计算机应用 ›› 2012, Vol. 32 ›› Issue (08): 2378-2384.DOI: 10.3724/SP.J.1087.2012.02378

• 典型应用 • 上一篇    下一篇

基于模糊理论的交通溢流识别算法

张立东1,2,贾磊1,朱文兴1   

  1. 1. 山东大学 控制科学与工程学院,济南 250061
    2. 山东交通学院 交通与物流工程学院,济南 250023
  • 收稿日期:2012-02-20 修回日期:2012-04-22 发布日期:2012-08-28 出版日期:2012-08-01
  • 通讯作者: 张立东
  • 作者简介:张立东(1979-),男,山东蒙阴人,助理研究员,博士研究生,主要研究方向:智能交通、交通流理论、并行计算;
    贾磊(1959-),男,山东济南人,教授,博士生导师,博士,主要研究方向:智能交通、交通流理论、建筑节能;
    朱文兴(1971-),男,山东平度人,副教授,博士,主要研究方向:智能交通、交通流理论。
  • 基金资助:
    国家自然科学基金资助项目(61174175);中国博士后科学基金资助项目(20100481265);山东省博士后创新项目专项基金资助项目(201102025);山东省自然科学基金资助项目(Y2008G34)

Traffic spillover identification based on fuzzy theory

ZHANG Li-dong1,2,JIA Lei1,ZHU Wen-xing1   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan Shandong 250061, China
    2. School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan Shandong 250023, China
  • Received:2012-02-20 Revised:2012-04-22 Online:2012-08-28 Published:2012-08-01
  • Contact: ZHANG Li-dong

摘要: 交通溢流是交通拥堵的一种极端现象,会导致交通系统的严重紊乱。为实现对交通溢流的控制,必须先对其进行识别。以智能模糊推理为理论基础,提出交通溢流模糊识别算法。该算法推理器以车辆排队比率和路段平均速度为输入语言变量,以道路交通溢流严重程度为输出语言变量,采取Mamdani推理法为蕴含规则,在确定基本论域和离散论域的基础上,建立了模糊规则查询表,实现了交通溢流状态的识别。仿真结果表明,该算法的识别正确率达到98%,证明了模糊算法可以较好地实现交通溢流的识别。

关键词: 智能交通, 交通溢流, 模糊推理理论, 语言变量, 隶属度函数, 交通仿真

Abstract: Traffic spillover is a kind of extreme traffic jam phenomenon, which can lead to serious disorder of traffic system. Identification algorithm should be carried out first in order to realize traffic spillover control, so the fuzzy identification algorithm was presented based on fuzzy inference theory. The fuzzy identification inference machine took the car queue rate and road average speed as the input language variables, and the traffic spillover severity index as the output variable. Mamdani was adopted as the implication method. Based on the definition of basic language domain and discrete language domain, the fuzzy query rules table was built based on traffic experts' knowledge. Finally, the simulation results indicate the correct rate of identification reaches 98%, which proves the fuzzy inference method is a good tool to recognize the state of traffic spillover.

Key words: Intelligent Traffic System (ITS), traffic spillover, fuzzy inference theory, language variable, membership function, traffic simulation

中图分类号: