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We apply an important tool to extract the traffic cycle signal from traffic data. An alternative to traditional analysis is a non-linear empirical mode decomposition (EMD) method. This method is adaptive and therefore highly efficient at identifying embedded structures, even those with small amplitudes. Using this analysis, the traffic time series are completely decomposed into five non-stationary...
In this paper, we propose a new measure of variability, called the time-dependent Hurst exponent H(t), which fully captures the degree of variability of traffic flow at each time t. In order to assess the accuracy of the technique, we calculate the exponent H(t) for artificial series with assigned Hurst exponents H. We next calculate the exponent H(t) for the traffic time series observed on the Beijing...
In this paper, we applied multifractal modeling techniques to analyze the traffic data collected from the Beijing Yuquanying. The results indicated that multifractal characteristics obviously exist in the traffic system; the degree of fractality of these traffic data tends to increase as the traffic system becomes congested; the Hölder exponent that measures the local rate of fractality may be used...
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