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Identification Method of Chaos in Traffic Flow from Small Data Sets

Author: ZhangYong
Tutor: HeGuoGuang
School: Tianjin University
Course: Systems Engineering
Keywords: Traffic Flow Chaos Lyapunov exponent Wavelet Analysis Multi-scale decomposition Support Vector Machine Wavelet Neural Network
CLC: U491.112
Type: Master's thesis
Year: 2007
Downloads: 90
Quote: 4
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Abstract


In order to achieve based the chaotic traffic control , must as soon as possible to identify the traffic flow in the chaos , in order to take timely control measures tends orderly traffic flow . Recognition Method of Chaos , however , demand for large sample size calculation slow , unable to meet the real-time requirements . Therefore, the study of online real-time to quickly determine the chaotic method can not only fill the theoretical gaps , but also has a high practical value . From the qualitative determination and quantitative estimate of the chaotic phenomena in the two aspects of traffic flow , and the effectiveness of the proposed identification method is verified by simulation . This paper presents a qualitative judgment on the chaotic traffic flow determination method based on the system of prior information chaos . The method through knowledge discovery method based on support vector machine to find a correspondence between Chaos and initial conditions , the decision of chaos into the problem to determine whether the system to meet the specific initial conditions , in order to achieve a small amount of data on the chaos of the fast decision. Give a qualitative overall framework of the system chaos is determined in the section , and details of the realization of the various subsystems . In quantitative estimates of chaos in traffic flow , the proposed method based on wavelet neural network and calculation of Lyapunov Exponents . The method uses a reconfigurable system to study the phase space reconstruction reconstruction of the one-dimensional sequence , reconstructed dynamic system of equations is estimated using wavelet neural network , and then calculate the Lyapunov exponent spectrum of the system of equations , unknown system . The experimental part of the method of Logistic map and Henon map one-dimensional sequence calculated results show that the wavelet neural network has excellent non-linear approximation capability , the final calculation of the high accuracy of the results , the needs of the small sample size , to meet the real-time requirements. The simulation test the application of the method in the traffic stream .

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Traffic Survey and Planning > Traffic survey > Traffic flow
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