Analysis of Stability-To-Chaos in the Dynamic Evolution of Network Traffic Flows under a Dual Updating Mechanism

Liu, Shixu; Yan, Hao; Easa, Said M.; Guo, Lidan; Tang, Yingnuo

doi:10.3390/su10114182

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…As shown in Figure 5, the average period is 4. Since the largest Lyapunov exponents are all positive numbers (see Figure 6), and the average largest Lyapunov exponents is 0.5979, the time series has chaotic characteristics [35], which can be analyzed and predicted by chaos theory.…”

Section: Case Analysismentioning

confidence: 99%

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

2021

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As the process of informatization progresses in an equipment manufacturing enterprise, its information system becomes a dissipative structure due to the nonlinear interaction of many factors. The objectives of this study were to help enterprises adopt intelligent manufacturing, realize sustainable development strategies, and understand the operation rules of information systems. For this purpose, this study analyzed an anomaly index time series of an information system in the process of integration. First, the embedding dimension, time delay, average period, and maximum Lyapunov exponent of the time series were calculated. The anomaly index with chaotic characteristics was denoised by combining phase space reconstruction with singular value decomposition (SVD). Finally, a radial basis function (RBF) neural network and local nonlinear method were used to predict the anomaly index of 79 test data points. The case simulation results verify that the anomaly index is affected by changes in basic data, system development, and online migration. One instance of local noise reduction can reveal hidden problems in the actual operations of enterprises, and multiple iterations can extract the actual information of the signals, avoid failures at isolated points, and show a clear attractor structure. Both the RBF neural network and local nonlinear approach are effective prediction methods with low relative errors, but the performance of the latter is superior.

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Section: Case Analysismentioning

confidence: 99%

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

2021

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“…It influences by objective factors such as weather, geography, and other human activities. These factors overall influence the changing states of traffic flow conditions [3]. Currently, several literatures demonstrate that traffic state predictions can enhance the stability of traffic flow [4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Parameter Chaotic Fusion Approach for Traffic Flow Forecasting

Huang

Ullah

2020

IEEE Access

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Since traffic is a time-dependent and complicated non-linear system. Chaos Theory has been specifically designed to identify chaotic behaviour and properties for such systems. Previous researches have been restricted to single traffic parameter, which fails to get actual behaviour of the traffic systems. In this work, we have devised the multi-parameters chaos prediction method to describe the tendency of traffic from different aspects for prediction of single parameter chaotic time series. The fusion method is considered the relationship of different traffic parameters according to the phase space reconstruction. Taking the exit ramp of Xishanping expressway in Beibei district, Chongqing Municipality as the example, the feasibility and reliability of traffic state prediction were tested. The trail results indicates that more features of realistic traffic conditions are reflected by fusing multiple traffic parameters, and gain real accuracy improvements in traffic prediction. Compared with three single-parameter time series prediction methods, the mean absolute relative error of the multi-parameter prediction method decreased by 2.42, 2.39 and 0.8 respectively, the mean absolute relative error decreased by 2.33, 3.25 and 1.27, and the equal coefficient reached 0.9528 with a slight increase. Proposed method will definitely generates the opportunity for next generation of traffic control that are better able to detect the dynamic states of traffic, and therefore more effectively prevent the traffic congestion and pollution in the urban areas worldwide.

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Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

Barroso

Albuquerque-Oliveira

Oliveira-Neto

2020

Journal of Transport Geography

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Analysis of Stability-To-Chaos in the Dynamic Evolution of Network Traffic Flows under a Dual Updating Mechanism

Cited by 3 publications

References 25 publications

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

Research on the Chaotic Characteristics and Noise Reduction Prediction of Information System Anomalies in Equipment Manufacturing Enterprises

A Multi-Parameter Chaotic Fusion Approach for Traffic Flow Forecasting

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

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