• DocumentCode
    3204687
  • Title

    Traffic Incident Duration Prediction Based on Artificial Neural Network

  • Author

    Guan, Liping ; Liu, Weiming ; Yin, Xiangyuan ; Zhang, Luping

  • Volume
    3
  • fYear
    2010
  • fDate
    11-12 May 2010
  • Firstpage
    1076
  • Lastpage
    1079
  • Abstract
    The prediction of traffic incident duration is an important foundation of advanced incident management system and driver information system. In this paper, actual traffic incident data was used to study the prediction problem of traffic incident duration by the method of neural network. 660 sets of actual traffic incident data from a freeway management center were used to train a neural network model, and 170 sets of incident data in the same data collection, which are different from training data, were used to test the prediction effect of the model. The test result shows that the correlation of the prediction values and the actual values is 0.8535, which indicates that the prediction result of the neural network model can basically represent actual incident duration.
  • Keywords
    correlation methods; neural nets; transportation; artificial neural network; data collection; driver information system; freeway management center; traffic incident duration prediction; Artificial neural networks; Decision trees; Disaster management; Intelligent networks; Intelligent transportation systems; Neural networks; Predictive models; Telecommunication traffic; Testing; Traffic control; incident duration; incident management; intelligent transportation system; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-7279-6
  • Electronic_ISBN
    978-1-4244-7280-2
  • Type

    conf

  • DOI
    10.1109/ICICTA.2010.418
  • Filename
    5523317