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Data-driven spatio-temporal discretization for pedestrian flow characterization

机译:数据驱动的时空离散用于行人流表征

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Abstract: We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimensional Voronoi diagrams. Synthetic data is used to empirically investigate the performance of the approach and to illustrate its advantages. Our approach outperforms the considered approaches from the literature in terms of the robustness with respect to the simulation noise and with respect to the sampling frequency. Additionally, the proposed approach is by design (i) independent from an arbitrarily chosen discretization; (ii) appropriate for the multidirectional composition of pedestrian traffic; (iii) able to reflect the heterogeneity of the pedestrian population; and (iv) applicable to pedestrian trajectories described either analytically or as a sample of points.
机译:摘要:我们提出了一种新颖的行人流表征方法。通过数据驱动的时空离散化框架扩展了文献中存在的密度,流量和速度的定义。该框架基于三维Voronoi图。综合数据用于实证研究该方法的性能并说明其优势。就模拟噪声和采样频率的鲁棒性而言,我们的方法优于文献中考虑的方法。另外,所提出的方法是通过设计(i)独立于任意选择的离散化; (ii)适合行人交通的多向组成; (iii)能够反映出行人的异质性; (iv)适用于以分析方式描述或作为点样本描述的行人轨迹。

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