Human dynamics scaling characteristics for aerial inbound logistics operation

Wang, Qing; Guo, Jun

doi:10.1016/j.physa.2010.01.009

Cited by 15 publications

(16 citation statements)

References 15 publications

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“…A lot of researchers have found that such as web browsing [1], [5], blog comments [6], market trading [7], human communication behavior [8]- [10], logistics transportation [11] and other human behavior all obey the power law relationship .Therefore, many researchers studied the method to determine whether a distribution is a power law distribution and an estimate of the power exponent.…”

Section: Methodsmentioning

confidence: 99%

An Empirical Research of Human Behavior Dynamics in Network Course Learning

Cheng¹,

Yan²

2017

IJIET

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Abstract-Learning is a kind of important human behavior to acquire knowledge. This paper discussed temporal characteristics of network course learning on behavior dynamics. Firstly, the students behavior data of the network course learning for 8-weeks are collected from online learning platform. Then, the work used the Maximum Likelihood Estimation(MLE) method for estimating the power exponent of learning behavior interval time distribution, and introduced Kolmogorov-Smirnov(KS) method to test power-law hypothesis .The empirical research results show that: both in the group and individual level, learning behavior time interval obey characteristics of power-law distribution. Underlying these, This thesis combined with the learning psychology, environment and other factors explained the statistical characteristics, and provided some suggestions for teaching management.Index Terms-Empirical research, behavior dynamics, behavior interval time distribution, power-law.

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Section: Methodsmentioning

confidence: 99%

An Empirical Research of Human Behavior Dynamics in Network Course Learning

Cheng¹,

Yan²

2017

IJIET

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“…And they are soon absorbed into physical sciences like system science and social sciences such as sociology, management, and finance as well as economics and make fruitful progress including supply chain management [3], logistics operation [4], commercial networks [5], international trade networks [6], and stocks networks [7]. In the fields of social science, Guo [3] researched on degree distributions of nodes on supply chain networks.…”

Section: Introductionmentioning

confidence: 99%

“…In the fields of social science, Guo [3] researched on degree distributions of nodes on supply chain networks. Wang and Guo [4] studied the scaling characteristics for aerial inbound logistics operation and proposed a third human dynamics mechanism beyond two classical models proposed by Vazquez et al [8]. Souma et al [5] studied topological characteristics of banks and companies networks and further analyzed the influences on organizations and splits of communities caused by merging behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Visibility Graph Approach to CNY Exchange Rate Networks and Characteristic Analysis

Yao

Lin

2017

Discrete Dynamics in Nature and Society

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We find that exchange rate networks are significantly similar from the perspective of topological structure, though with relatively great differences in fluctuation characteristics from perspective of exchange rate time series. First, we transform central parity rate time series of US dollar, Euro, Yen, and Sterling against CNY into exchange rate networks with visibility graph algorithm and find consistent topological characteristics in four exchange rate networks, with their average path lengths 5 and average clustering coefficients 0.7. Further, we reveal that all four transformed exchange rate networks show hierarchical structure and small-world and scale-free properties, with their hierarchy indexes 0.5 and power exponents 1.5. Both of the US dollar network and Sterling network exhibit assortative mixing features, while the Euro network and Yen network exhibit disassortative mixing features. Finally, we research community structure of exchange rate networks and uncover the fact that the communities are actually composed by large amounts of continuous time point fractions and small amounts of discrete time point fractions. In this way, we can observe that the spread of time series values corresponding to nodes inside communities is significantly lower than the spread of those values corresponding to nodes of the whole networks.

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“…1. This also relates to the heavy-tailed temporal distributions observed in human and animal behavior and locomotion [1,2,3,4,5,6,14,15,16,17,18]. A number of approaches have been used to characterize the features of the empirical time statistics of written communication [6,19,20,21,22,23,11,24,25,26,27], with debated indications of scaling behavior for the waiting times, and for their possible modeling through priority queueing.…”

Section: Introductionmentioning

confidence: 99%

New activity pattern in human interactive dynamics

et al. 2015

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We investigate the response function of human agents as demonstrated by written correspondence, uncovering a new universal pattern for how the reactive dynamics of individuals is distributed across the set of each agent's contacts. In long-term empirical data on email, we find that the set of response times considered separately for the messages to each different correspondent of a given writer, generate a family of heavy-tailed distributions, which have largely the same features for all agents, and whose characteristic times grow exponentially with the rank of each correspondent. We furthermore show that this universal behavioral pattern emerges robustly by considering weighted moving averages of the priorityconditioned response-time probabilities generated by a basic prioritization model. Our findings clarify how the range of priorities in the inputs from one's environment underpin and shape the dynamics of agents embedded in a net of reactive relations. These newly revealed activity patterns might be present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.

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Human dynamics scaling characteristics for aerial inbound logistics operation

Cited by 15 publications

References 15 publications

An Empirical Research of Human Behavior Dynamics in Network Course Learning

An Empirical Research of Human Behavior Dynamics in Network Course Learning

A Visibility Graph Approach to CNY Exchange Rate Networks and Characteristic Analysis

New activity pattern in human interactive dynamics

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