• DocumentCode
    3510388
  • Title

    Study on Automated Incident Detection Algorithms Based on PCA and SVM for Freeway

  • Author

    Cai, Zhili ; Ding, Qiushi ; Wang, Mingting

  • Author_Institution
    Shandong Jiaotong Univ., Jinan, China
  • Volume
    2
  • fYear
    2010
  • fDate
    11-12 Nov. 2010
  • Firstpage
    420
  • Lastpage
    424
  • Abstract
    According to the freeway traffic flow characteristics under incidents situation, this paper puts forward a kind of AID algorithm based on PCA & SVM. Above all, PCA has been applied to extract principle components from preselected indexes, to realize dimension reductions, and construct the traffic eigenvectors. And then, validity of the new algorithm proposed in this paper has been tested using various SVM models, and also contrasts with the results of algorithms only based on SVM. The simulation results demonstrate that the new algorithm can detect incidents more effectively and more expeditiously, with the preferable detection performance. Especially, detection effects of the new algorithm based on linear SVM models are the best.
  • Keywords
    automated highways; eigenvalues and eigenfunctions; object detection; principal component analysis; road traffic; support vector machines; AID algorithm; PCA; SVM; automated incident detection; freeway; intelligent transportation system; principle component analysis; support vector machine; traffic eigenvector; Automated Incident Detection (AID); Freeway; Intelligent Transportation Systems (ITS); Principal Component Analysis (PCA); Support Vector Machine (SVM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Optoelectronics and Image Processing (ICOIP), 2010 International Conference on
  • Conference_Location
    Haiko
  • Print_ISBN
    978-1-4244-8683-0
  • Type

    conf

  • DOI
    10.1109/ICOIP.2010.124
  • Filename
    5662911