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- W571206657 abstract "Prediction for short-term urban passenger rail flow is essential for effective urban rail transit operation and management. It is important for a forecasting model to capture the periodicity and nonlinearity of short-term passenger flow and to embed these characteristics into the model to enhance forecasting performance. In this research, a support vector machine global online model (SVMGOL) is first proposed by embedding the periodic characteristics via SARIMA model to capture the inherent periodicity of passenger flow. A support vector machine local online model (SVMLOL) is then proposed by embedding the nonlinear characteristics via successive passenger flow value inputs to capture the local nonlinear characteristics of the passenger flow. To take advantage of the two online models, this research then constructs a support vector machine hybrid online model (SVMHOL) based on the idea of data fusion. The model building process and its application in the prediction of short-term passenger flow at Zhujianglu Station of Nanjing Metro is discussed. Testing results show that for the one-step forecasting, the SVMHOL model outperforms the individual SARIMA or SVM model in terms of mean absolute error, mean absolute percent error and root mean square error." @default.
- W571206657 created "2016-06-24" @default.
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- W571206657 date "2013-01-01" @default.
- W571206657 modified "2023-09-24" @default.
- W571206657 title "Forecasting of Short-Term Urban Rail Transit Passenger Flow with Support Vector Machine Hybrid Online Model" @default.
- W571206657 hasPublicationYear "2013" @default.
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