Contact duration: Intricacies of human mobility

Q1 Social Sciences
Leonardo Tonetto , Malintha Adikari , Nitinder Mohan , Aaron Yi Ding , Jörg Ott
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引用次数: 0

Abstract

Human mobility shapes our daily lives, our urban environment and even the trajectory of a global pandemic. While various aspects of human mobility and inter-personal contact duration have already been studied separately, little is known about how these two key aspects of our daily lives are fundamentally connected. Better understanding of such interconnected human behaviors is crucial for studying infectious diseases, as well as opportunistic content forwarding. To address these deficiencies, we conducted a study on a mobile social network of human mobility and contact duration, using data from 71 persons based on GPS and Bluetooth logs for 2 months in 2018. We augment these data with location APIs, enabling a finer granular characterization of the users’ mobility in addition to contact patterns. We model stops durations to reveal how time-unbounded-stops (e.g., bars or restaurants) follow a log-normal distribution while time-bounded-stops (e.g., offices, hotels) follow a power-law distribution. Furthermore, our analysis reveals contact duration adheres to a log-normal distribution, which we use to model the duration of contacts as a function of the duration of stays. We further extend our understanding of contact duration during trips by modeling these times as a Weibull distribution whose parameters are a function of trip length. These results could better inform models for information or epidemic spreading, helping guide the future design of network protocols as well as policy decisions.

接触时间:人类流动性的复杂性
人类的流动性影响着我们的日常生活、城市环境,甚至影响着全球流行病的发展轨迹。虽然人们已经分别研究了人类流动性和人际接触持续时间的各个方面,但我们对日常生活中这两个关键方面是如何从根本上联系在一起的知之甚少。更好地了解这种相互关联的人类行为对于研究传染病以及机会主义内容转发至关重要。为了解决这些不足,我们在移动社交网络上进行了一项关于人类流动性和接触时间的研究,使用了71人的数据,基于2018年的GPS和蓝牙日志,为期2个月。我们使用位置api增强这些数据,除了联系模式之外,还可以对用户的移动性进行更细粒度的表征。我们对停车时间进行建模,以揭示无时间限制的停车(例如,酒吧或餐馆)是如何遵循对数正态分布的,而有时间限制的停车(例如,办公室、酒店)是如何遵循幂律分布的。此外,我们的分析显示,接触持续时间遵循对数正态分布,我们用它来模拟接触持续时间作为停留时间的函数。通过将这些时间建模为威布尔分布,其参数是旅行长度的函数,我们进一步扩展了对旅行期间接触持续时间的理解。这些结果可以更好地为信息或流行病传播模型提供信息,帮助指导未来网络协议的设计以及政策决策。
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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来源期刊
Online Social Networks and Media
Online Social Networks and Media Social Sciences-Communication
CiteScore
10.60
自引率
0.00%
发文量
32
审稿时长
44 days
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