A multiple-parameter approach for short-term traffic flow prediction

Abstract

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.