Optimizing Aggregation Level for Intelligent Transportation System Data Based on Wavelet Decomposition

Appropriate aggregation levels and sampling frames of real-time data in intelligent transportation systems (ITSs) are indispensable to transportation planners and engineers. Conventional techniques for the retrieval of aggregation levels are normally based on statistical comparison of the original and the aggregated data sets. However, it is not guaranteed that errors and noise will not be transmitted to the aggregated data sets and that the desired information will be reserved. Wavelet decomposition is a new technique that can be applied to the determination of aggregation level. An optimization process that can provide the optimized aggregation level of ITS data for different applications was developed. To illustrate the proposed technique, ITS data archived by the TransGuide Center in San Antonio, Texas, were used for a case study. Aggregation levels for different days of a week and different time periods over the whole year of 2001 were obtained through the proposed approach.