A Systematic Spatiotemporal Modeling Framework for Characterizing Traffic Dynamics Using Hierarchical Gaussian Mixture Modeling and Entropy Analysis

Abstract

To accurately characterize traffic flow, a hierarchical Gaussian mixture modeling (GMM) framework is proposed for constructing a proper empirical dynamics model. The traffic flow data are first represented by a linear combination of multiple Gaussian functions for characterizing related timing and geographical parameters and for reducing the quantity of collected traffic data. To further examine dynamically changing behaviors, the phase-transition approach is used for identifying various traffic flow patterns and their dynamic switching behaviors. Furthermore, the information entropy on the traffic data collected at various vehicle detectors can be calculated for characterizing the location significance of these detectors. Detailed experimental analyses showed that five types of traffic flow patterns can be identified based on a six-month traffic data set from Taiwanese highway systems. Each traffic flow pattern indicates a distinct interpretation of a special dynamic traffic behavior.