Urban Traffic Flow Forecasting Model of Double RBF Neural Network Based on PSO

Traffic flow forecasting is a real-world problem in the adaptive control of urban traffic. This conference paper reports on a study undertaken to investigate the real time adaptive control of urban traffic, focusing on two typical adjacent intersections of city road. The authors present a double RBF NN model with classifying coefficient. The space of high dimensional input samples is divided into two lower dimensional subspaces by the model; the nonlinear degree of the space samples is also reduced greatly. A particle swarm optimization (PSO) algorithm is used to determine the parameters of two RBF NN respectively. The method not only simplifies the structure of RBF NN, but also enhances training speed and mapping accuracy. The authors conclude that the simulations support the effectiveness of their model.

Language

  • English

Media Info

  • Media Type: Print
  • Features: References;

Subject/Index Terms

Filing Info

  • Accession Number: 01054247
  • Record Type: Publication
  • ISBN: 0769525288
  • Files: TRIS
  • Created Date: Jul 23 2007 5:55PM