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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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