Study on Short-Time Passenger Volume Forecasting of Urban Rail Transit Based on Combined ModelReport as inadecuate




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Abstract: Passenger volume forecasting belongs to the basic research contents of urban rail transit operation management. In order to meet the development needs of rail transit construction, the paper studies on the ARIMA and ANN models for predicting short-time passenger volume which can reflect the real-time characteristics of passenger volume changes. On this basis, the optimal combined model is presented based on ARIMA and ANN models. The application shows that the combined model has higher accuracy compared to single prediction method, and the instance proves the validity and feasible of the model.

KEYWORDS

Keywords: Urban Rail Transit; Passenger Volume Forecasting; ARIMA; ANN; Combined Model

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Author: Yan ZHOU, Lei-Shan ZHOU, Yi-Xiang YUE

Source: http://www.scirp.org/



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