This study proposes a short-term traffic flow prediction approach based on multiple traffic flow basic parameters, in which the chaos theory and support vector regression are utilized. First, a high-dimensional variable space can be obtained according to the traffic flow fundamental function. Then, a maximum conditional entropy method is proposed to determine the embedding dimension. And multiple time series are reconstructed based on the phase space reconstruction theory using the time delay obtained by mutual information method and the embedding dimension captured by the maximum conditional entropy method. Finally, the reconstructed phase space is used as the input and the support vector regression optimized by the genetic algorithm is utilized to predict the traffic flow. Numerical experiments are performed and the results show that the approach proposed has strong fitting capability and better prediction accuracy.

中文翻译：

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一种用于短期交通流预测的多参数方法

本研究提出了一种基于多个交通流基本参数的短期交通流预测方法，该方法利用混沌理论和支持向量回归。首先，根据交通流基本函数可以得到一个高维变量空间。然后，提出了一种最大条件熵方法来确定嵌入维数。并基于相空间重构理论，利用互信息法得到的时间延迟和最大条件熵法捕获的嵌入维数，对多个时间序列进行重构。最后，以重建的相空间为输入，利用遗传算法优化的支持向量回归对交通流进行预测。