Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (27): 234-237.DOI: 10.3778/j.issn.1002-8331.2010.27.066

• 工程与应用 • Previous Articles     Next Articles

Study of TOD based on Isomap and K-means clustering algorithm

SU Yuan-ying,DONG Chao-jun   

  1. Institute of Information,Wuyi University,Jiangmen,Guangdong 529020,China
  • Received:2009-02-26 Revised:2009-04-28 Online:2010-09-21 Published:2010-09-21
  • Contact: SU Yuan-ying



  1. 五邑大学 信息学院,广东 江门 529020
  • 通讯作者: 苏苑英

Abstract: In the time of day control schemes,the basic problem is to program the traffic intervals rationally.But,as the traditional main means to fix the lengths of intervals,the artificial method has subjectivity and one-sidedness.To avoid the shortcomings,this paper puts forward a new method to fix the lengths of intervals.Based on the concepts of manifold and manifold learning algorithm,it raises an estimation method which can discover the intrinsic dimensions of the flow data in isolated intersection.Firstly,the paper supposes the measured data that has its lower-dimensional manifold embedded in the high-dimension manifold;with Isomap algorithm,it finds out the intrinsic dimensions;finally using the reduced sample data,it clusters them with K-means algorithm and gets corresponding traffic intervals.The results indicate that the Isomap and K-means algorithm based clustering method outperforms the traditional artificial method as well as the other methods in the traffic signal periods division of day.

Key words: urban traffic, traffic intervals programming, manifold learning algorithm, K-means clustering algorithm, traffic signal control

摘要: 为避免在城市交通多时段定时控制中人工时段划分方法所带来的主观性、片面性,以提高工作效率,结合流形学习算法中的等距映射算法和K均值聚类算法,提出了一种时段划分新方法。给出一组实测数据,假设它是一个存在于高维数据空间中的低维流形;利用等距映射算法,找出它的内在维数,将数据约简;根据约简后的样本点分布情况,利用K均值聚类算法聚类,划分交通时段。实验结果表明,此方法划分交通时段准确高效,并有效地避免了人工划分方法的主观性。

关键词: 城市交通, 交通信号时段划分, 流形学习算法, K均值聚类, 信号控制

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