Characterization of Vehicle Behavior with Information Theory

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.Borl\"ange GPS data set collected in the Swedish city of Borl\"ange; 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 aprox. 0) when there is congestion, and more correlated velocities (k aprox. 3) in the presence of free traffic flow

Study about vehicles velocities using time causal Information Theory quantifiers

New proposals of applications and protocols for vehicular networks appear every day. It is crucial to evaluate, test and validate these proposals on a large scale before deploying them in the real world. Simulation is by far the preferred method by the researchers to evaluate their proposals in a scalable way with low costs. It is known, in vehicular network simulators, that realistic mobility models are the foremost requirement to make reliable evaluations. However, until then, the proposed mobility models are based on stochastic processes, introducing white noise in their formulations, which do not correspond to reality. This work presents the characterization of global, daily and hourly vehicles behavior through their velocities in different real scenarios. To perform this characterization was used the Bandt-Pompe methodology applied to time series from vehicular velocities. Then, the probability histogram was assigned to the following Information Theory quantifiers: Shannon Entropy, Statistical Complexity, and Fisher Information Measure. The application of this methodology, based on time causal Information Theory quantifiers, was possible to identify different regimes and behaviors. The results show that the vehicles velocities present correlated noise with f −k Power Spectrum ranging between 2.5 ≤ k ≤ 3 for highways traffic, 1.5 ≤ k ≤ 2 for mixed traffic, and 0.25 ≤ k ≤ 1 for denser traffic. Additionally, by using the same methodology, we verify that the mobility models used in simulation tools do not produce the same vehicular velocities dynamics observed in real scenarios, the best one presents a correlated noise with f −k Power Spectrum ranging between 0 ≤ k ≤ 2.5, for all traffic analyzed. These results suggest that these models must be improved.Fil: Silva, Mauricio J.. Universidade Federal de Ouro Preto; BrasilFil: Cavalcante, Tamer S. G.. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina. Universidad de Los Andes.; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal de Alagoas; BrasilFil: Rodrigues, Joel J. P. C.. National Institute of Telecommunications; Brasil. Instituto de Telecomunicacoes; Portugal. Universidade de Fortaleza; BrasilFil: Pereira de Oliveira, Ricardo Alexandre. Universidade Federal de Ouro Preto; BrasilFil: Aquino, Andre L. L.. Universidade Federal de Alagoas; Brasi