Sixth International Conference on Transportation Engineering
Prediction of High-Speed Railway Passenger Traffic Volume Based on Integrated Method
Publication: ICTE 2019
ABSTRACT
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.
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ACKNOWLEDGEMENT
This research was supported by the Service Science and Innovation Key Laboratory of Sichuan Province (KL1701) and the Innovation Fund for Doctoral Students of Southwest Jiaotong University (D-CX201829)
REFERENCES
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Information & Authors
Information
Published In
ICTE 2019
Pages: 643 - 650
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Jan 13, 2020
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