Pedestrian behavior tends to depend on the type of facility. The flow at bottlenecks, for instance, can exceed the maximal rates observed in straight corridors. Consequently, accurate predictions of pedestrians movements in complex buildings including corridors, corners, bottlenecks, or intersections are difficult tasks for minimal models with a single setting of the parameters. Artificial neural networks are robust algorithms able to identify various types of patterns. In this paper, we will investigate their suitability for forecasting of pedestrian dynamics in complex architectures. Therefore, we develop, train, and test several artificial neural networks for predictions of pedestrian speeds in corridor and bottleneck experiments. The estimations are compared with those of a classical speed-based model. The results show that the neural networks can distinguish the two facilities and significantly improve the prediction of pedestrian speeds.
Prediction of pedestrian dynamics in complex architectures with artificial neural networks
Journal of Intelligent Transportation Systems ; 24 , 6 ; 556-568
01.11.2020
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Probabilistic goal-directed pedestrian prediction by means of artificial neural networks
TIBKAT | 2022
|Pedestrian Detection with Convolutional Neural Networks
British Library Conference Proceedings | 2005
|Pedestrian detection with convolutional neural networks
IEEE | 2005
|British Library Conference Proceedings | 2020
|