1007/978-3-030-28665-1_27
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024 7 _ |a 2128/23606
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037 _ _ |a FZJ-2019-05548
100 1 _ |a Tordeux, Antoine
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|e Corresponding author
111 2 _ |a Stochastic Models, Statistics and Their Applications
|g SMSA 2019
|c Dresden
|d 2019-03-06 - 2019-03-08
|w Germany
245 _ _ |a Artificial Neural Networks Predicting Pedestrian Dynamics in Complex Buildings
260 _ _ |a Cham
|c 2019
|b Springer International Publishing
295 1 0 |a Steland, Ansgar (Editor) Chapter 27 ; ISSN: 2194-1009=2194-1017 ; ISBN: 978-3-030-28664-4=978-3-030-28665-1
300 _ _ |a 363 - 372
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a Contribution to a book
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490 0 _ |a Springer Proceedings in Mathematics & Statistics
|v 294
520 _ _ |a The prediction of pedestrian movements in complex buildings is a difficulttask. Recent experiments have shown that the behaviour of pedestrians tends todepend on the type of facility. For instance, flows at bottlenecks often exceed themaximal rates observed in straight corridors. This makes pedestrian behavioursgeometry-dependent. Yet the types of geometries are various, and their systematicidentification in complex buildings is not straightforward. Artificial neural networksare able to identify various types of patterns without supervision. They could be asuitable alternative for forecasts of pedestrian dynamics in complex architectures. Inthis paper, we test this assertion.We develop, train and test artificial neural networksfor the prediction of pedestrian speeds in corridor and bottleneck experiments. Theestimations are compared to those of an elementary speed-based model. The resultsshow that neural networks distinguish the flow characteristics for the two differenttypes of facilities and significantly improve the prediction of pedestrian speeds.
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700 1 _ |a Chraibi, Mohcine
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700 1 _ |a Seyfried, Armin
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700 1 _ |a Schadschneider, Andreas
|0 P:(DE-HGF)0
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773 _ _ |a 10.1007/978-3-030-28665-1_27
856 4 _ |y OpenAccess
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914 1 _ |y 2019
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