ESTIMATE TRAFFIC CONTROL PATTERNS USING A HYBRID NEURAL NETWORK

In this paper, the authors focus on traffic demand prediction and traffic data modeling. They propose an approach of combining advanced neural networks and conventional error correction for improved intelligent transportation systems (ITS) applications.

• Supplemental Notes:
  • Publication Date: 1999 IEEE Service Center, Piscataway NJ
• Corporate Authors:

  Texas Transportation Institute

  Texas A&M University System, 3135 TAMU
  College Station, TX  United States  77843-3135
• Authors:
  • Chang, Edmond Chin-Ping
• Conference:
  • IEEE International Joint Conference on Neural Networks (1999 : Washington, D. C.). Proceedings. Vol. 4
• Publication Date: 1999

Language

• English

Media Info

• Pagination: p. 2763-2767

Subject/Index Terms

• TRT Terms: Freeway management systems; Neural networks; Traffic estimation
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 00799962
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Oct 12 2000 12:00AM