This work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: (i) Mobile Century data set collected on Highway I-880, near Union City, California; (ii) Borlange GPS data set collected in the Swedish city of Borlange; and (iii) Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f − k Power Spectrum with k ≥ 0. With this we identify traffic behaviors as, for instance, random velocities (k ≃ 0) when there is congestion, and more correlated velocities (k ≃ 3) in the presence of free traffic flow. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015
@cite_18 analyzed the traffic congestion index (TCI) of Beijing which is defined by the Beijing Municipal Commission. The TCI is computed using information gathered by specialized devices (in vehicles, traffic lights etc.), all around the city. This index indicates the traffic state as congested or not. Using the Bandt-Pompe methodology, the authors showed that the Complexity-Entropy plane is the best way to classify the levels of traffic congestion using the TCI as reference, when compared to other methods: @math analysis () @cite_1 and DFA method () @cite_29 . The former is a statistical method used to evaluate the nature and the magnitude of the variability of the data over time, while the latter determines the behavior of data scale considering some trends, regardless their origin or shape. Differently from our approach, Ref. @cite_18 does not attempt at characterizing the underlying dynamics of the process.
@cite_18 @cite_29 @cite_1
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In this paper, the complexity-entropy causality plane approach is applied to analyze traffic data. The R S analysis and detrended fluctuation analysis (DFA) methods are also used to compare with this approach. Moreover, based on the concept of entropy, we propose to use permutation to calculate the probability distribution of the time series when applying the representation plane. The empirical results indicate that traffic dynamics exhibit different levels of traffic congestion and demonstrate that this statistical method can give a more refined classification of traffic states than the R S analysis and DFA. The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium‐like state [Physiol. Rev. 9, 399–431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343–1346 (1993); Fractals in Biology and Medicine (Birkhauser‐Verlag, Basel, 1994), pp. 55–65] reveal that under normal conditions, beat‐to‐beat fluctuations in heart rate display the kind of long‐range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381–384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long‐range correlation behavior. We describe a new method—detrended fluctuation analysis (DFA)—for quantifying this correlation property in non‐stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long‐range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.