An understanding of human dynamics in urban subway traffic from the Maximum Entropy Principle

Yong, Nuo; Ni, Shijun; Shen, Shifei; Ji, Xuewei

doi:10.1016/j.physa.2016.03.071

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…Bursts and heavy tails in human dynamics result in spread processes with a more substantial decay time (diminishing more quickly) than would be predicted by Poisson processes. Researchers continue examining different mechanisms of human activities in two general emphases: (1) how individuals take actions and respond with various queueing strategies, for instance, priority queueing (Barabási, 2005), time-varying priority queueing (Jo, Pan, & Kaski, 2012), and stochastic queueing (Walraevens, Demoor, Maertens, & Bruneel, 2012); and (2) the overall patterns of inter-event distributions without considering individual strategies, such as fractals (Fan, Guo, & Zha, 2012), maximum entropy (Yong, Ni, Shen, & Ji, 2016), and multiple timescales (Zhang, Cui, Song, Zhu, & Yang, 2016). Barabási's (2005) landmark human dynamics research would not have been possible without the massive number of email messages from several thousands of email users, along with the details of senders, recipients, time, and size of each email.…”

Section: I G Ital Informati On Technolog Ie S Tr An S Form H Umanmentioning

confidence: 99%

Human dynamics in space and time: A brief history and a view forward

Yuan

2018

Transactions in GIS

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This article highlights the key intellectual development in human dynamics research, examines the modeling emphases in publications, and argues for research directions in need. Human dynamics research is discussed in two broad directions: spacing time and timing space, to model human activities and interactions. Time is essential to human dynamics research. Space, while often being overlooked, in complement with time is critical to understanding human dynamics because knowing where activities take place is essential to knowing how and why people act and interact. Some interactions allow remote or asynchronized participations, and others require movement to collocate individuals for participating in synchronized activities. A spacing time approach examines the temporal gaps between interactions. A timing space approach investigates the spatial pulses between interactions. Primary research in the spacing time of human dynamics established queueing theories to explain the bursts and heavy‐tailed distribution of human interactions. Although research on the timing space of human dynamics enjoys growing popularity with data from geo‐tagged social media and location‐aware social internet of things (SIoT), its publications remain mostly exploratory. This article suggests a hierarchical framework to systematically study human dynamics and relate findings to build the body of knowledge about human dynamics.

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Section: I G Ital Informati On Technolog Ie S Tr An S Form H Umanmentioning

confidence: 99%

Human dynamics in space and time: A brief history and a view forward

Yuan

2018

Transactions in GIS

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“…[3]). Under this general understanding, it is not surprising that, after Jaynes, MaxEnt has been presented as a general method for inference [4][5][6][7] and being applied in a large range of subjects such as, but not limited to, economics [8,9], ecology [10,11], cell biology [12,13], opinion dynamics [14,15] and geography [16,17]. This modern perspective of MaxEnt is seen as a method for updating probability distributions when new information about the system becomes available.…”

Section: Introductionmentioning

confidence: 99%

Entropic dynamics of networks

Costa¹,

Pessoa²

2021

NEJCS

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Here we present the entropic dynamics formalism for networks. That is, a framework for the dynamics of graphs meant to represent a network derived from the principle of maximum entropy and the rate of transition is obtained taking into account the natural information geometry of probability distributions. We apply this framework to the Gibbs distribution of random graphs obtained with constraints on the node connectivity. The information geometry for this graph ensemble is calculated and the dynamical process is obtained as a diffusion equation. We compare the steady state of this dynamics to degree distributions found on real-world networks.

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“…It consists of selecting probability distributions by maximizing a functional-namely entropy-usually under a set of expected values constraints, arriving at what is known as Gibbs distributions. Since Shore and Johnson [6] MaxEnt has been understood as a general method for inference-see also [7][8][9]-hence it is not surprising that (i) Gibbs distributions are what is known in statistical theory as exponential family-the only distributions for which sufficient statistics exist (see e.g., [10]), (ii) MaxEnt encompasses the methods of Bayesian statistics [11], and (iii) MaxEnt has found successful applications in several fields of science (e.g., [12][13][14][15][16][17][18][19][20][21][22]).…”

Section: Introductionmentioning

confidence: 99%

Legendre Transformation and Information Geometry for the Maximum Entropy Theory of Ecology

Pessoa

2021

The 40th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

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Here I investigate some mathematical aspects of the maximum entropy theory of ecology (METE). In particular I address the geometrical structure of METE endowed by information geometry. As novel results, the macrostate entropy is calculated analytically by the Legendre transformation of the log-normalizer in METE. This result allows for the calculation of the metric terms in the information geometry arising from METE and, by consequence, the covariance matrix between METE variables.

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An understanding of human dynamics in urban subway traffic from the Maximum Entropy Principle

Cited by 12 publications

References 17 publications

Human dynamics in space and time: A brief history and a view forward

Human dynamics in space and time: A brief history and a view forward

Entropic dynamics of networks

Legendre Transformation and Information Geometry for the Maximum Entropy Theory of Ecology

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