Nonlinear Characteristics Analysis,Predication And Control Of Traffic Flow

Nonlinear characteristics in traffic flow is one research area in recent years, the research object is to reveal formation mechanism of nonlinear characteristics of traffic system, and then prediction and control. The research targets include each kind traffic flow model and real traffic flow time series. The study contents of this dissertation are nonlinear characteristics, predication and control of traffic flow, the main aspects are as follow:(1) Nonlinear characteristics of macroscopic and microscopic traffic flow time series had been tested, the test methods include:recurrence plot, surrogate time series method, CLY method and power spectrum, the results show that macroscopic and microscopic traffic flow contain nonlinear characteristics and are chaotic.(2) Nonlinear characteristics of microscopic traffic flow time series are analyzed of three aspects. The firstly, method of calculation Lyapunov exponent spectrum of time series based on least-squared support vector machine(LS-SVM) is proposed, and chaos and super chaos characteristics in traffic flow are analyzed by this method, the results show that traffic flow is super chaotic in jam condition. The secondly, methods of estimation ratio of periodic ingredient in system by Kolmogrov Complexity(Kc) and estimation complexity of structure variation of system by Approximate Entropy(ApEn) are proposed. The results of calculation show that different Kc and ApEn compare with different traffic conditions. The thirdly, methods of computation hurst index and multi-fractal spectrum are improved, then, fractal structure of microscopic traffic flow analyzed by hurst index and multi-fractal spectrum. The results show that microscopic traffic flow have different fractal characteristic values in different conditions, so it have different fractal characteristic structure in different conditions.(3) The research of traffic predication include:The firstly, method of forecast chaotic time series based on maximum Lyapunov exponent is improved, in which several neighbouring reference vectors are selected in reconstructed phase space to increase forecasting precision, and the algorithm is given. The result of predication theory chaotic time series show that the improved method has a higher than the original method, meanwhile the influence from noise and number of nearly neighbour vector on the predication results is discussed, and traffic flow is predicated by the improved method. A method for predication of multivariable chaotic time series is proposed, and the calculation process is given, the influence from the noise and number of nearly neighbour vector and the forecasting step on the predication results is discussed, and traffic flow are predicated by the method. A combined dynamic method of forecast traffic volume time series is proposed. The traffic flow is decomposed into cycle item and tendency item and chaotic disturbing item, the sum of the cycle item and the tendency item is forecasted by seasonal index smoothing method, in this process the cycle chosen one day and one week, and recursive least squares with forgetting factor determined the weights, and the chaotic disturbing item is forecasted by partial forecast method. The simulation results show the reasonability and the effectiveness of the method.(4) Based on variable structure control theory, speed controller are designed to stabilized traffic flow, and three control methods are proposed:approximately variable structure control continuous macroscopic traffic flow model; control discrete macroscopic traffic flow model based on discrete approaching low; forecasting control discrete macroscopic traffic flow model based on discrete approaching low. the algorithms are proposed, the simulation results show that the proposed methods can make the traffic density in a certain range. Distribution of traffic flow on different lane was achieved by changing lane rate.