Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

Adediran, Adewumi; Kagamba, Jimmy; Alochukwu, Alex

doi:10.1155/2016/5656734

Cited by 24 publications

(15 citation statements)

References 21 publications

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“…The KC complexity and its derivatives are essential to use because randomness is a crucial characteristic that has significant implications for solar irradiation modelling and prediction. Another complementary measure important for solar irradiation analysis is the Lyapunov time as the inverse of the largest Lyapunov exponent because it can detect the presence of deterministic chaos in complex systems (also in radiative processes) and quantify the predictability of potential future outcomes [18]. This time is used for predicting the time series.…”

Section: Predictability and Kolmogorov Timementioning

confidence: 99%

Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)

Mihailoviĉ

Bessafi²,

Marković

et al. 2018

Entropy

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Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability.

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Section: Predictability and Kolmogorov Timementioning

confidence: 99%

Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)

Mihailoviĉ

Bessafi²,

Marković

et al. 2018

Entropy

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show abstract

“…In addition, the upcoming random bit should be unpredictable. [58][59][60][61] As TRNGs have been generated for cryptographic purpose, the resistance of TRNG against possible threats is too important. From this point of view, the IC application for TRNG has top-priority significance.…”

Section: True Random Number Generatormentioning

confidence: 99%

“…However, the bit streams of TRNGs should pass the statistical randomness tests to meet the needs of the security of one‐time pad, key generation, and other cryptographic applications that necessitate high security level. In addition, the upcoming random bit should be unpredictable . As TRNGs have been generated for cryptographic purpose, the resistance of TRNG against possible threats is too important.…”

Section: Basic Conceptsmentioning

confidence: 99%

A novel high speed Artificial Neural Network–based chaotic True Random Number Generator on Field Programmable Gate Array

Alçın

Koyuncu

Tuna

et al. 2018

Circuit Theory & Apps

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Summary It is well observed that cryptographic applications have great challenges in guaranteeing high security as well as high throughput. Artificial neural network (ANN)–based chaotic true random number generator (TRNG) structure has not been unprecedented in current literature. This paper provides a novel type of high‐speed TRNG based on chaos and ANN implemented in a Xilinx field‐programmable gate array (FPGA) chip. The paper consists of two main parts. In the first part, chaos analyses of Pehlivan‐Uyaroglu_2010 chaotic system (PUCS) have been accomplished to prove that PUCS operates in chaotic regime. So PUCS can be an efficient alternative to the entropy source for classical TRNGs. In the second part, the hardware design of the proposed TRNG has been created using VHDL in Xilinx platform. As a result, the implemented TRNG offers throughput up to 115.794 Mbps. Besides, the generated random numbers have been tested with the FIPS 140‐1 and NIST 800.22 test suites. The high quality of generated true random numbers have been confirmed by passing all randomness tests. The results have shown that the proposed system can provide not only high throughput but also high quality random bit sequences for a wide variety of embedded cryptographic applications.

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“…Frazier et al applied chaos theory to traffic systems and proved that the prediction performance was better than that of the nonlinear least-squares method [16]. Considering the urban road traffic network, Adewumi et al verified that the traffic flow has the characteristics of chaos and constructed an urban road network traffic flow prediction model based on chaos theory [17]. In recent years, with the rise of machine learning methods, scholars have begun to explore machine learning methods and deep learning in traffic flow prediction technology.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

et al. 2020

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

Cited by 24 publications

References 21 publications

Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)

Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)

A novel high speed Artificial Neural Network–based chaotic True Random Number Generator on Field Programmable Gate Array

An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

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