基于多尺度熵的交通流复杂性分析
Complexity analysis of traffic flow based on multi-scale entropy
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摘要: 交通流演化复杂性的研究有助于深刻理解交通系统的内在演化规律,为交通流的预测和控制提供理论依据。多尺度熵方法在生理时间序列和计算机网络流量的分析中得到了广泛的应用。考虑到交通流中的车辆到达和计算机网络中的分组到达具有类似特性,本文以刹车灯模型的车头时距为分析对象,利用多尺度熵方法来分析交通流演化的复杂性。分析结果表明:1)车头时距的复杂性随着时间尺度的增加而降低,反映了交通流的短时间难预测性;2)当时间尺度较小时,车头时距复杂性在自由流时和同步流时差异不大,但是,随着时间尺度的增加,自由流时车头时距的熵值迅速下降,而同步流时车头时距的熵值下降较慢。这一特性对于识别自由流中是否产生了同步流有非常重要的参考价值。本文的研究可以为揭示交通流演化的复杂性提供新的思路和方法。Abstract: Research on the complexity of traffic flow evolution is helpful to deeply understand the evolution rule of traffic flow system, which can provide the theoretical foundation for forecasting and controlling traffic flow. Multi-scale entropy (MSE) method is widely used in the analyses of time series of physiology and traffic of computer networks. Considering the similarity between the vehicle arrival in traffic flow system and the packet arrival in computer network, the complexity of the time headway in braking light model is analyzed to show the complexity of traffic flow evolution by using the MSE method. The analysis results show that the complexity of the time headway decreases with the increase of the time scale, which reflects that it is difficulty to predict the traffic flow in a shorttime. In addition, the difference in the complexity of the time headway between the phases of the free flow and synchronized flow is small when the time scale is small. However, with the increase of the time scale, the MSE of the time headway decreases rapidly for free flow, but rather slowly for synchronized flow. Such a difference can be used as a very important reference to distinguish the synchronized flow and the free flow. Research results in this paper can provide new ideas and methods for investigating the complexity of traffic flow evolution.
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