Prediction of pedestrian dynamics in complex architectures with artificial neural networks

• Published in: Journal of intelligent transportation systems Vol. 24; no. 6; pp. 556 - 568
• Main Authors: Tordeux, Antoine, Chraibi, Mohcine, Seyfried, Armin, Schadschneider, Andreas
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 01.11.2020; Taylor & Francis Ltd
• Subjects: Algorithms; Artificial neural network; Artificial neural networks; complex architecture; Corridors; Intersections; Neural networks; Parameter identification; Pedestrian traffic flow; prediction of pedestrian dynamics; Task complexity
• ISSN: 1547-2450; 1547-2442
• DOI: 10.1080/15472450.2019.1621756

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.