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Using Multiscale Entropy Method to Analyze the Complexity of Traffic Flow

机译:用多尺度熵分析交通流的复杂性

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Multiscale entropy (MSE) analysis is a method of measuring the complexity of time series across multiple time scales. The complexity of traffic flow time series generated by NS model is analyzed with MSE. The results show that both of the randomization probability and vehicle density affect the complexity of time headway. And when we focus on large time scale, we found that with the same randomization probabilities, the complexity is at a stable and high level in higher vehicle density scenarios. However, with the same randomization probabilities, the complexity will decrease as time scale increases in lower vehicle density scenarios.
机译:多尺度熵(MSE)分析是一种测量跨多个时标的时间序列的复杂性的方法。用MSE分析了NS模型产生的交通流时间序列的复杂性。结果表明,随机概率和车辆密度都影响时间前进的复杂性。而且,当我们关注较大的时间尺度时,我们发现在相同的随机概率下,在较高车辆密度的情况下,复杂度处于稳定且较高的水平。但是,在具有相同的随机概率的情况下,在较低的车辆密度情况下,复杂度将随着时间尺度的增加而降低。

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