A Complex Empirical Mode Decomposition for Multivariant Traffic Time Series

Abstract

Data-driven modeling methods have been widely used in many applications or studies of traffic systems with complexity and chaos. The empirical mode decomposition (EMD) family provides a lightweight analytical method for non-stationary and non-linear data. However, a large amount of traffic data in practice are usually multidimensional, so the EMD family cannot be used directly for those data. In this paper, a method to calculate the extremum point and the envelope-like function (series) from the complex function (series) is proposed so that the EMD family can be applied to two-variate traffic time-series data. Compared to the existing multivariate EMD, the proposed method has advantages in computational burden, flexibility and adaptivity. Two-dimensional trajectory data were used to test the method and its oscillatory characteristics were extracted. The decomposed feature can be used for data-driven traffic analysis and modeling. The proposed method also extends the utilization of EMD to multivariate traffic data for applications such as traffic data denoising, pattern recognition, traffic flow dynamic evaluation, traffic prediction, etc.