Incorporating Wavelet Decomposition Technique to Compress TransGuide Intelligent Transportation System Data

Abstract

With the improvement and application of various data collection techniques, intelligent transportation system (ITS) data have created obstacles for the effective storage, transmission, and retrieval of data. Some traffic management centers (TMCs) collect ITS information and maintain the “recent” data until it can be transferred to users for ultimate long-term storage, management, or both. Others archive data in convenient storage formats (usually compressed text) without action on data usage and analyses. In currently compressed ITS data (e.g., TransGuide zipped data), many redundant and empty spaces can be eliminated and compressed. Sophisticated approaches must be developed to compress ITS data in TMCs effectively. The wavelet-incorporated ITS data compression method not only makes use of conventional data-compression techniques but also incorporates the advanced one-dimensional discrete wavelet-compression approach. Three compression indices are constructed, and one threshold selection algorithm is proposed. The identified threshold can balance both compression ratio and signal distortion. Results of a case study in San Antonio, Texas, indicate that the proposed method and algorithm can achieve a compression ratio that is about 8.12% of what TransGuide currently provides. The entire compression ratio is <1% for a typical day's data. Results of impact analyses indicate that the selection of wavelet forms does not significantly affect the final compression ratio, whereas higher decomposition levels yield smaller decomposition ratios.