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Intelligent transportation systems data compression using wavelet decomposition technique.
  • Published Date:
    2009-12-01
  • Language:
    English
Filetype[PDF-1.20 MB]


Details:
  • Corporate Creators:
  • Publication/ Report Number:
    SWUTC/09/167651-1
  • Resource Type:
  • Geographical Coverage:
  • OCLC Number:
    653241753
  • NTL Classification:
    NTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMS ; NTL-INTELLIGENT TRANSPORTATION SYSTEMS-Information Management ;
  • Format:
  • 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.

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