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Intelligent transportation systems data compression using wavelet decomposition technique.

File Language:
English


Details

  • Creators:
    Qiao, Fengxiang ; Liu, Hao ORCID ICON IMG ; Yu, Lei
  • 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
  • Download URL:
    https://rosap.ntl.bts.gov/view/dot/5751/dot_5751_DS1.pdf
  • File Type:
    Filetype[PDF - 1.20 MB]
  • Collection(s):
    University Transportation Centers ; US Transportation Collection
  • Main Document Checksum:
    urn:sha-512:3b61595d2cbdb60621be1a3cac61a585bbbd37249111f8a46fb9fd70e9fc6b3961b5ebefca8132cc6564daa58e1187498de3d52c6dc021137b3f31ec93d44553
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