This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/23263 in citations.
Please use the identifier: http://dx.doi.org/10.1007/978-3-030-11440-4_36 in citations.
Prediction of Pedestrian Speed with Artificial Neural Networks
Prediction of Pedestrian Speed with Artificial Neural Networks
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 t...
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Personal Name(s): | Tordeux, Antoine |
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Chraibi, Mohcine (Corresponding author) / Seyfried, Armin / Schadschneider, Andreas | |
Contributing Institute: |
Zivile Sicherheitsforschung; IAS-7 |
Published in: |
Traffic and Granular Flow '17 / Hamdar, Samer H. (Editor) ; Cham : Springer International Publishing, 2019, Chapter 36 ; ISBN: 978-3-030-11439-8 |
Imprint: |
Cham
Springer International Publishing
2019
|
Physical Description: |
327-335 |
DOI: |
10.1007/978-3-030-11440-4_36 |
Conference: | Traffic and Granular Flow 2017, Washington (USA), 2017-07-19 - 2017-07-22 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Computational Science and Mathematical Methods |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1007/978-3-030-11440-4_36 in citations.
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. |