Short-term Urban Rail Transit Passenger Flow Forecasting Based on Empirical Mode Decomposition and LSTM

DOI

10.2991/masta-19.2019.20How to use a DOI?

Keywords

EMD, LSTM, Urban rail transit, Regression-forecast extension

Abstract

This paper proposed a method to forecast the short-term passenger flow, which is a vital component of urban rail transit system. We used a hybrid EMD-LSTM prediction model which combines empirical mode decomposition (EMD) and long short-term memory (LSTM) to forecast the short-term passenger flow in urban rail transit system. EMD can extract the variation trend of passenger flow, then LSTM can make the prediction to prove the accuracy. The experimental results indicate that the EMD-LSTM model used in this paper has better prediction accuracy than the LSTM model alone. Besides, the amount of data used in this experiment is small, and there is no need to consider additional features except temporal factor. According to what we have learned, this is the first time to combine EMD and LSTM to make short-term prediction in the urban rail transit system.

Copyright

© 2019, 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  - Zi-ji-an Wang
AU  - Chao Chen
AU  - Xiao-le Li
AU  - Jing Li
PY  - 2019/07
DA  - 2019/07
TI  - Short-term Urban Rail Transit Passenger Flow Forecasting Based on Empirical Mode Decomposition and LSTM
BT  - Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)
PB  - Atlantis Press
SP  - 119
EP  - 126
SN  - 1951-6851
UR  - https://doi.org/10.2991/masta-19.2019.20
DO  - 10.2991/masta-19.2019.20
ID  - Wang2019/07
ER  -