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Prediction of pedestrian dynamics in complex architectures with artificial neural networks
| 题名： | Prediction of pedestrian dynamics in complex architectures with artificial neural networks |
| 正文语种： | 英文 |
| 作者： | Antoine Tordeux;Mohcine Chraibi;Armin Seyfried;Andreas Schadschneider |
| 作者单位： | University of Wuppertal |
| 关键词： | Artificial neural network; complex architecture; prediction of pedestrian dynamics |
| 摘要： | 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, comers, bottle necks, 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. |
| 出版日期： | 2020 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | Vol24 |
| 期： | No01-06 |
| 页码： | 556-568 |
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