TY  - CONF
AU  - Tordeux, Antoine
AU  - Chraibi, Mohcine
AU  - Seyfried, Armin
AU  - Schadschneider, Andreas
TI  - Prediction of Pedestrian Speed with Artificial Neural Networks
CY  - Cham
PB  - Springer International Publishing
M1  - FZJ-2019-05417
SP  - 327-335
PY  - 2019
AB  - 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.
T2  - Traffic and Granular Flow 2017
CY  - 19 Jul 2017 - 22 Jul 2017, Washington (USA)
Y2  - 19 Jul 2017 - 22 Jul 2017
M2  - Washington, USA
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:000653682700036
DO  - DOI:10.1007/978-3-030-11440-4_36
UR  - https://juser.fz-juelich.de/record/866251
ER  -