Journal of South China University of Technology (Natural Science Edition) ›› 2007, Vol. 35 ›› Issue (10): 194-197,232.

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Forecastability Analysis and Combination Forecast AIgorithm for Short-Term Traffic Flow of Related Intersections

Xu Jian-min  Fu Hui  Xu Lun-hui   

  1. School of Traffic and Communications , South China Univ. of Tech. , Guangzhou 510640 , Guangdong , China
  • Received:2007-03-01 Online:2007-10-25 Published:2007-10-25
  • Contact: 徐建闽(1960-) ，男，教授，博士生导师，主要从事现代交通流信息检测与处理、智能交通系统、现代交通控制研究. E-mail:aujmxu@scut. edu.cn
  • About author:徐建闽(1960-) ，男，教授，博士生导师，主要从事现代交通流信息检测与处理、智能交通系统、现代交通控制研究.
  • Supported by:

    国家"863" 计划项目(2006AA11 Z21l ) ;国家自然科学基金资助项目(50578064)

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Abstract:

In this paper, first , the related intersection is defined according to the relationship between traffic flow time series. Then , several forecastability indexes for the short-term traffic flow of related intersections are proposed for the quantitative analysis , and the computation method of the first Lyapunov index to traffic flow time series is described.Moreover , a nonlinear combination forecast model using RBF neural network is set up based on a set of forecast models after the forecastability analysis. Thus , a combination forecast algorithm for the short-term traffic flow of related intersections comes into being. The results of simulation indicate that the proposed method is more efficient than the single forecast method.

Key words: related intersection, short-term traffic flow, forecastability, combination forecast