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Research for Short-term Traffic Flow Forecasting Methoed Based on Chaos Theory

Author: ZhuoHui
Tutor: JiaLiMin
School: Beijing Jiaotong University
Course: Safety Technology and Engineering
Keywords: Intelligent Transportation Systems Short-term traffic flow Forecast Chaos Phase Space Reconstruction Neural Networks Wavelet Analysis
CLC: U491.112
Type: Master's thesis
Year: 2008
Downloads: 303
Quote: 1
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Abstract


Short-term traffic flow forecasting for dynamic route guidance, advanced traffic management, traffic control and safety, are of great significance to the field of traffic engineering has become a key research project is one of the core research content of the intelligent transportation system. People, vehicles, road combined effects of a complex giant system, the nature of the traffic flow system is an open, far from equilibrium systems. The system exists within the nonlinear interaction process has not reversible. Get through some observer to the time series of a state of the system bears traces of the chaotic motion of all the variables in the system. Chaotic phase space reconstruction theory provides a technique for understanding of the internal evolution of the kind of system to reconstruct the overall behavior of complex systems, it is one-dimensional projection of the overall system behavior, and then according to their complex nature within the system The characteristics were analyzed. Reconstruction method on the basis of the analysis of the traffic flow time series characteristics of the phase space of the dynamical system chaotic time series study on the reconstruction phase space parameters were calculated. On the basis of the analysis of short-term traffic flow prediction method based on chaos theory, for the local area to determine the shortcomings of neighboring points, improved program; introduced the idea of ??error correction, to take full advantage of the step prediction, improve the prediction accuracy. On this basis, the weighted one-rank local prediction model; combined with parallel processing and powerful nonlinear mapping ability of neural networks, the proposed short-term traffic chaos local-RBF neural network theory flow prediction model relative to the linear fit weighted first order local prediction model improves the accuracy of the forecast; non-stationary traffic flow time series, the introduction of the wavelet analysis method to establish Wavelet - Chaotic Local-RBF neural network combination forecasting model, the model from the time-frequency multiresolution analysis perspective of short-term traffic flow time series with non-stationary characteristics, to show the details inherent in the original sequence of traffic flow, taking into account the chaos characteristics of short-term traffic flow more applicable has a non-linear, chaotic and non-stationary characteristics in the short-term traffic flow forecast. The instance data verification results show that these three models are capable of achieving better predict, comparatively speaking, the best wavelet - Chaotic Local-RBF neural network prediction model forecasts.

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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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