Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph

Abstract

A new method based on complex network theory is proposed to analyze traffic flow time series in different states. We use the data collected from loop detectors on freeway to establish traffic flow model and classify the flow into three states based on K-means method. We then introduced two widely used methods to convert time series into networks: phase space reconstruction and visibility graph. Furthermore, in phase space reconstruction, we discuss how to determine delay time constant and embedding dimension and how to select optimal critical threshold in terms of cumulative degree distribution. In the visibility graph, we design a method to construct network from multi-variables time series based on logical OR. Finally, we study and compare the statistic features of the networks converted from original traffic time series in three states based on phase space and visibility by using the degree distribution, network structure, correlation of the cluster coefficient to betweenness and degree–degree correlation.