Short-term Forecasting of High-Speed Rail Passenger Flow
Authors
Pei Zhang, Xiao-Long Li, Qin-Zhao Wang
Corresponding Author
Pei Zhang
Available Online February 2018.
- DOI
- 10.2991/ifeesm-17.2018.305How to use a DOI?
- Keywords
- Short-term forecasting; passenger flow; empirical mode decomposition; grey support vector machine
- Abstract
Short-term forecasting of high-speed rail (HSR) passenger flow is the key to high-speed passenger rail planning decision-making. Furthermore, accurate short-term demand estimates are sure to success of rail revenue management. Different station passenger flow is effect each other, however, previous researches are lack of consideration. This paper proposed an approach based on the ensemble empirical mode decomposition and grey support vector machine. Then we use the approach to forecast the passenger flow of multiple stations. Application results indicate that the approach is effective in terms of prediction accuracy.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Pei Zhang AU - Xiao-Long Li AU - Qin-Zhao Wang PY - 2018/02 DA - 2018/02 TI - Short-term Forecasting of High-Speed Rail Passenger Flow BT - Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017) PB - Atlantis Press SP - 1671 EP - 1676 SN - 2352-5401 UR - https://doi.org/10.2991/ifeesm-17.2018.305 DO - 10.2991/ifeesm-17.2018.305 ID - Zhang2018/02 ER -