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人类移动模式的级联步行模型。

Cascading walks model for human mobility patterns.

作者信息

Han Xiao-Pu, Wang Xiang-Wen, Yan Xiao-Yong, Wang Bing-Hong

机构信息

Institute of Information Economy, Hangzhou Normal University, Hangzhou 311121, China; Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China.

Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0435, USA; Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.

出版信息

PLoS One. 2015 Apr 10;10(4):e0124800. doi: 10.1371/journal.pone.0124800. eCollection 2015.

Abstract

BACKGROUND

Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies and the prediction of human movements. However, an agent-based model that could reproduce most of empirical observations without priori is still lacking.

METHODOLOGY/PRINCIPAL FINDINGS: In this paper, considering the empirical findings on the correlations of move-lengths and staying time in human trips, we propose a simple model which is mainly based on the cascading processes to capture the human mobility patterns. In this model, each long-range movement activates series of shorter movements that are organized by the law of localized explorations and preferential returns in prescribed region.

CONCLUSIONS/SIGNIFICANCE: Based on the numerical simulations and analytical studies, we show more than five statistical characters that are well consistent with the empirical observations, including several types of scaling anomalies and the ultraslow diffusion properties, implying the cascading processes associated with the localized exploration and preferential returns are indeed a key in the understanding of human mobility activities. Moreover, the model shows both of the diverse individual mobility and aggregated scaling displacements, bridging the micro and macro patterns in human mobility. In summary, our model successfully explains most of empirical findings and provides deeper understandings on the emergence of human mobility patterns.

摘要

背景

揭示人类轨迹中尺度定律和一系列异常现象背后的机制,对于理解许多时空现象具有根本意义。最近,已经提出了几种模型,例如探索-回报模型(宋等人,2010年)和城市间旅行的辐射模型(西米尼等人,2012年),来研究这些异常现象的起源和人类移动的预测。然而,仍然缺乏一种无需先验知识就能重现大多数实证观察结果的基于主体的模型。

方法/主要发现:在本文中,考虑到人类出行中移动长度与停留时间相关性的实证发现,我们提出了一个简单的模型,该模型主要基于级联过程来捕捉人类移动模式。在这个模型中,每次远距离移动都会激活一系列较短的移动,这些移动按照局部探索和在规定区域内优先返回的规律进行组织。

结论/意义:基于数值模拟和分析研究,我们展示了五个以上与实证观察结果高度一致的统计特征,包括几种类型的尺度异常和超慢扩散特性,这意味着与局部探索和优先返回相关的级联过程确实是理解人类移动活动的关键。此外,该模型既展示了个体移动的多样性,又展示了聚合的尺度位移,弥合了人类移动中的微观和宏观模式。总之,我们的模型成功地解释了大多数实证发现,并为人类移动模式的出现提供了更深入的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1f7a/4393089/454adfcd3502/pone.0124800.g001.jpg

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