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
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
  • [41] Application of Hard C-means and Fuzzy C-means in data fusion
    Tang Ai-Hong
    Cai Li-An
    Zhang You-Mei
    DIGITAL MANUFACTURING & AUTOMATION III, PTS 1 AND 2, 2012, 190-191 : 265 - 268
  • [42] Relative entropy fuzzy c-means clustering
    Zarinbal, M.
    Zarandi, M. H. Fazel
    Turksen, I. B.
    INFORMATION SCIENCES, 2014, 260 : 74 - 97
  • [43] Diverse fuzzy c-means for image clustering
    Zhang, Lingling
    Luo, Minnan
    Liu, Jun
    Li, Zhihui
    Zheng, Qinghua
    PATTERN RECOGNITION LETTERS, 2020, 130 (130) : 275 - 283
  • [44] Soil clustering by fuzzy c-means algorithm
    Goktepe, AB
    Altun, S
    Sezer, A
    ADVANCES IN ENGINEERING SOFTWARE, 2005, 36 (10) : 691 - 698
  • [45] Robust Weighted Fuzzy C-Means Clustering
    Hadjahmadi, A. H.
    Homayounpour, M. A.
    Ahadi, S. M.
    2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2008, : 305 - 311
  • [46] Gaussian Collaborative Fuzzy C-Means Clustering
    Gao, Yunlong
    Wang, Zhihao
    Li, Huidui
    Pan, Jinyan
    INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2021, 23 (07) : 2218 - 2234
  • [47] Application of fuzzy ARTMAP and fuzzy c-means clustering to pattern classification with incomplete data
    Chee Peng Lim
    Mei Ming Kuan
    Robert F. Harrison
    Neural Computing & Applications, 2005, 14 : 104 - 113
  • [48] Kernel Functions Derived from Fuzzy Clustering and Their Application to Kernel Fuzzy c-Means
    Hwang, Jeongsik
    Miyamoto, Sadaaki
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2011, 15 (01) : 90 - 94
  • [49] Application of fuzzy ARTMAP and fuzzy c-means clustering to pattern classification with incomplete data
    Lim, C
    Kuan, M
    Harrison, R
    NEURAL COMPUTING & APPLICATIONS, 2005, 14 (02): : 104 - 113
  • [50] Clustering Traffic Flow Patterns by Fuzzy C-Means Method: Some Preliminary Findings
    Silgu, Mehmet Ali
    Celikoglu, Hilmi Berk
    COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2015, 2015, 9520 : 756 - 764