Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Yu Wei, Mu-Chen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

260 Scopus citations


Short-term passenger flow forecasting is a vital component of transportation systems. The forecasting results can be applied to support transportation system management such as operation planning, and station passenger crowd regulation planning. In this paper, a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) is developed to predict the short-term passenger flow in metro systems. There are three stages in the EMD-BPN forecasting approach. The first stage (EMD Stage) decomposes the short-term passenger flow series data into a number of intrinsic mode function (IMF) components. The second stage (Component Identification Stage) identifies the meaningful IMFs as inputs for BPN. The third stage (BPN Stage) applies BPN to perform the passenger flow forecasting. The historical passenger flow data, the extracted EMD components and temporal factors (i.e., the day of the week, the time period of the day, and weekday or weekend) are taken as inputs in the third stage. The experimental results indicate that the proposed hybrid EMD-BPN approach performs well and stably in forecasting the short-term metro passenger flow.

Original languageEnglish
Pages (from-to)148-162
Number of pages15
JournalTransportation Research Part C: Emerging Technologies
Issue number1
StatePublished - 1 Jan 2012


  • Empirical mode decomposition
  • Forecasting
  • Neural networks
  • Short-term metro passenger flow

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