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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    Title :

    Road Traffic Safety Prediction Based on Improved SVM


    Contributors:
    Yang, Jie (author) / Zhao, Junbo (author)

    Conference:

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


    Published in:

    ICTE 2013 ; 107-114


    Publication date :

    2013-10-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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