Improved aggregation levels of ITS data via wavelet decomposition and fast Fourier transform algorithm

To make the real-time ITS data more useful to transportation planners, ITS data should be reasonably processed to acquire appropriate aggregation levels and sampling frames. Conventional aggregation techniques concentrated on the statistical comparison between the original and aggregated data sets, so they cannot eliminate the undesired information (e.g., error or noise). This research improves the wavelet technique which analyzes real-time data within frequency domain. ITS data were decomposed by wavelet transformation and then transformed by FFT. Through unifying the parameter of FFT and creating grading system, the optimal aggregation level can be determined. As a result of this research, the computer software compiled in MATLAB was developed, which can provide the optimal aggregation levels and aggregated data series, which was applied to the archived 2-minute traffic data in Beijing. Optimal aggregation levels for different days of a week and different time periods of a day were obtained by the proposed approach.