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行人动态路线选择中的波动。

Fluctuations in pedestrian dynamics routing choices.

作者信息

Gabbana Alessandro, Toschi Federico, Ross Philip, Haans Antal, Corbetta Alessandro

机构信息

Department of Applied Physics, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

CNR-IAC, Via dei Taurini 19, 00185 Roma, Italy.

出版信息

PNAS Nexus. 2022 Aug 27;1(4):pgac169. doi: 10.1093/pnasnexus/pgac169. eCollection 2022 Sep.

Abstract

Routing choices of walking pedestrians in geometrically complex environments are regulated by the interplay of a multitude of factors such as local crowding, (estimated) time to destination, and (perceived) comfort. As individual choices combine, macroscopic traffic flow patterns emerge. Understanding the physical mechanisms yielding macroscopic traffic distributions in environments with complex geometries is an outstanding scientific challenge, with implications in the design and management of crowded pedestrian facilities. In this work, we analyze, by means of extensive real-life pedestrian tracking data, unidirectional flow dynamics in an asymmetric setting, as a prototype for many common complex geometries. Our environment is composed of a main walkway and a slightly longer detour. Our measurements have been collected during a dedicated high-accuracy pedestrian tracking campaign held in Eindhoven (The Netherlands). We show that the dynamics can be quantitatively modeled by introducing a collective discomfort function, and that fluctuations on the behavior of single individuals are crucial to correctly recover the global statistical behavior. Notably, the observed traffic split substantially departs from an optimal, transport-wise, partition, as the global pedestrian throughput is not maximized.

摘要

在几何形状复杂的环境中,步行行人的路线选择受到多种因素相互作用的调节,如局部拥挤程度、(估计的)到达目的地的时间以及(感知到的)舒适度。随着个体选择的叠加,宏观交通流模式出现。理解在具有复杂几何形状的环境中产生宏观交通分布的物理机制是一项重大的科学挑战,这对拥挤行人设施的设计和管理具有重要意义。在这项工作中,我们通过大量真实的行人跟踪数据,分析了非对称环境中的单向流动动力学,将其作为许多常见复杂几何形状的一个原型。我们的环境由一条主通道和一条稍长的绕行路线组成。我们的测量数据是在荷兰埃因霍温举行的一次专门的高精度行人跟踪活动中收集的。我们表明,通过引入一个集体不适函数可以对动力学进行定量建模,并且单个个体行为的波动对于正确恢复全局统计行为至关重要。值得注意的是,观察到的交通分流与最优的、从运输角度来看的划分有很大偏差,因为全局行人吞吐量并未达到最大化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f756/9802426/ace704ce9f3a/pgac169fig1.jpg

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