There are many factors contributing to traffic collisions and their severity. Different major factors are likely to contribute to traffic collisions and their severity for different groups of people. Clearly identifying the major contributing factors to traffic collisions and their severity for different groups of people will assist highway safety improvement initiatives by improved facility design and educational program to address the needs due to the changes in demographics. The traffic collision data used in this study has been collected over the last 20 years on the rural highways and urban streets from Saskatchewan, Canada. In order to determine the major factors contributing to traffic collisions and their severity for different groups of people, we present a data mining model using ID3 and C4.5 decision tree algorithms to analyze the traffic collision data in this paper. The experiment results from this study will show that the developed data mining model using decision tree can effectively classify the major contributing factors to traffic collisions and their collision severity for different groups of people with good accuracy. The major contributing factors to traffic collisions and their severity for different groups of people are compared.


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    Titel :

    Using Data Mining Models to Identify Major Factors Contributing to the Severity of Traffic Collisions for Different Groups in Saskatchewan, Canada


    Beteiligte:
    Zhang, Xuefei (Autor:in) / Fan, Lisa (Autor:in) / Liu, Andrew G. X. (Autor:in)

    Kongress:

    Second International Conference on Transportation Information and Safety ; 2013 ; Wuhan, China


    Erschienen in:

    ICTIS 2013 ; 1411-1416


    Erscheinungsdatum :

    2013-06-11




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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