• DocumentCode
    2643453
  • Title

    AOID: adaptive on-line incident detection system

  • Author

    Boedihardjo, Arnold P. ; Lu, Chang-Tien

  • Author_Institution
    Dept. of Comput. Sci., Virginia Polytech. Inst. & State Univ.
  • fYear
    2006
  • fDate
    17-20 Sept. 2006
  • Firstpage
    858
  • Lastpage
    863
  • Abstract
    The provisions of any emergency management system with respect to the public safety necessitates the inclusion of the transportation network. The transportation network provides a means for mitigation strategies for any disaster, whether it is natural or human-induced. In this paper, we introduce a set of tools to integrate with a traffic information system to provide automatic traffic incident detection and traffic forecast. Current automated incident detection techniques may not perform well under changing traffic patterns, recurrent congestions, and may require large amounts of training data. We propose a solution to mitigate these shortcomings by utilizing predicted traffic models and performing comparative analysis against observed traffic patterns to automatically detect incidents
  • Keywords
    disasters; emergency services; traffic information systems; adaptive online incident detection system; automatic traffic incident detection; data analysis; data mining; emergency management system; intelligent highway system; public safety; traffic forecast; traffic information system; traffic pattern; transportation network; Disaster management; Management information systems; Pattern analysis; Performance analysis; Predictive models; Safety; Telecommunication traffic; Traffic control; Training data; Transportation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
  • Conference_Location
    Toronto, Ont.
  • Print_ISBN
    1-4244-0093-7
  • Electronic_ISBN
    1-4244-0094-5
  • Type

    conf

  • DOI
    10.1109/ITSC.2006.1706851
  • Filename
    1706851