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000866251 0247_ $$2doi$$a10.1007/978-3-030-11440-4_36
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000866251 037__ $$aFZJ-2019-05417
000866251 1001_ $$0P:(DE-Juel1)159135$$aTordeux, Antoine$$b0
000866251 1112_ $$aTraffic and Granular Flow 2017$$cWashington$$d2017-07-19 - 2017-07-22$$gTGF'17$$wUSA
000866251 245__ $$aPrediction of Pedestrian Speed with Artificial Neural Networks
000866251 260__ $$aCham$$bSpringer International Publishing$$c2019
000866251 29510 $$aTraffic and Granular Flow '17 / Hamdar, Samer H. (Editor)   ; Cham : Springer International Publishing, 2019, Chapter 36 ; ISBN: 978-3-030-11439-8
000866251 300__ $$a327-335
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000866251 520__ $$aPedestrian 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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000866251 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b1$$eCorresponding author
000866251 7001_ $$0P:(DE-Juel1)132266$$aSeyfried, Armin$$b2
000866251 7001_ $$0P:(DE-HGF)0$$aSchadschneider, Andreas$$b3
000866251 773__ $$a10.1007/978-3-030-11440-4_36
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