华南理工大学学报(自然科学版) ›› 2006, Vol. 34 ›› Issue (9): 66-69,75.

• 交通与运输工程 • 上一篇    下一篇

基于灰色关联分析的路段行程时问卡尔曼滤波预测算法

温惠英 徐建闽 傅惠   

  1. 华南理工大学 交通学院,广东 广州 510640
  • 收稿日期:2006-01-12 出版日期:2006-09-25 发布日期:2006-09-25
  • 通信作者: 温惠英(1965-),女,在职博士生,副教授,主要从事ITS与现代物流技术、交通运输规划与管理的研究. E-mail:hywen@scut.edu.cn
  • 作者简介:温惠英(1965-),女,在职博士生,副教授,主要从事ITS与现代物流技术、交通运输规划与管理的研究.
  • 基金资助:

    国家自然科学基金资助项目(B04B50590)

Estimation Algorithm with Kalman Filtering for Road Travel Time Based on Grey Relation Analysis

Wen Hui-ying  Xu Jian-min  Fu Hui   

  1. School of Traffic and Communications,South China Univ.of Tech.,Guangzhou 510640,Guangdong.China
  • Received:2006-01-12 Online:2006-09-25 Published:2006-09-25
  • Contact: 温惠英(1965-),女,在职博士生,副教授,主要从事ITS与现代物流技术、交通运输规划与管理的研究. E-mail:hywen@scut.edu.cn
  • About author:温惠英(1965-),女,在职博士生,副教授,主要从事ITS与现代物流技术、交通运输规划与管理的研究.
  • Supported by:

    国家自然科学基金资助项目(B04B50590)

摘要: 为改善卡尔曼滤波用于时间序列预测时的自适应性能,提出基于灰色关联分析的路段行程时间实时预测算法.首先,利用灰色理论对行程时间序列的各影响因素进行灰色关联分析,根据灰色关联度的大小来选取路段行程时间的主要影响因素,由此建立相应的动态方程.在此动态方程基础上,通过卡尔曼滤波递推进行路段行程时间预测.文中利用深圳某交通干道上的实测行程时间进行仿真实验,结果表明该算法的综合预测性能优于常规卡尔曼滤波方法,可应用于正常交通流状况下的路段行程时间预测.

关键词: 行程时间, 预测, 卡尔曼滤波, 灰色关联分析

Abstract:

In order to improve the self-adaptability of Kalman filtering applied to the estimati0n of time series. a real-time estimation algorithm for road travel time is put forwarded based on the grey relation analysis. The grev re-lation analysis of various factors to affect the travel time series is first caried out based on the gray the0ry. and the main factors to influence the road travel time are picked out according to the grey relevancy degree. Then.the cor-responding dynamic equations are set up,and the estimated values are obtained using a set of recursion form ulas.The real data of the road travel time collected in an artery in Shenzhen City are finallv used to perform simulated experiments.The results indicate that the proposed algorithm is suitable for the estimation of road travel time in natu-ral traffic flow because it is of better integrated perform ance than the conventional Kalman fihering model.

Key words: time, estimation, Kalman filtering, grey relation analysis