基于泛函主成分分析的行人动力学模型评价
Assessment of Models for Pedestrian Dynamics with Functional Principal Component Analysis
摘要：
许多基于智能体的行人流仿真方法已经被提出。在应用这些模型时，例如\在疏散研究中，这些模型的质量和可靠性至关重要。行人轨迹是函数数据，因此函数主成分分析是评估行人流模型质量的自然工具。在这篇文章中，我们进行功能PCA的行人通过瓶颈的轨迹。我们基准两个代理的行人流量模型对实验数据使用PCA平均和随机特征。功能PCA被证明是一个有效的工具，以检测模拟和实验之间的偏差，并评估行人模型的质量。
英文摘要：
Many agent based simulation approaches have been proposed for pedestrian flow. As such models are applied e.g.\ in evacuation studies, the quality and reliability of such models is of vital interest. Pedestrian trajectories are functional data and thus functional principal component analysis is a natural tool to asses the quality of pedestrian flow models beyond average properties. In this article we conduct functional PCA for the trajectories of pedestrians passing through a bottleneck. We benchmark two agent based models of pedestrian flow against the experimental data using PCA average and stochastic features. Functional PCA proves to be an efficient tool to detect deviation between simulation and experiment and to asses quality of pedestrian models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
