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Intelligent transportation systems data compression using wavelet decomposition technique.

Filetype[PDF-1.20 MB]


  • English
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    Details:

    • Creators:
    • Corporate Creators:
      Texas Southern University
    • Subject/TRT Terms:
    • Publication/ Report Number:
      SWUTC/09/167651-1
    • Resource Type:
      Tech Report
    • Geographical Coverage:
      Texas
    • Corporate Publisher:
      Southwest Region University Transportation Center (U.S.)
    • Abstract:
      Intelligent Transportation Systems (ITS) generates massive amounts of traffic data, which posts

      challenges for data storage, transmission and retrieval. Data compression and reconstruction technique plays an

      important role in ITS data procession. Traditional compression methods have been utilized in Transportation

      Management Centers (TMCs), but the data redundancy and compression efficiency problems remain. In this

      report, the wavelet incorporated ITS data compression method is initiated. The proposed method not only

      makes use of the conventional compression techniques but, in addition, incorporates the one-dimensional

      discrete wavelet compression approach. Since the desired wavelet compression is a lossy algorithm, the

      balancing between the compression ratio and the signal distortion is exceedingly important. During the

      compression process, the determination of the threshold is the key issue that affects both the compression ratio

      and the signal distortion. An algorithm is proposed that can properly select the threshold by balancing the two

      contradicted aspects. Three performance indexes are constructed and the relationships between the three

      indices and the threshold are identified in the algorithm. A MATLAB program with the name Wavelet

      Compression for ITS Data (WCID) has been developed to facilitate the compression tests. A case study on

      TransGuide ITS data was put into play and a final compression ratio of less than one percent on the trade-off

      threshold value shows that the proposed approach is practical. Finally, the threshold selection algorithm can be

      further tuned up utilizing Autoregressive model so that the quality of reconstructed data can be improved with

      a minor overhead of saving only a few parameters.

    • Format:
      PDF
    • Funding:
      10727
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