Congestion Number Analysis of Cross-Flow Dynamics: Experimental Data and Simulations

F. Zanlungo, Zeynep Yucel, Claudio Feliciani, K. Nishinari, Takayuki Kanda
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Abstract

We recently proposed the "Congestion Number" (CN) as a metricto evaluate the state of a pedestrian crowd. Such metric, whose definition is based on the gradient of the rotor of the crowd velocity field, appears to provide additional information with respect to traditional metrics based on pedestrian density and flow. We also published two works on the dynamics of orthogonally crossing pedestrian flows under different density regimes. In the first manuscript we analysed experimental data by using traditionalobservables such as density, velocity and relative position between pedestrians, along with less explored ones such as body orientation. In the second one we proposed a hierarchy of simulation models to reproduce the cross-flow dynamics, and used the aforementioned observables to compare such models. Based on theoretical considerations and analysis of real world data, we believe the crossing flow setting to be a good arena to test the CN metric, and in this work we perform a CN analysis on the empirical and simulation data. Results show that simulation models, which reproduced almost perfectly the density time dependence of the pedestrian crowd, fail to reproduce the CN one. Actually, models "outperform" the pedestrian crowd when analysed using CN. These preliminary results suggest that the CN metric may provide useful information not only in crowd assessment but also in model evaluation.
交叉流动态拥塞数分析:实验数据与模拟
我们最近提出了 "拥堵数"(CN)作为评估行人群状态的指标。这种度量的定义基于人群速度场转子的梯度,与基于行人密度和流量的传统度量相比,似乎能提供更多信息。我们还发表了两篇关于不同密度下正交行人流动态的文章。在第一篇手稿中,我们利用行人之间的密度、速度和相对位置等传统观测数据,以及身体方向等较少探索的观测数据,对实验数据进行了分析。基于理论考虑和对现实世界数据的分析,我们认为交叉流环境是测试 CN 指标的良好舞台,在这项工作中,我们对经验数据和模拟数据进行了 CN 分析。结果表明,模拟模型几乎完美地再现了行人群的密度时间依赖性,但却无法再现 CN 模型。实际上,在使用 CN 进行分析时,模型 "优于 "行人群。这些初步结果表明,CN 指标不仅可为人群评估提供有用信息,还可为模型评估提供有用信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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