Traffic safety is considered as one of the important problems in the development of cities. Thus, a reliable estimation of traffic safety is crucial to be proposed. As predicting outcome indicators, two relative indicators, accident rate per 10000 cars and accident rate per 10000 capita, are introduced in this paper. Based on the theory of support vector machine (SVM), the improved SVM models are proposed. To improve the efficiency of learning, the least support vector machine (LSSVM) is introduced; to improve the model's robustness further, the weighted least support vector machine (WLSSVM) is introduced. As an example, using the traffic safety datacollected from 2006 to 2010, the models' effectiveness and feasibility were verified by comparing and analyzing.


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    Titel :

    Road Traffic Safety Prediction Based on Improved SVM


    Beteiligte:
    Yang, Jie (Autor:in) / Zhao, Junbo (Autor:in)

    Kongress:

    Fourth International Conference on Transportation Engineering ; 2013 ; Chengdu, China


    Erschienen in:

    ICTE 2013 ; 107-114


    Erscheinungsdatum :

    09.10.2013




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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