Abstract 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.

中文翻译：

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使用人工神经网络预测复杂结构中的行人动态

摘要 行人行为往往取决于设施的类型。例如，瓶颈处的流量可能超过在直通道中观察到的最大流量。因此，对于具有单一参数设置的最小模型而言，准确预测复杂建筑物（包括走廊、拐角、瓶颈或交叉口）中的行人运动是一项艰巨的任务。人工神经网络是强大的算法，能够识别各种类型的模式。在本文中，我们将研究它们在复杂建筑中预测行人动态的适用性。因此，我们开发、训练和测试了几个人工神经网络，用于预测走廊和瓶颈实验中的行人速度。将估计值与经典的基于速度的模型的估计值进行比较。