FREEWAY TRAFFIC FLOW MODELING WITH RBF NEURAL NETWORK

In this paper, the authors present a modified speediness radial basis function (RBF) neural network for modeling the kinetic flow of freeway traffic. The method uses a single path clustering algorithm to determine the parameters of the RBF neural network. When applied to the modeling of freeway traffic flow, the ability of the neural network to learn quickly is shown to be valuable in realizing online modeling and traffic flow control.

• Supplemental Notes:
  • Publication Date: May 2000
• Corporate Authors:

  Shang hai chiao t'ung ta hsueh

  ,    
• Authors:
  • Ou, Hai-Tao
  • Zhang, Wen-Yuan
  • Yang, Yu-Pu
  • Xu, Xiao-Ming
• Publication Date: 2000

Language

• Chinese

Media Info

• Pagination: p. 665-668
• Serial:
  • Shang hai chiao t'ung ta hsueh hsueh pao = Journal of Shanghai Jiaotong University. Vol. 34, no. 5
  • Publisher: Shang hai chiao t'ung ta hsueh

Subject/Index Terms

• TRT Terms: Freeways; Neural networks; Traffic flow
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 00801300
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Nov 7 2000 12:00AM