ROUGH SETS AND PROBABILITY MASSES FOR DEMPSTER-SHAFER DATA FUSION AT TRAFFIC MANAGEMENT CENTER

This paper studies Dempster-Shafer data fusion technique as affected by probability masses as a result of sensor selection and probability masses distribution. Dempster-Shafer inference is a statistical-based data classification technique for detecting traffic events that affect normal traffic operations, and is used when data sources are contributing discontinuous and incomplete information, and no single data source can produce a predominantly high probability of certainty for identifying the most probable event. To help select appropriate sensors and probability masses, this research proposes and tests the application of Rough Sets data mining technique in support of Dempster-Shafer inference. The basic Rough Set technique is introduced, and a numerical example used to explain its application. Field testing of Rough Sets technique has shown that it is able to process a large amount of traffic information systematically instead of relying on intuition of operating engineers and system managers. This technique allows easy maintenance and update of estimated probability masses, suitable for large scale applications at the traffic management center

  • Supplemental Notes:
    • Publication Date: 2003. Transportation Research Board, Washington DC. Remarks: Paper prepared for presentation at the 82nd annual meeting of the Transportation Research Board, Washington, D.C., January 2003. Format: CD ROM
  • Corporate Authors:

    University of California, Berkeley

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

    California Department of Transportation

    1120 N Street
    Sacramento, CA  United States  95814

    University of California, Berkeley

    Department of Electrical Engineering and Computer Sciences
    Berkeley, CA  United States  94720
  • Authors:
    • Yi, Ping
    • Lu, Huapu
    • Zhang, Yucheng
  • Conference:
  • Date: 2003

Language

  • English

Media Info

  • Pagination: 27 p.

Subject/Index Terms

Filing Info

  • Accession Number: 00962488
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: PATH, STATEDOT
  • Created Date: Sep 2 2003 12:00AM