In the analysis of vehicular traffic flow, a myriad of techniques have been implemented. In this study, superstatistics is used in modeling the traffic flow on a highway segment. Traffic variables such as vehicular speeds, volume, and headway were collected for three days. For the superstatistical approach, at least two distinct time scales must exist, so that a superposition of nonequilibrium systems assumption could hold. When the slow dynamics of the vehicle speeds exhibit a Gaussian distribution in between the fluctuations of the system at large, one speaks of a relaxation to a local equilibrium. These Gaussian distributions are found with corresponding standard deviations 1/β. This translates into a series of fluctuating beta values, hence the statistics of statistics, superstatistics. The traffic flow model has generated an inverse temperature parameter (beta) distribution as well as the speed distribution. This beta distribution has shown that the fluctuations in beta are distributed with respect to a chi-square distribution. It must be mentioned that two distinct Tsallis q values are specified: one is time-dependent and the other is independent. A ramification of these q values is that the highway segment and the traffic flow generate separate characteristics. This highway segment in question is not only nonadditive in nature, but a nonequilibrium driven system, with frequent relaxations to a Gaussian

Precise wind energy potential assessment is vital for wind energy generation and planning and development of new wind power plants. This work proposes and evaluates a novel two-stage method for location-specific wind energy potential assessment. It combines accurate statistical modelling of annual wind direction distribution in a given location with supervised machine learning of efficient estimators that can approximate energy efficiency coefficients from the parameters of optimized statistical wind direction models. The statistical models are optimized using differential evolution and energy efficiency is approximated by evolutionary fuzzy rules.

h i g h l i g h t s • Platoon-based formations are discussed through nonextensive thermostatistics. • The limit values of Tsallis q index for vehicular platoon formation are proposed. • Superstatistics approach is utilized to obtain Tsallis q index.

The nearest and close neighbor models are well within the conventional additive entropy framework. In this article, both the long-range vehicular interactions and safe driving behavior in traffic are handled in the nonadditive entropy domain. It is also inferred that the Tsallis entropy region would correspond to mandatory lane changing behavior, whereas additive and either the extensive or nonextensive entropy region would match discretionary lane changing behavior. This article states that driver behaviors would be in the nonadditive entropy domain to provide a safe traffic stream and hence with vehicle accident prevention in mind.

A variety of approaches, within literature, has been conducted to interpret vehicular speed characteristics. This study turns the attention to the entropy-based approaches, and thus focuses on the maximum entropy method of statistical mechanics and the Kullback–Leibler (KL) divergence approach to examining the vehicular speeds. The vehicle speeds at the selected highway are analyzed in order to find out the disparities among them. However, it is turned out that the speed dynamics could not be distinguished over the speed distributions; hence the maximization of Shannon entropy seems insufficient to compare the speed distributions of each data set. For this reason, the KL divergence approach was performed. This approach displays the comparison, among the speed distributions, based on two prior distribution models, i.e., uniform and Gauss. The examination of the trends of KL divergences obtained from both distributions was made. It was concluded that the KL divergence values for the highway speed data sets ranged between about 0.53 and 0.70 for the uniform case, while for the Gaussian case the obtained values are between 0.16 and 0.33. The KL divergence trends for the real speeds were obtained analogous for both cases, but they differed significantly when the synthetic data sets were employed. As a result, the KL divergence approach proves suitable as an appropriate indicator to compare the speed distributions.