Intelligent Transportation Systems Data Compression Using Wavelet Decomposition Technique
Fengxiang Qiao, Hao Liu and Lei Yu, Texas Southern University, December 2009, 82 pp. (167651-1)
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.
Keywords: ITS Data Compression, Wavelet Decomposition, Signal Distortion; Compression Ratio, Threshold Selection, Autoregressive Model.
ENTIRE REPORT (Adobe Acrobat File – 1.2 MB)