Traffic incident is the main cause of nonrecurring congestion. To detect incident and to verify incident as soon as possible will reduce their adverse effects. Several traditional algorithms used in automatic incident detection are reviewed. The parameters of traffic flow detection were analyzed and processed by means of wavelet and wavelet packet and then pattern recognition of the parameters' character was done by neural network. By comparing the performances of new algorithm with traditional algorithms, it shows its advantages in many aspects, such as higher diction rate, lower false alarm rate and shorter mean detection time.


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    Titel :

    Traffic Incident Detection Based on Wavelet and Neural Network


    Beteiligte:
    Cheng, Xueqing (Autor:in) / Tang, Ruixue (Autor:in) / Tang, Zhihui (Autor:in) / Zuo, Dajie (Autor:in)

    Kongress:

    Second International Conference on Transportation Engineering ; 2009 ; Southwest Jiaotong University, Chengdu, China



    Erscheinungsdatum :

    29.07.2009




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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