Non-Markovian Character in Human Mobility: Online and Offline
Non-Markovian Character in Human Mobility: Online and Offline
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DOI：
10.1063/1.4922302
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发表时间：
2014-06
期刊：
影响因子：
2.9
通讯作者：
Zhi-Dan Zhao;Shimin Cai;Yang Lu
中科院分区：
文献类型：
--
作者：
Zhi-Dan Zhao;Shimin Cai;Yang Lu
人类流动的动力学表征了人类在日常活动中遵循的轨迹，是从流行病传播到交通预测和信息推荐的基础。在本文中，我们调查了大量的人类活动的数据集，包括在线浏览网站的行为和离线访问塔基于移动的终端之一。从在线和离线的情况下观察到的非马尔可夫字符建议在个人和集体水平的停留时间分布的标度律，分别。此外，我们认为，较低的熵和较高的可预测性，在人类流动性的在线和离线的情况下，可能源于这种非马尔可夫字符。然而，个体熵和可预测性的分布在在线和离线情况下显示出不同程度的非马尔可夫特征。为了解释人类流动性的非马尔可夫特征，我们应用了一个具有三个基本成分（即优先回报、惯性效应和探索）的原型模型来再现线上和线下人类流动性的动态过程。仿真结果表明，该模型具有获得更接近经验观察的特征的能力。
The dynamics of human mobility characterizes the trajectories that humans follow during their daily activities and is the foundation of processes from epidemic spreading to traffic prediction and information recommendation. In this paper, we investigate a massive data set of human activity, including both online behavior of browsing websites and offline one of visiting towers based mobile terminations. The non-Markovian character observed from both online and offline cases is suggested by the scaling law in the distribution of dwelling time at individual and collective levels, respectively. Furthermore, we argue that the lower entropy and higher predictability in human mobility for both online and offline cases may originate from this non-Markovian character. However, the distributions of individual entropy and predictability show the different degrees of non-Markovian character between online and offline cases. To account for non-Markovian character in human mobility, we apply a protype model with three basic ingredients, namely, preferential return, inertial effect, and exploration to reproduce the dynamic process of online and offline human mobilities. The simulations show that the model has an ability to obtain characters much closer to empirical observations.