A new prediction approach for the railway passenger volume is put forward by means of support vector machine optimized by genetic algorithm (GA-SVM). In GA-SVM model, GA is used to determine training parameters of support vector machine. GA has strong global search capability, which can get optimal solution in short time. Railway passenger volume of China from 1985-2002 is used to illustrate the performance of the proposed GA-SVM model. The experimental results indicate that the GA-SVM method can achieve greater forecasting accuracy than artificial neural network in railway passenger volume forecasting.


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    Title :

    Railway Passenger Volume Forecasting Based on Support Vector Machine and Genetic Algorithm


    Contributors:


    Publication date :

    2009-06-01


    Size :

    384141 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English