Abstract It is well known that traffic incident detection is essential to intelligent transportation system (ITS) and modern traffic management. Compared to traditional models based on traffic theory, some data mining computational algorithms are believed more appropriate and flexibility for automatic incident detection. In this paper, four classification models were introduced and their parameters were selected by tenfold cross-validation. Using an open dataset their predictive performance was compared based on five criteria. The results show that the classification models perform well to detect traffic incidents and no over-fitting problem. What’s more, AdaBoost-Cart and Naïve Bayes models seem to outperform support vector machine and Cart models since they provide superior detection rate. However, they cost long time to train.


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    Titel :

    Real-Time Traffic Incident Detection with Classification Methods


    Beteiligte:
    Li, Linchao (Autor:in) / Zhang, Jian (Autor:in) / Zheng, Yuan (Autor:in) / Ran, Bin (Autor:in)


    Erscheinungsdatum :

    2017-07-12


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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