Multidimensional Compression of ITS Data Using Wavelet-Based Compression Techniques

This paper explores the use of wavelet transform-based methods for ITS data compression. A methodology for structuring data and applying wavelet transform-based algorithms is proposed. The methodology provides the option of controlling the compression ratio at the cost of an acceptable distortion, visualizing data at different detail levels. With proper database management, this methodology will also allow faster data access without fully decompressing them. Given a high correlation of traffic data and knowing that the image data are compressed very well due to the inherent correlation of image pixels, the idea here is to restructure the traffic data, such that efficient image compression methods underlying modern image compression standards can be used. Three data structures are discussed: 1-D, 2-D, and 3-D. For a 1-D arrangement, different wavelets and decomposition levels were tested and analyzed for distortion levels in the data after decompression. The 2-D and 3-D data arrangements were compressed using embedded zero-tree wavelet and set partitioning in hierarchical trees algorithms, which are well-proved algorithms for compressing image data. A case study was performed using the traffic flow data from freeways in Las Vegas, Nevada. As could be expected, the compression ratio under the 3-D scheme has shown the best results. The 2-D and 3-D approaches yielded a 91% and 95.2% reduction ratios, respectively.