Railway freight demand are predicted and analyzed by using multidimensional association rule based on Apriori algorithm. At the same time, Three correlation models—goods-weight-transport distance, goods-weight-arrival railroad, goods-weight-arrival province are established by using data mining software Clementine to analysis some railway freight invoice of a railway bureau. Finally, some association rules which are helpful for railway freight demand analysis are obtained. It proved that using multidimensional association rules to analyze the railway freight demand is rational and feasible.


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    Title :

    Railway Freight Demand Analysis Based on Multidimensional Association Rules Mining


    Contributors:
    Tang, Yinying (author) / Qin, Yang (author)

    Conference:

    Fifth International Conference on Transportation Engineering ; 2015 ; Dailan, China


    Published in:

    ICTE 2015 ; 2075-2081


    Publication date :

    2015-09-25




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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