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- W2083121920 abstract "This paper analyze different aspects of factors that affecting passenger volume of Beijing subway, then select fifteen key factors from four aspects: internal structure of the urban rail transit system, urban demographic features, economic development and urban transport structure. Firstly, SPSS software is used to examine the multicollinearity among all the variables and then we remove three factors that are of strong multicollinearity with others. Finally, B-P artificial neural network model is established based on the remainder of factors to predict passenger volume of Beijing subway for the next few years. The results show that the average relative error of the past twenty year is 5.56%." @default.
- W2083121920 created "2016-06-24" @default.
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- W2083121920 date "2014-01-01" @default.
- W2083121920 modified "2023-09-26" @default.
- W2083121920 title "Use of Back Propagation Artificial Neural Network to Predict Passenger Volume of Beijing Subway" @default.
- W2083121920 cites W1562837045 @default.
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- W2083121920 doi "https://doi.org/10.2991/scict-14.2014.10" @default.
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