Urban Chaos Of Traffic Flow Modeling Method

The traffic flow system is a complex large system. The complex non-linear relationships exist in various factors of the system, which leads to the generation of chaos phenomenon. And accurate and reliable prediction of short-term traffic flow is one of the systematic key technology of urban traffic control and route guidance in ITS. However, with the shortening of the forecasting term, the uncertainty and variability of the traffic flow becomes more and more seriously. And the traditional forecasting methods can't achieve the desired precision.Chaos theory is a major and effective theory, which solves nonlinear problems. Chaotic theory has been proved to be an important and useful tool, and it can provide new methods which grasp the essences and rules of the complex phenomena in traffic flow starting with the transport system itself.The main contribution can be stated as follows:1. As the uncertainty of traffic system is in close connection with chaos, after introducing the definition and the basic features of chaos, the chaotic characteristics and uncertainty of the urban traffic flow have been researched in detail. Meanwhile, recent work in studies of traffic flow chaos at home and abroad has been summarized.2. Phase space reconstruction is the base of using dynamical methods to analyze nonlinear time series. The key of phase space reconstruction is the estimation of its parameter. For the purpose of solving the problem of uncertainty and nonlinearity of the short-term traffic flow, in this paper we introduce the theory of phase space reconstruction and discuss the methods of choosing embedding dimension and delay time. And based on this, the chaotic characteristics of the urban traffic flow time series have been distinguished.3. On the basis of analysis of urban short-term traffic flow time series and the characteristic of chaos, prediction method improving adding-weight one-rank local-region, prediction method based on the largest Lyapunov exponent and a novel local linear prediction method based on the kernel function are successfully used in real-time prediction of traffic flow. The experiment results show that three proposed methods are effective.