基于FFCM聚类的城市交通拥堵判别研究  

Study of urban traffic congestion judgment based on FFCM clustering

作　　者：杨祖元[1] 黄席樾[1] 杜长海[1] 唐明霞[1] 

机构地区：[1]重庆大学自动化学院,重庆400044

出　　处：《计算机应用研究》2008年第9期2768-2770,共3页Application Research of Computers

基　　金：重庆市自然科学基金重点资助项目(CSTC;2006BA6016)

摘　　要：对城市道路交通拥堵状态判别的问题,提出了一种硬C-均值(HCM)聚类与模糊C-均值(FCM)聚类相结合的快速模糊C-均值聚类(FFCM)算法。用硬聚类的结果对模糊聚类初始值的选取进行指导,以加速算法的收敛过程。将该算法用于城市交通流数据的聚类分析结果表明,该算法能够快速而有效地对城市交通流状况进行判别,为动态交通拥堵预警和交通疏导策略的制定提供依据。In order to solve the problem of urban traffic congestion judgment, a fast fuzzy C-means （FFCM） clustering method integrating hard C-means clustering method and fuzzy C-means clustering method was put forward. In this algorithm, the result of HCM clustering is the basis of the initial value of FFCM, which can speed-up its convergence. Then it＇ s applied to a set of real traffic flow data. The clustering result shows that this method enjoy good performance in fast and effective distinguishing urban traffic congestion and will contribute to dynamic traffic congestion warning and traffic guidance.

关 键 词：城市交通 快速模糊聚类 拥堵判别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]