Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow

Mu-Chen Chen, Long Sheng Chen, Yu Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publication2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages239-243
Number of pages5
ISBN (Electronic)9781509067749
DOIs
StatePublished - 5 Jun 2017
Event4th International Conference on Industrial Engineering and Applications, ICIEA 2017 - Nagoya, Japan
Duration: 21 Apr 201723 Apr 2017

Publication series

Name2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017

Conference

Conference4th International Conference on Industrial Engineering and Applications, ICIEA 2017
Country/TerritoryJapan
CityNagoya
Period21/04/1723/04/17

Keywords

  • Hilbert-Huang transform
  • ensemble empirical mode decomposition
  • metro station
  • passenger flow
  • time variant

Fingerprint

Dive into the research topics of 'Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow'. Together they form a unique fingerprint.

Cite this