NEW FILTERING METHODS FOR DATA FUSION AND SHORT TERM FORECASTING FOR URBAN TRAFFIC

This paper presents a new filtering method that can be used in analyzing and forecasting urban traffic patterns and is based on the inverse scattering theory. Preliminary results and a data fusion architecture are also presented.

  • Supplemental Notes:
    • Publication Date: 2001. IEEE Service Center, Piscataway NJ
  • Corporate Authors:

    Siemens Aktiengesellschaft

    Machtfinger Strasse 10, Postfach 100079
    8000 Munich,   Germany 

    Texas A&M University, College Station

    Department of Mechanical Engineering
    College Station, TX  United States  77843-3123

    National Science Foundation

    1800 G Street, NW
    Washington, DC  United States  20550

    University of California, Berkeley

    California PATH Program, Institute of Transportation Studies
    Richmond Field Station, 1357 South 46th Street
    Richmond, CA  United States  94804-4648

    Universidad Nacional Autonoma de Mexico. Instituto de Ingenieria

    ,    

    University of California, Berkeley

    Department of Mechanical Engineering
    Berkeley, CA  United States  94720-1740

    DaimlerChrysler Forschungszentrum in Ulm

    ,    

    Technische Universitat Munchen. Fachgebiet Verkehrsplanung und Verkehrsplanung

    ,    

    University of California, Irvine

    Institute of Transportation Studies
    4000 Anteater Instruction and Research Building
    Irvine, CA  United States  92697

    Nanyang Technological University

    Centre for High Performance Embedded Systems
    Singapore,   Singapore 
  • Authors:
    • Kummerer, Burkhard
    • Kuhne, Reinhart D
  • Conference:
  • Publication Date: 2001

Language

  • English

Media Info

  • Pagination: p. 233-239

Subject/Index Terms

Filing Info

  • Accession Number: 00963569
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: PATH
  • Created Date: Oct 2 2003 12:00AM