基于改进型替代数据法的实测交通流的混沌判别

通过对实测交通流进行混沌判别，可以为实际交通流的预测和控制提供理论指导。改进型替代数据法是准确判定时间序列是否具有混沌特性的一种有效方法，该算法不仅能够很好地重构原始时间序列的特性，并且能够避免直接识别混沌方法的局限性。本文以关联维数作为混沌判据，应用改进型替代数据法对微观实测交通流的时间序列进行了混沌判别。实证结果表明，我们实测的交通流中存在混沌，改进型的替代数据法能对其进行准确判别。

The Identification of Chaos in the Real Traffic Flow Based on the Improved Surrogate-data Technique

The identification of chaos in the real traffic flow can provide theoretical guidance for forecasting and control of real traffic flow. The improved surrogate--data technique is one of effective methods to identify the chaos in the time series exactly. It can not only reconstruct characteristics of original data, but also avoid the limitation of the positively identifying chaos. In this paper, correlation dimension was used as the identification evidence, the improved surrogate-data technique was used for the identification of chaos in time series of the real traffic flow. The results indicate that there is chaos in the real traffic flow and the improved surrogate-data technique can identify chaos exactly.