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| 001 | 866251 | ||
| 005 | 20210628150959.0 | ||
| 024 | 7 | _ | |a 10.1007/978-3-030-11440-4_36 |2 doi |
| 024 | 7 | _ | |a 2128/23263 |2 Handle |
| 024 | 7 | _ | |a WOS:000653682700036 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-05417 |
| 100 | 1 | _ | |a Tordeux, Antoine |0 P:(DE-Juel1)159135 |b 0 |
| 111 | 2 | _ | |a Traffic and Granular Flow 2017 |g TGF'17 |c Washington |d 2017-07-19 - 2017-07-22 |w USA |
| 245 | _ | _ | |a Prediction of Pedestrian Speed with Artificial Neural Networks |
| 260 | _ | _ | |a Cham |c 2019 |b Springer International Publishing |
| 295 | 1 | 0 | |a Traffic and Granular Flow '17 / Hamdar, Samer H. (Editor) ; Cham : Springer International Publishing, 2019, Chapter 36 ; ISBN: 978-3-030-11439-8 |
| 300 | _ | _ | |a 327-335 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 520 | _ | _ | |a 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. |
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| 588 | _ | _ | |a Dataset connected to CrossRef Book |
| 700 | 1 | _ | |a Chraibi, Mohcine |0 P:(DE-Juel1)132077 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Seyfried, Armin |0 P:(DE-Juel1)132266 |b 2 |
| 700 | 1 | _ | |a Schadschneider, Andreas |0 P:(DE-HGF)0 |b 3 |
| 773 | _ | _ | |a 10.1007/978-3-030-11440-4_36 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/866251/files/ArticleTGF17_NN.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/866251/files/ArticleTGF17_NN.pdf?subformat=pdfa |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
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