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Sasu Tarkoma - One of the best experts on this subject based on the ideXlab platform.

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    Scientific Reports, 2015
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Explaining the Power-Law Distribution of human mobility through transportation modality decomposition

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    arXiv: Physics and Society, 2014
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with Power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a Power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a Power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.

Kai Zhao - One of the best experts on this subject based on the ideXlab platform.

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    Scientific Reports, 2015
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Explaining the Power-Law Distribution of human mobility through transportation modality decomposition

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    arXiv: Physics and Society, 2014
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with Power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a Power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a Power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.

Yuying Wu - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Prediction of social tag frequency's Power Law Distribution with RGF model
    2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012
    Co-Authors: Zhenyu Wu, Yuying Wu
    Abstract:

    The Power Law Distribution of social tag frequency is one of the most important statistic characteristics in social tagging system. Researchers have proposed several models to predict this Distribution, but these models depend on specific system. What's more, data fitting methods were failed to describe the raw data precisely compared with a mathematical model. Random Group Formation (RGF) model is a newly proposed mathematical model which could predict the Power Law Distribution precisely. This model does not depend on the specific system. It could predict the Power Law Distribution precisely using only three values: total number of elements, groups and the number of elements in the largest group. In this paper, RGF model is employed to predict the Power Law Distribution of tag frequency. The experiments show that the Power Law Distribution of tag frequency can be predicted precisely by three values, and the Distribution is independent on specific system. The analysis of the tag dataset using RGF model shows that the exponent of Power Law decreases with the increasing of the dataset's size.

  • Prediction of social tag frequency's Power Law Distribution with RGF model
    2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012
    Co-Authors: Zhenyu Wu, Yuying Wu
    Abstract:

    The Power Law Distribution of social tag frequency is one of the most important statistic characteristics in social tagging system. Researchers have proposed several models to predict this Distribution, but these models depend on specific system. What's more, data fitting methods were failed to describe the raw data precisely compared with a mathematical model. Random Group Formation (RGF) model is a newly proposed mathematical model which could predict the Power Law Distribution precisely. This model does not depend on the specific system. It could predict the Power Law Distribution precisely using only three values: total number of elements, groups and the number of elements in the largest group. In this paper, RGF model is employed to predict the Power Law Distribution of tag frequency. The experiments show that the Power Law Distribution of tag frequency can be predicted precisely by three values, and the Distribution is independent on specific system. The analysis of the tag dataset using RGF model shows that the exponent of Power Law decreases with the increasing of the dataset's size.

Mirco Musolesi - One of the best experts on this subject based on the ideXlab platform.

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    Scientific Reports, 2015
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Explaining the Power-Law Distribution of human mobility through transportation modality decomposition

  • explaining the Power Law Distribution of human mobility through transportation modality decomposition
    arXiv: Physics and Society, 2014
    Co-Authors: Kai Zhao, Mirco Musolesi, Sasu Tarkoma
    Abstract:

    Human mobility has been empirically observed to exhibit Levy flight characteristics and behaviour with Power-Law distributed jump size. The fundamental mechanisms behind this behaviour has not yet been fully explained. In this paper, we analyze urban human mobility and we propose to explain the Levy walk behaviour observed in human mobility patterns by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bicycle, Train/Subway or Car/Taxi/Bus. Our analysis is based on two real-life GPS datasets containing approximately 10 and 20 million GPS samples with transportation mode information. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal Distribution rather than a Power-Law Distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight Distributions associated with each modality is a Power-Law Distribution, providing an explanation to the emergence of Levy Walk patterns that characterize human mobility patterns.

Satish Karra - One of the best experts on this subject based on the ideXlab platform.

  • fracture size and transmissivity correlations implications for transport simulations in sparse three dimensional discrete fracture networks following a truncated Power Law Distribution of fracture size
    Water Resources Research, 2016
    Co-Authors: Jeffrey D Hyman, Garrett Aldrich, Hari S Viswanathan, Nataliia Makedonska, Satish Karra
    Abstract:

    We characterize how different fracture size-transmissivity relationships influence flow and transport simulations through sparse three-dimensional discrete fracture networks. Although it is generally accepted that there is a positive correlation between a fracture's size and its transmissivity/aperture, the functional form of that relationship remains a matter of debate. Relationships that assume perfect correlation, semi-correlation, and non-correlation between the two have been proposed. To study the impact that adopting one of these relationships has on transport properties, we generate multiple sparse fracture networks composed of circular fractures whose radii follow a truncated Power Law Distribution. The Distribution of transmissivities are selected so that the mean transmissivity of the fracture networks are the same and the Distributions of aperture and transmissivity in models that include a stochastic term are also the same. We observe that adopting a correlation between a fracture size and its transmissivity leads to earlier breakthrough times and higher effective permeability when compared to networks where no correlation is used. While fracture network geometry plays the principal role in determining where transport occurs within the network, the relationship between size and transmissivity controls the flow speed. These observations indicate DFN modelers should be aware that breakthrough times and effective permeabilities can be strongly influenced by such a relationship in addition to fracture and network statistics. This article is protected by copyright. All rights reserved.