@inproceedings{ef63b6f1ec8946319f3476ff8cfe707e,
title = "Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow",
abstract = "This paper applies both Empirical Mode Decomposition (EmD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more unique pattern than the extracted meaningful EMD components. The patterns of these EEMD components of passenger flow in the metro system are more specific and can be explained more easily for management purposes.",
keywords = "Hilbert-Huang transform, ensemble empirical mode decomposition, metro station, passenger flow, time variant",
author = "Mu-Chen Chen and Chen, {Long Sheng} and Yu Wei",
year = "2017",
month = jun,
day = "5",
doi = "10.1109/IEA.2017.7939214",
language = "English",
series = "2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "239--243",
booktitle = "2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017",
address = "United States",
note = "4th International Conference on Industrial Engineering and Applications, ICIEA 2017 ; Conference date: 21-04-2017 Through 23-04-2017",
}