This paper constructs a passenger traffic volume prediction framework integrating Holt-Winters exponential smoothing model and ARIMA (autoregressive integrated moving average model). Taking the passenger traffic volume historical data of Beijing-Shanghai high-speed railway from July 2011 to June 2017 as an example, we validate the proposed model framework. The results show that the prediction deviation of integrated method is only 0.029, which is lower than that of the two single prediction models. Therefore, the integrated prediction model constructed in this paper is effective.
Prediction of High-Speed Railway Passenger Traffic Volume Based on Integrated Method
Sixth International Conference on Transportation Engineering ; 2019 ; Chengdu, China
ICTE 2019 ; 643-650
2020-01-13
Conference paper
Electronic Resource
English
Schienenverkehr - Prediction of passenger traffic 2020 - high speed railway traffic keeps successful
Online Contents | 2004
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