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.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0769525288
• Corporate Authors:

  IEEE Computer Society

  1730 Massachusetts Avenue, NW
  Washington, DC  United States  20036
• Authors:
  • Zhao, Jianyu
  • Jia, Lei
  • Chen, Yuehui
  • Wang, Xudong
• Conference:
  • 2006 6th International Conference on Intelligent Systems Design and Applications
  • Location: Jinan , China
  • Date: 2006-10-16 to 2006-10-18
• Publication Date: 2007

Language

• English

Media Info

• Media Type: Print
• Features: References;

Subject/Index Terms

• TRT Terms: Accuracy; Algorithms; Computer models; Intersections; Mapping; Mathematical models; Neural networks; Simulation; Traffic; Traffic flow; Traffic forecasting; Urban areas
• Uncontrolled Terms: Particle swarm optimization
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

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