Characterization of vehicle behavior with information theory

Aquino, André L. L.; Cavalcante, Tamer; Almeida, Eliana Silva de; Frery, Alejandro C.; Rosso, Osvaldo A.

doi:10.1140/epjb/e2015-60384-x

Cited by 23 publications

(19 citation statements)

References 40 publications

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“…Since its introduction almost fifteen years ago by Bandt and Pompe (BP) in their foundational paper [1], it has been successfully applied in a wide range of scientific areas and for a vast number of purposes. Without being exhaustive, applications in heterogeneous fields, such as biomedical signal processing and analysis [2][3][4][5][6][7][8][9][10], optical chaos [11][12][13][14][15], hydrology [16][17][18], geophysics [19][20][21], econophysics [22][23][24][25], engineering [26][27][28][29], and biometrics [30] can be mentioned. The PE is just the celebrated Shannon entropic measure evaluated using the ordinal scheme introduced by BP to extract the probability distribution associated with an input signal.…”

Section: Introductionmentioning

confidence: 99%

Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

et al. 2017

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Section: Introductionmentioning

confidence: 99%

Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

et al. 2017

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“…Zhao et al [63] proposed the DTSTOS, also aiming to reduce the traffic delays in a road intersection. Aquino et al [125], [126] propose a characterization of vehicles velocities to identify traffic behaviors using information theory.…”

Section: Traffic Monitoring and Managementmentioning

confidence: 99%

Vehicular Data Space: The Data Point of View

Rettore

Maia

Villas

et al. 2019

IEEE Commun. Surv. Tutorials

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Over the years, governments and automakers launched initiatives to improve road traffic efficiency, safety, and people mobility. They have been working on various aspects of intelligent transportation systems (ITSs), which aim to improve decision-making, availability of information, and communication technologies to provide applications and services to boost the transportation systems. The development of new applications and services for ITS depends on the availability of different data sources, what it is not the current case. Many studies focus on the communication issues of applications and their associated challenges. To reveal the recent vehicular data use, we examined the most remarkable studies of the last few years, which describe services and applications for ITS, however with a focus on the data employed by them. We introduce the concept of vehicular data space (VDS), which is then used to describe the vehicular scenario from the perspective of data. Moreover, we outline a taxonomy, according to the different data sources. We also categorize the applications, highlighting the data each one used in their approach. Finally, we present some challenges and open issues related to the process for data creation, data preparation, data processing, and data use. In a nutshell, this paper constitutes one of the first holistic surveys on services and applications for ITSs, focusing on the data used by them, as well as their future challenges. Index Terms-Vehicular data space (VDS), vehicular sensor data, connected vehicles, intelligent transportation system (ITS), heterogeneous data, vehicular ad-hoc network (VANET). I. INTRODUCTIONM ORE and more cities will have to deal with significant issues related to transportation and traffic because people and goods are in constant need for quicker and safer mobility modes. The number of fatalities and injuries on the road has achieved an alarming scenario. Globally, 1.3 million people die every year, and up to 50 million suffer severe injuries. These facts have a direct impact on the economics of nations, leading to costs in the order of about 2% to 5% of the Gross Domestic Product (GDP) in many countries [1]. It is also reported that traffic congestion results in critical economic and environmental costs. In 2011, 498 U.S. urban areas were

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“…In both causal information planes ( , see Fig 1 and , see Fig 2 ), stochastic data are clearly localized at different planar positions than deterministic chaotic ones. These two causal information planes have been profitably used to visualize and characterize different dynamical regimes when the system parameters vary [ 34 , 35 , 42 – 51 ]; to study temporal dynamic evolution [ 52 – 54 ]; to identify periodicities in natural time series [ 55 ]; to identify deterministic dynamics contaminated with noise [ 56 , 57 ]; to estimate intrinsic time scales and delayed systems [ 58 – 60 ]; for the characterization of pseudo-random number generators [ 61 , 62 ]; to quantify the complexity of two-dimensional patterns [ 63 ]; and for ecological [ 45 ], biomedical and econophysics applications (see [ 39 ] and references therein).…”

Section: Quantifiers From Information Theorymentioning

confidence: 99%

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

et al. 2016

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Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

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Characterization of vehicle behavior with information theory

Cited by 23 publications

References 40 publications

Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

Vehicular Data Space: The Data Point of View

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

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