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We investigate the traffic flow volume data on the time dependent activity of Beijing's urban road network. The couplings between the average flux and the fluctuations on individual links are shown to follow certain scaling laws and yield a wide variety of scaling exponents between 1/2 and 1. To quantitatively explain this interesting phenomenon, a non-stationary Poisson arriving model is proposed. The scaling property is interpreted as the result of the time- variation of the arriving rate of flux over the network, which nicely explicates the effect of aggregation windows, and provides a concise model for the dependence of scaling exponent on the external/internal force ratio. 相似文献
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Fluctuations and pseudo long range dependence in network flows: A non-stationary Poisson process model
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In the study of complex networks (systems), the scaling phenomenon
of flow fluctuations refers to a certain power-law between the mean
flux (activity) < Fi> of the i-th node and its
variance σi as Fi ∝ <Fiα. Such scaling laws are found to be prevalent both in
natural and man-made network systems, but the understanding of their
origins still remains limited. This paper proposes a non-stationary
Poisson process model to give an analytical explanation of the
non-universal scaling phenomenon: the exponent α varies
between 1/2 and 1 depending on the size of sampling time window
and the relative strength of the external/internal driven forces of
the systems. The crossover behaviour and the relation of fluctuation
scaling with pseudo long range dependence are also accounted for by
the model. Numerical experiments show that the proposed model can
recover the multi-scaling phenomenon. 相似文献
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