交通流量序列混沌特性分析及DFPSOVF预测模型

Chaotic characteristic analysis for traffic flow series and DFPSOVF prediction model

以实际采集的交通流量序列作为研究对象,分别应用互信息法和虚假邻点法确定其延迟时间和最佳嵌入维数,完成交通流量序列的相空间重构.通过计算交通流量序列的饱和关联维数和最大Lyapunov指数判定其混沌特性.以最小均方(LMS)算法为基础,构建了一种基于Davidon-Fletcher-Powell方法的二阶Volterra模型(DFPSOVF),其应用了一种可随输入信号变化而实时变化的基于后验误差假设的可变收敛因子技术.DFPSOVF模型避免了在Volterra模型中采用LMS自适应算法调整系数时参数选择不当引起的问题.将DFPSOVF模型应用于具有混沌特性的短时交通流量预测,结果表明:当模型记忆长度与交通流量序列的嵌入维数选择一致时,模型的预测精度较高,可以满足交通诱导和交通控制的需要,为智能交通控制提供了新方法、新思路及工程应用参考.

Time delay and optimal embedding dimension for the real measurement traffic flow series, which are used by mutual informa-tion method and false nearest-neighbor method, respectively, are determined for phase space reconstruction of the traffic flow series. The saturation correlation dimension and the largest Lyapunov exponent for traffic flow series are calculated to estimate its chaotic characteristics. Based on the least mean square （LMS） algorithm, a novel second-order Volterra model using Davidon-Fletcher-Powell method （DFPSOVF） is constructed, in which a variable convergence factor based on a posteriori error assumption, characteristic of real-time change with the input signal, is applied. DFPSOVF model can avoid some problems caused by improper selection of pa-rameters when using LMS adaptive algorithm for coefficient adjustment in Volterra model. DFPSOVF model can also be applied to short-term traffic flow prediction with chaotic characteristics. Results show that when model memory length is consistent with embed-ding dimension of traffic flow series, it obtains higher prediction accuracy, which can meet the needs for traffic guidance and traffic control, and can also provide a new method, a new idea and engineering application reference for intelligent traffic control.