A mobility network approach to identify and anticipate large crowd gatherings
A mobility network approach to identify and anticipate large crowd gatherings
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用于识别和预测大规模人群聚集的移动网络方法
DOI：
10.1016/j.trb.2018.05.016
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发表时间：
2018-08-01
影响因子：
6.8
通讯作者：
Schich, Maximilian
中科院分区：
文献类型：
--
作者：
Huang, Zhiren;Wang, Pu;Schich, Maximilian
对大型人群聚集的研究将远距离人类流动性与特定地点的行人动态相结合。最近，在理解特定地点范围内人群的集体行为方面取得了实质性进展。然而，就大规模人群聚集的数量而言，人员流动性方面仍然模糊。我们的方法利用数以百万计的、可能实时的地铁和出租车记录形式的高分辨率人类流动数据，揭示了大规模人群聚集中涉及的流动模式。此外，我们通过引入异常移动网络的概念来区分异常移动通量和普通移动通量，其中节点是流量区域，链路是通过 Jensen-Shannon 散度定义的。我们的方法可以轻松识别人群形成的发生、位置和发展阶段。引人注目的是，在异常移动网络中，我们发现高压力人群密度之前有超过临界阈值 IQ 的节点度数 km，通常比最大人群密度早几个小时，这使我们能够通过基于简单网络索引 kin 的极其简单的方法来预测大规模人群聚集。 (C) 2018 Elsevier Ltd. 保留所有权利。
The study of large crowd gatherings combines aspects of longer-range human mobility with site-specific pedestrian dynamics. Recently, substantial progress has been made in understanding the collective behaviors of crowds on the site-specific scale. Yet, the human mobility aspect remains vague in terms of how large crowds come together in the first place. Using high-resolution human mobility data in form of millions, potentially real-time, subway and taxi records, our approach uncovers the mobility patterns involved in large crowd gatherings. In addition, we discriminate anomalous mobility fluxes from ordinary mobility fluxes by introducing the concept of anomalous mobility networks, within which nodes are traffic zones and links are defined via the Jensen-Shannon divergence. Our approach allows for easy identification of occurrence, location and developing stages of crowd formation. Strikingly, within the anomalous mobility networks, we find high-stress crowd density to be preceded by a node in-degree km surpassing the critical threshold IQ, typically preceding the maximum crowd density by a couple of hours, enabling us to anticipate large crowd gatherings via a surprisingly simple approach based on the simple network index kin. (C) 2018 Elsevier Ltd. All rights reserved.