Short-term Forecasting of High-Speed Rail Passenger Flow

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/).

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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  -