Metro Passenger Flow Forecast with a Novel Markov-Grey Model

Abstract

Accurate forecasts of passenger flow entering and leaving metro stations is an important work for Metro operation management, such as for the automatic adjustment of train operation diagrams or station passenger crowd regulation planning measures. In this study, Grey theory is introduced to develop a time series GM (1, 1) model for total passenger forecasting. Two modification factors determined by two minimum mean square error principles are proposed to decrease the discreteness of input data and thus improve the forecast accuracy. Moreover, the Markov chain approach is further used to optimize the residual error series. Passenger flow data entering and leaving the Xiaozhai station of Xi'an Metro Line 2 from September 1-30, 2015, were utilized to verify the effectiveness of the proposed method; the forecast results show that this novel Markov-Grey model performs well in terms of forecast accuracy with smaller SMSE and MAPE values. To this effect, the proposed method is especially well-suited to smooth passenger flow forecasting compared to other forecast techniques.