Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks
ridership - forecasting, mode - subway/metro, planning - travel demand management
Forecasting, Short-term metro passenger flow, Empirical mode decomposition, Neural networks
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Wei, Y., & Chen, M.C. (2012). Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies, Vol. 21, (1), pp. 148-162.