Classification of Urban Expressway Traffic Flow Situation Based on Fuzzy Clustering

doi:10.3969 /j.issn.1674-0696.2013.04.25

Journal of Chongqing Jiaotong University(Natural Science) ›› 2013, Vol. 32 ›› Issue (4): 652-655.DOI: 10.3969 /j.issn.1674-0696.2013.04.25

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Classification of Urban Expressway Traffic Flow Situation Based on Fuzzy Clustering

Yin Junjie，Ding Hongfei，Bo Wu，Zhong Mei

School of Traffic ＆ Transportation，Southwest Jiaotong University，Chengdu 610031，Sichuan，China

Received:2012-05-22 Revised:2013-04-27 Online:2013-08-15 Published:2014-10-27

基于模糊聚类的城市快速路交通流状态划分

殷俊杰，丁宏飞，薄雾，钟媚

西南交通大学交通运输与物流学院，四川成都 610031

作者简介:殷俊杰(1988—),男,四川成都人,硕士研究生,主要从事智能交通及交通工程方面的研究。E-mail:420164881@qq.com。
基金资助:
四川省科技支撑计划项目( 2011FZ0050)

Abstract

Abstract: In order to solve the problem of urban expressway traffic flow situation judgment，an improved algorithm which uses fuzzy C-means clustering method is proposed． Based on the combination of FCM algorithm and hierarchical clustering algorithm，the hierarchical clustering algorithm is firstly used to get the number of cluster type and initial cluster centers; then Ｒelief F algorithm is used to calculate the weights of the chose features，which affects the division of the traffic conditions;at last FCM is used to perform the cluster to obtain the division results of traffic conditions． A simulation is performed by software VISSIM to confirm the proposed methodology． Through the comparative analysis，it is obtained that the propose method can improve the classification results of urban expressway traffic flow situation．

Key words: traffic conditions, fuzzy cluster, hierarchical clustering, feature weight, Ｒelief F algorithm

摘要： 针对城市快速路交通流状态分类的问题，提出了一种改进的模糊 C 均值( FCM) 算法。结合层次聚类算法和FCM 聚类算法，运用层次聚类算法得到最佳聚类数和初始聚类中心，并通过Ｒelief F 特征加权对影响交通状态的不同特征指标赋予相应的权值，最终用 FCM 算法再次聚类得出交通流状态的分类结果。以 VISSIM 为工具，对该方法进行了模拟。对比分析结果显示，所提出的方法能够提高城市快速路交通流状态分类的效果。

关键词: 交通状态, 模糊聚类, 层次聚类, 特征加权, Ｒelief F 算法

CLC Number:

U491.1

Yin Junjie，Ding Hongfei，Bo Wu，Zhong Mei. Classification of Urban Expressway Traffic Flow Situation Based on Fuzzy Clustering[J]. Journal of Chongqing Jiaotong University(Natural Science), 2013, 32(4): 652-655.

殷俊杰，丁宏飞，薄雾，钟媚. 基于模糊聚类的城市快速路交通流状态划分[J]. 重庆交通大学学报（自然科学版）, 2013, 32(4): 652-655.

References

[1] 石征华，侯忠生．城市快速路拥挤度判别方法研究[J]．交通与计算机，2006， 24( 5) : 20-23．
Shi Zhenghua，Hou Zhongsheng． Urban highway congestion degree evaluation method [J]． Computer and Communications，2006，24( 5) : 20-23．
[2] 郝媛，徐天东，孙立军．基于模糊的城市快速路交通流状态判别[J]．公路工程， 2008，33( 2) : 94-99．
Hao Yuan，Xu Tiandong，Sun Lijun． Fuzzy logics based traffic state identification on urban expressway [J]． Highway Engineering，2008，33( 2) : 94-99．
[3] 陈德望．基于模糊聚类的快速路交通流状况分类[J]．交通运输系统工程与信息，2005，5( 1) : 62-67．
Chen Dewang． Classification of traffic flow situation of urban freeways based on fuzzy clustering [J]． Journal of Transportation Systems Engineering and Information Technology，2005，5 ( 1) : 62-67．
[4] 王璇，翁小雄．基于模糊C 均值聚类的快速路交通流相态划分[J]．交通信息与安全， 2009，27( 1) : 149-152．
Wang Xuan，Weng Xiaoxiong． Classification of three-phase traffic flow of urban expressway based on fuzzy C-means clustering[J]．Journal of Transport Information and Safety，2009，27 ( 1) : 149-152．
[5] 王江锋，高峰．一种用于快速路交通状态识别的模糊逻辑方法[C]/ /第一届同济交通论坛-城市交通系统设计学术会议论文集．上海: 同济大学， 2004: 63-69．
Wang Jiangfeng，Gao Feng． A fuzzy logic method applied in the identification of expressway traffic state[C]/ /Tongji Transport Forum-Conference Proceedings of Urban Transportation Systems Design． Shanghai: Tongji University，2004: 63-69．
[6] 张晓亮，张可，刘浩，等．基于FCM-粗糙集的城市快速路交通状态判别[J]．系统工程， 2010，28( 8) : 75-80．
Zhang Xiaoliang，Zhang Ke，Liu Hao，et al． Identification of urban expressway traffic state based on FCM-Ｒough sets [J]． Systems Engineering，2010，28( 8) : 75-80．
[7] Bezdek J C． Pattern Ｒecognition with Fuzzy Objective Function Algorithms[M]． New York: Plenum Press，1981．
[8] 张姣玲．利用FCM 求解最佳聚类数的算法[J]．计算机工程与应用， 2008，44( 22) : 65-67．
Zhang Jiaoling． FCM-based algorithms for determining optimal number of clusters [J]． Computer Engineering and Applications，2008，44( 22) : 65-67．
[9] 窦慧丽，王国华．基于模糊聚类和判别分析的交通状态提取算法[J]．交通信息与安全， 2010， 28( 2) : 13-15．
Dou Huili，Wang Guohua． Algorithm of traffic state extraction based on fuzzy clustering and discriminant analysis [J]． Journal of Transport Information and Safety，2010，28( 2) : 13-15．
[10] Johnson S C，Hierarchical clustering schemes [J]． Psychometrika，1967，32( 3) : 241-243．
[11] Kira K，Ｒendell L A． A practical approach to feature selection[C]/ / Proceedings of the 9th International Workshop on Machine Leaning． San Francisco，C A: Morgan Kaufmann，1992: 249-256．
[12] Kononenko I． Estimation attributes: analysis and extensions of ＲEL IEF [C]/ /European Conference on Machine Learning． Catania:Springer Verlag，1994: 171-182．

Classification of Urban Expressway Traffic Flow Situation Based on Fuzzy Clustering

基于模糊聚类的城市快速路交通流状态划分

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