Application of Fuzzy C-Means in Level Clustering of Traffic Accident Vulnerability

被引:1
|
作者
Syahputri, K. [1 ]
Sari, R. M. [1 ]
Rizkya, I [1 ]
Farhan, T. A. [1 ]
Syardhi, O. C. [1 ]
机构
[1] Univ Sumatera Utara, Fac Engn, Dept Ind Engn, Medan, Indonesia
来源
2ND INTERNATIONAL CONFERENCE ON INDUSTRIAL AND MANUFACTURING ENGINEERING (ICI&ME 2020) | 2020年 / 1003卷
关键词
D O I
10.1088/1757-899X/1003/1/012099
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traffic accidents are a serious problem and need the right handling. The high level of traffic accidents is a problem often happened in big cities, especially in the Medan city. Data on traffic accident of 2018 are obtained from the Medan Police Station Unit and from the official website of Badan Pusat Statistika recorded of 1393 traffic accidents with 254 deaths, 721 seriously injured, and 985 slightly injured. Then this data grouped based on the accident road segment and culestered level of vulnerability using the Fuzzy C-Means Algorithm. The results of the Fuzzy C-Means algorithm are 13 road segments in vulnerable clusters, 36 road segments in quite vulnerable clusters and 53 road segments in safe clusters. Road segments in vulnerable clusters are marked in red, quite vulnerable clusters are marked in yellow and safe culsters are in green. Data validation obtained by the traffic accident vulnerability clustered by classifying traffic accident data in the first two months of 2019 using the same method, namely the Fuzzy C-Means Algorithm, the conformity level of the Fuzzy C-Means resulted of 78,75%.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] The global Fuzzy C-Means clustering algorithm
    Wang, Weina
    Zhang, Yunjie
    Li, Yi
    Zhang, Xiaona
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 3604 - +
  • [32] On Fuzzy c-Means and Membership Based Clustering
    Torra, Vicenc
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT I (IWANN 2015), 2015, 9094 : 597 - 607
  • [33] Intuitionistic fuzzy C-means clustering algorithms
    Xu, Zeshui
    Wu, Junjie
    JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2010, 21 (04) : 580 - 590
  • [34] Gaussian Collaborative Fuzzy C-Means Clustering
    Yunlong Gao
    Zhihao Wang
    Huidui Li
    Jinyan Pan
    International Journal of Fuzzy Systems, 2021, 23 : 2218 - 2234
  • [35] Fuzzy c-means clustering of incomplete data
    Hathaway, RJ
    Bezdek, JC
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (05): : 735 - 744
  • [36] An Accelerated Fuzzy C-Means clustering algorithm
    Hershfinkel, D
    Dinstein, I
    APPLICATIONS OF FUZZY LOGIC TECHNOLOGY III, 1996, 2761 : 41 - 52
  • [37] Novel possibilistic fuzzy c-means clustering
    School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
    不详
    Tien Tzu Hsueh Pao, 2008, 10 (1996-2000):
  • [38] Hierarchically Structured Fuzzy c-Means Clustering
    Hye Won Suk
    Ji Yeh Choi
    Heungsun Hwang
    Behaviormetrika, 2013, 40 (1) : 1 - 17
  • [39] Fuzzy Approaches To Hard c-Means Clustering
    Runkler, Thomas A.
    Keller, James M.
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2012,
  • [40] Suppressed fuzzy C-means clustering algorithm
    Fan, JL
    Zhen, WZ
    Xie, WX
    PATTERN RECOGNITION LETTERS, 2003, 24 (9-10) : 1607 - 1612