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Modeling traffic flow correlation using DFA and DCCA

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Abstract

The focus of the present paper is on the power-law auto-correlations and crosscorrelations in traffic time series. Detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) are used to study the traffic flow fluctuations. We find that the original traffic fluctuation time series may exhibit power-law auto-correlations; however, the sign-separated traffic fluctuation signals, both the positive fluctuation signals and the negative fluctuation signals, exhibit anti-correlated behavior. Further, we show that two original traffic speed fluctuation time series derived from adjacent sections exhibit much stronger power-law cross-correlations than the two time series derived from adjacent lanes. Finally, we demonstrate that for two sign-separated traffic fluctuation signals, there exist long-range cross-correlations between the positive fluctuation signals and the negative fluctuation signals, derived from two adjacent lanes, respectively. But, for two same-sign traffic fluctuation signals derived from two adjacent lanes, there is power-law cross-anti-correlation in the variables.

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Authors and Affiliations

  1. School of Science, Beijing Jiaotong University, Beijing, 100044, China

    Na Xu & Pengjian Shang

  2. Department of Civil Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Santi Kamae

Authors
  1. Na Xu
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  2. Pengjian Shang
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  3. Santi Kamae
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Correspondence to Pengjian Shang.

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Xu, N., Shang, P. & Kamae, S. Modeling traffic flow correlation using DFA and DCCA. Nonlinear Dyn 61, 207–216 (2010). https://doi.org/10.1007/s11071-009-9642-5

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  • DOI: https://doi.org/10.1007/s11071-009-9642-5

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