计算机工程 ›› 2011, Vol. 37 ›› Issue (16): 185-187.doi: 10.3969/j.issn.1000-3428.2011.16.063

• 人工智能及识别技术 • 上一篇    下一篇

交通流序列的Volterra自适应预测

张玉梅 1,马 骕 2   

  1. (1. 陕西师范大学计算机科学学院,西安 710062;2. 西安工程大学计算机科学学院,西安 710048)
  • 收稿日期:2010-12-26 出版日期:2011-08-20 发布日期:2011-08-20
  • 作者简介:张玉梅(1977-),女,讲师、博士,主研方向:混沌理论,智能交通控制;马 骕,讲师
  • 基金项目:
    陕西省自然科学基金资助项目(2008k07);陕西师范大学青年科技基金资助项目(200901001)

Volterra Adaptive Prediction of Traffic Flow Sequence

ZHANG Yu-mei 1, MA Su 2   

  1. (1. College of Computer Science, Shaanxi Normal University, Xi’an 710062, China; 2. College of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China)
  • Received:2010-12-26 Online:2011-08-20 Published:2011-08-20

摘要: 基于混沌动力系统的相空间重构和非线性系统的Volterra级数,构建交通流的Volterra自适应预测模型。在应用小数据量法判定交通流存在混沌特性的前提下,分别用平均互信息法和虚假邻点法选取延滞时间和嵌入维数以实现对交通流时间序列的相空间重构。通过Volterra级数展开式建立非线性预测模型,采用LMS自适应算法实时调整模型的系数。以Volterra自适应预测模型对实际采集的高速公路交通流量时间序列及模拟产生的Chens和Duffing混沌时间序列进行仿真研究。结果表明,该模型能够较准确地预测交通流量时间序列和低维混沌时间序列。

关键词: 短时交通流, 预测模型, Volterra级数, 相空间重构, 混沌

Abstract: Based on phase space reconstruction of chaos dynamic system and Volterra series for nonlinear system, Volterra adaptive prediction model for traffic flow is constructed. On the premise that small data set method is used to determine that chaos exists in traffic flow time series, this paper respectively employs average mutual information method and false nearest neighbor technique to choose delay time and embedding dimension so as to perform phase space reconstruction for traffic flow data. Nonlinear prediction model, whose coefficients are real-time updated by LMS adaptive algorithm is constructed by applying Volterra series extensions. It applies this Volterra prediction model to performing simulations for the real measured expressway traffic flow data and chaotic time series generated by Chens and Duffing. Experimental results show that the proposed Volterra adaptive prediction model is capable of effectively predicting traffic flow time sequence and low-dimensional chaotic time sequence.

Key words: short-term traffic flow, prediction model, Volterra series, phase space reconstructio

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