TRAFFIC CONGESTION PREDICTION BASED ON A CASE BASED MODELING METHOD

In this paper, the authors present a traffic congestion predicting method for local and short term prediction that is based on a case- based modeling method. Current traffic conditions as well as features such as day of the week and hour are used in the method. Real or observed traffic data sets are first stored with their features in a database. The future traffic condition is then retrieved from the database based on the distance of the features. Experimental results indicate that this methodology has a high accuracy rate in predicting traffic congestion.

• Supplemental Notes:
  • Publication Date: November 1999
• Corporate Authors:

  Keio Gijuku Daigaku

  ,    

  Kikai Gijutsu Kenkyujo

  ,    

  NTT Service Integration Laboratories

  ,    

  NTT Cyber Space Laboratories

  ,    

  Saitama Daigaku

  ,    

  Kyoto Daigaku

  ,    
• Authors:
  • Mohri, H
• Publication Date: 1999

Language

• Japanese

Media Info

• Pagination: p. 1993-2001
• Serial:
  • Transactions of the Institute of Electronics, Information and Communication Engineers. B-I. Vol. J82B-I, no. 11
  • Publisher: Keio Gijuku Daigaku

Subject/Index Terms

• TRT Terms: Traffic congestion; Traffic estimation
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 00791720
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: May 5 2000 12:00AM